Authors - Reinardus Dwi Prio Christianto, Nur Wening, Abdul Wahab Samad, Ranthy Pancasasti, Sujoko Abstract - This study explores the role of transitioning to a green economy within the agribusiness sector, encompassing agriculture, fisheries, livestock, forestry, agro-processing industries, and renewable energy. Using advanced theoretical and practical approaches, the primary focus is to investigate policies and innovations that facilitate green economy implementation, while emphasizing behavioral stress management among stakeholders facing structural and technological shifts. Data were collected through surveys assessing eco-friendly policy adoption, technological innovations, and stakeholders' perceptions of psychological stress, adaptation pressures, and behavioral resilience during the transition. Data analysis was conducted using NVIVO 14 through coding techniques to identify key themes, such as resource efficiency, carbon emission reduction, and coping mechanisms for stress. The findings reveal that policies fostering cross-sectoral collaboration and green technology adoption not only enhance agribusiness efficiency and sustainability but also mitigate behavioral stress induced by the demands of change. Visual tools such as word clouds and frequency distribution charts illustrate the levels of policy acceptance and emerging psychosocial adaptation patterns. This study contributes significantly to understanding the intersection of green policy, technological innovation, and stress management strategies in accelerating sustainable transformation within the agribusiness sector
Authors - Reshma C R, ArunKumar B R Abstract - The wireless communication impacts any application in accordance with the security model. This research paper focuses on the transmission technologies to access the unused portion of the spectrum among primary user and secondary user in cognitive radio networks. The users ensure trust models are implemented for the identity of the user, later the verification process are involved to assign the spectrum to the secondary users. The security threats are experiential for both the users i.e. Primary User and Secondary User. The prevention measures need to be adapted to perform the hand-off for spectrum management in CRN. This research work, encompasses the different transmission technologies for spectrum utilization. The security model with zero knowledge proof, blind signature and zero trust security model for identity and access management in cognitive radio network are addressed to manage the spectrum securely. The system performs various stage verification to determine the identity of the user.
Authors - Bhagyashree Thakur, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - The pediatricians and other health workers manage and treat children has been modified through the introduction of software into children's medicine. Clinical decision support systems (CDSS), telemedicine platforms, electronic health records (EHRs), and patient management systems are imperative for optimizing patient outcomes, eliminating administrative overhead, and optimizing the delivery of care. This research examines the advantages and disadvantages of using technology in a pediatric health facility. Among the ways these technologies work are more accurate diagnosis, more efficient chronic disease management, more effective caregivers and providers communication, and monitoring patient progress remotely. For instance, telemedicine has been extremely beneficial in rural and underserved communities But using software in the pediatric clinic does pose several challenges. Software system compatibility, training needs for healthcare personnel, and data privacy concerns especially in maintaining the confidentiality of sensitive information on children are significant challenges. The complexity of pediatric care and its specific requirements, such as tracking children's growth and evaluating development, also make integrating generic healthcare software a challenge., offering specialty care that would not otherwise be available. In spite of these issues, there are still possible advantages to the use of software in pediatric healthcare delivery. As technology continues to evolve, software will have to overcome security, training, and system integration issues in order to be as helpful as possible for enhancing pediatric healthcare outcomes.
Authors - Rafid Al Ahsan, Rifat Ara Rouf, Farzana Sadia, Mahady Hasan, Saadia Binte Alam, M. Rokonuzzaman Abstract - Software developer productivity is a critical factor influencing the efficiency and success of software projects. However, understanding the key factors that impact developer productivity remains a challenge, particularly in region-specific contexts such as Bangladesh. This study aims to identify the primary factors affecting developer productive and analyze how these factors vary across companies and experience levels. To achieve this, a survey was conducted among software developers working in Bangladesh. The survey sent out to 168 software developers working across 38 different software firms. The survey yield 44 responses. Statistical analysis was employed to determine the most influential factors. The findings suggest that autonomy in decision-making, task variety, and using the best practices, software and tool are the strongest factors affecting productivity. While some factors, such as task variety, remain consistent across companies, other, such as remote work policies and communication styles, vary significantly. Additionally, beginner developer prioritize learning, and tool adoption, mid-level developers face challenges with tight deadlines, and senior developer focus more on leadership, collaboration and decision-making autonomy.
Authors - T.A. Alka, M. Suresh Abstract - The study aims to map the current trends in social entrepreneurship education (SEE) research through a bibliometric analysis of 119 papers, followed by the PRISMA protocol analysed through the Biblioshiny package and the VOSViewer to investigate the themes and the collaboration patterns of countries, and to propose the research implications. This research contributes to the current knowledge through a systematic analysis of the existing SEE research guide, through the research questions, and a comprehensive analysis. Themes identified in this research are: the foundation of pedagogy for sustainable and value-based education, ecosystem support for the SEE, student-oriented perspective, and the social capital in SEE, etc. From the cluster analysis, the most emerging topics are: the role of innovation and design thinking for sustainability, entrepreneurship pedagogy development for inclusive social entrepreneurship knowledge management, experiential and service learning in the SEE, and the social innovation in the SEE for system-level metamorphosis to create responsible entrepreneurs. The collaboration dynamics reveal that the five clusters are in the southern and northern countries in the practice and context-based research in the SEEs, East Asia, and the central Europe or eastern Europe countries on the innovation, policy, and Anglo-Asia-Pacific collaboration in the scholarship of the policy in SEEs. The major limitation of the study is purely based on secondary data and lacks empirical understanding. The study offers further research scope on combining data-bases, and the mixed method research by integrating qualitative insights into more quantitative analysis through methods like BERTopic modelling, etc, integrated with hypothesis testing.
Authors - Azran Ahmad, Naziffa Raha Md. Nasir, Azlan Yusof, Aslina Mat Asli, Fazlina Mohd Ali, Surya Sumarni Hussein Abstract - As programming education evolves, enhancing conceptual under-standing among novice learners remains a critical challenge. This literature review synthesizes key pedagogical innovations from 2015 to 2025 that inform the development of a validated assessment instrument for programming conceptual understanding. Five major approaches are explored: conceptual metaphors, robotics and interactive tools, prototype theory, physical programming tools, and ontology-based frameworks. These strategies collectively aim to scaffold abstract reasoning, reduce cognitive load, and foster deeper learner engagement. Conceptual metaphors provide intuitive cognitive bridges between everyday experiences and abstract programming constructs. Robotics and physical tools offer tangible, multimodal learning environments that enhance motivation and comprehension. Prototype theory emphasizes relatable exemplary to support categorization and knowledge transfer, while ontology-based learning structures promote metacognitive reflection and conceptual integration. The review high-lights a growing consensus that multimodal, metaphorical, and structured pedagogies significantly improve computational thinking and problem-solving skills. This synthesis supports the design of the Programming Conceptual Understanding Test (PCUT), a novel assessment tool grounded in cognitive science and validated through mixed-methods research. The review concludes by identifying future research directions, including cultural adaptability, scalability, and integration with adaptive technologies to personalize learning. These insights offer a foundation for a more inclusive, effective, and engaging programming education
Authors - Ch V S Satyamurty, Ch Srikarthik Abstract - Plant diseases are of numerous kinds. Recognizing each one of them would be a tedious task for a human being, but a machine can solve this issue. Farmers used to take a small cutting of an infected plant to a local garden centre, which includes plant pathologists who can often help to identify the disease manually. Later Machine learning algorithms came into existence and its usage gave the accurate results. Machine Learning deals with classification and used to classify plants based on health like unhealthy and healthy plants. "Classification" is the key concept used here. Classification of the plant leaves done centred around morphological features. Classification can be done by using decision tree, k- means clustering, neural network etc. We are proposing a web application which helps in identifying the disease type for the given input from a trained data set using "Convolutional Neural Network", a deep learning algorithm.
Authors - Herry Rojivadiya, Priyanka Patel Abstract - In today’s digital-driven lifestyle, individuals across various sectors—including students, office employees, and remote workers—spend extended hours seated in front of screens. Prolonged sedentary behavior often results in poor postural habits, leading to musculoskeletal discomfort and decreased productivity. This research presents a real-time posture monitoring system utilizing MediaPipe BlazePose for keypoint detection. By analyzing the spatial relationships between body landmarks, the system identifies postures such as slouching, leaning, and improper arm or leg positioning. Immediate feedback is provided to encourage healthier sitting habits across diverse environments, from classrooms to offices and home workstations.
Authors - Truong Bui Quang, Huu Dang Quoc, Van Nguyen Thi Cam, Anh Nguyen Duc Abstract - This paper examines the interaction between Robotic Process Automation (RPA), Business Process Management (BPM), and Digital Transformation (DT) - three critical components in improving operational efficiency and driving business modernization. RPA automates repetitive tasks, reduces errors, accelerates processing, and optimizes resource use. When combined with Artificial Intelligence (AI) and Machine Learning (ML), it further enhances data analysis and decision-making. BPM focuses on analyzing, designing, and optimizing business processes to ensure organizational agility. DT provides a technological foundation for broader innovation in processes and structures. The paper contributes a comprehensive and updated perspective on how RPA, BPM, and DT interrelate—not only functioning independently but also reinforcing one another to create greater business value. It emphasizes that their integration is a strategic approach to improving performance, responsiveness, and continuous innovation. Importantly, the research is relevant to both Industry 4.0 and Industry 5.0. While Industry 4.0 prioritizes automation and data-driven systems, Industry 5.0 highlights human–technology collaboration for more adaptive and human-centric organizations. This study enriches theoretical insights and offers practical guidance for building effective and sustainable digital transformation strategies.
Authors - Anupama Ugemuge, Shubham Kishor Kadam, Utkarsha Wanjari Abstract - This paper highlights the transformative potential of wearables and the importance of ongoing innovation and research in this dynamic field. Self-monitoring devices and wearable sensors are changing the way people can maintain their health independently by enabling the instant tracking of some important bodily parameters. Such technologies enable continual monitoring that facilitates early identification of health problems and fosters proactive healthcare involvement. From fitness trackers to glucose monitors, these wearables are primarily used to help patients manage chronic conditions, achieve fitness goals, and maintain general well-being. They provide important advantages such as lowering costs, personalized health information, and compatibility with other mobile applications. Nevertheless, issues such as data accuracy, privacy, and user acceptance are still good fields for further research and development. With the rapid progress in artificial intelligence and sensor technology, we stand on the brink of a generation of wearable health devices that will reshape the landscape of healthcare, offering predictive health analytics and widening access to healthcare around the globe.
Authors - Nguyen Xuan Ha Giang, Lam Thanh-Toan, Nguyen Thai-Nghe Abstract - This study introduces a novel dual-branch Deep Matrix Factorization (DeepMF) framework enhanced by NLP techniques for predicting student performance in the Intelligent Tutoring Systems. Building on previous research, the proposed approach adopts a fundamentally different modeling strategy by transforming discrete educational features-such as learner ID, exercise, question, skill group, session start time, and repeated attempts-into structured sentences that capture both temporal and sequential information. These inputs are processed through two complementary branches. The first branch employs pre-trained GloVe embeddings, followed by a self-attention layer that captures intra-sequence dependencies before passing the representations into a DeepMF module. The second branch leverages a BERT-based model to extract contextualized language features. To address the issue of class imbalance, Focal Loss is applied during training on both the KDDCup 2010 and Assistment 2017 datasets. Experimental results demonstrate substantial improvements in prediction accuracy: RMSE is reduced from 0.418 to 0.167 (a 60.1% reduction) on KDDCup 2010, and from 0.472 to 0.186 on Assistment 2017 (representing a 60.6% relative improvement). These findings confirm the effectiveness of integrating contextual, sequential, and temporal modeling with DeepMF in educational data mining.
Authors - Hariprasad Rai M, Shankar Lingam. M Abstract - The COVID-19 pandemic has had profound and multifaceted impacts on global economies, with gender disparities becoming increasingly apparent. This paper explores the gendered effects of India's policy measures in response to the pandemic, focusing on the socio-economic challenges faced by women. It assesses the policy interventions implemented by the Indian government to mitigate these impacts, such as cash transfers, healthcare support, and social security measures. Drawing from data collected through the COVID-19 Global Gender Response Tracker, the study highlights the gendered dimensions of these policies and their effectiveness in addressing the unique needs of women during the crisis. The analysis underscores the need for gender-sensitive economic and social policies to ensure equitable recovery and resilience for women in future crises. By critically examining the intersection of gender, policy, and pandemic response, this paper contributes to a deeper understanding of the importance of gender-inclusive strategies in man-aging global health emergencies.
Authors - Md Mahbub Alam, Sabrina Sultana Prithul, Md. Sajjad Hossain Abstract - Understanding and predicting customer behavior is essential for sustaining growth and profitability in the software market. This study proposes an AI-driven framework for analyzing customer adoption patterns and churn risk through the integration of clustering and classification techniques. Using real-world behavioral data, the research first applies K-Means clustering to segment users into distinct behavioral groups. Four meaningful segments were identified, each characterized by varying engagement levels, contract types, and churn tendencies. Subsequently, supervised machine learning models—Logistic Regression, Random Forest, and XGBoost—were employed to predict churn. Among these, XGBoost achieved the highest performance, with an accuracy of 87.1% and an ROC-AUC score of 0.91. Feature importance analysis highlighted tenure, contract type, and monthly charges as critical churn predictors. The findings offer practical insights into personalized retention strategies and pricing interventions, emphasizing the utility of AI in customer analytics. This work contributes to the field by bridging behavioral segmentation with predictive modeling, providing a scalable and interpretable approach to managing customer lifecycle challenges in software-based services.
Authors - S. Rajaprakash, G. Sujatha, S. Kavitha, Vivek Darsi, Mohith Sai Kurakalva, Srikar Reddy Palapati Abstract - The growth of mobile applications has reached extraordinary levels because they provide users better features and convenience for daily needs. Massive mobile application expansion resulted in fresh security issues creating an in-creased number of cyber threats throughout the market. The number of cyber risks increases in Germany since malicious applications both harm device integrity and illegally obtain user information to threaten individual privacy together with organizational security measures. The development of strong auto- mated security systems with locating and removal capabilities of threats remains an urgent matter. Cybersecurity systems at present base their protection on fixed rule systems alongside traditional machine learning approaches. The implemented security methods deliver protection however they demonstrate reduced performance during changes in cyber threat patterns. The system exposes sever-al entry points which hackers can leverage for attack purposes. An innovative cyber- security framework has been developed which incorporates the RF algorithm with PCA through this project to address existing limitations. The system uses mobile application metadata preprocessing to accomplish three tasks through PCA: elimination of unneeded data and retention of important features along with feature selection. This procedure simultaneously maintains data authenticity as well as enhances processing speed. Random Forest becomes operational to classify mobile applications between malicious and safe categories within the framework. RF algorithm and PCA form a flexible and scalable system which enables handling extensive datasets along with security adaptation towards the latest threats. The system has a user-friendly interface which enables users to enter mobile app metadata and get prompt predictions along with performance scores. Mobile app users benefit from predictions which help them decide about the safety of apps through confidence scoring. The system maintains strict vali- dation procedures across unidentified datasets which confirm its dependable and practical functionality during actual use. Evaluation of the model effectiveness depends on performance metrics that include accuracy together with precision, recall and F1-score. The system delivers complete performance information which allows end-users to receive practical insights and enable greater transparency.
Authors - Gaurav Kamble, Chetan Parlikar Abstract - This study explores the impact of the quality of healthcare services, their accessibility, and affordability on patient satisfaction. With patient satisfaction becoming a crucial determinant of healthcare quality, it is important to establish drivers of patient satisfaction in order to empower healthcare providers to improve service delivery. Specifically, the study seeks to establish the level to which healthcare service quality, accessibility of healthcare services, and their affordability independently influence patient satisfaction. A survey of 206 respondents was conducted, and data on respondents' perceptions and experiences of healthcare services were collected. The study analyses the interaction among the three drivers and patient satisfaction with a view to providing actionable in-sights into the improvement of healthcare outcomes. With the onset of greater emphasis on patient-centred care, this study provides timely and relevant evidence capable of guiding policy and strategy towards enhancing the quality, accessibility, and affordability of healthcare services.
Authors - Jay Prakash Thakur, Akshata Kishore Moharir Abstract - Multi Agent Systems (MAS) face a critical challenge to maintain trustworthiness alongside operational efficiency as they move from theoretical development to practical implementation in essential domains. Human-in-the-loop approaches create operational bottlenecks that limit scalability, but fully autonomous systems face the risk of catastrophic failures. The proposed framework in the paper introduces a new approach to minimal human oversight through strategic intervention points that use intelligent triggers that assess risk and quantify uncertainty, detect novelty, and analyze consensus. The framework achieves scalable oversight of complex MAS through optimized information flow and reduced cognitive load that maintains critical safety guarantees.This position paper conducts a theoretical analysis of the impact of system performance while presenting applications for autonomous transportation, critical infrastructure management, and financial systems.
Authors - Sasipong Kijsason, Sa-Aat Niwitpong, Suparat Niwitpong Abstract - Parameter variance is used to measure the dispersion of data or the deviation of individual data points from the mean. A high variance indicates that the data are widely spread around the mean, reflecting greater variability within the dataset. This study introduces four novel methods for constructing confidence intervals for the variance of the Zero-Inflated two-Parameter Rayleigh distribution. These include the percentile bootstrap, the bootstrap method with standard error, the standard method based on a large sample, and generalized confidence interval approaches. A simulation-based comparison was conducted using coverage probability and expected length as performance criteria. The findings indicate that the generalized confidence interval and standard method achieved coverage probabilities closest to the nominal confidence level. Among these methods, the generalized confidence interval demonstrated the highest efficiency. Additionally, the proposed methods were applied to real-world data on COVID-19 mortality rates in Malaysia during September 2021.
Authors - Eshwari V. Kadu, Sudhir Agarmore Abstract - An IEEE family of standards called Time-Sensitive Networking (TSN) builds upon Ethernet to facilitate determinism over communications with guaranteed low latency, low jitter, and high dependability. Providing synchronized, deterministic network operation on prevalent Ethernet equipment, TSN finds itself at the center of essential enabler support for emerging applications such as industrial automation, self-driving vehicles, and smart grid, all which necessitate deterministic, real-time sharing of information. Time-sensitive scheduling, traffic priority, redundancy, and perfect time synchronization are only a few of the most important aspects of TSN discussed in depth in this paper, which also follows the development of the technology from ordinary Ethernet and identifies the necessity for time-sensitive communication. Besides, we examine the challenges of deploying TSN, summarize real-world applications across various industries, and discuss emerging trends such as integrating TSN with wireless networks and edge computing. As part of ensuring TSN has the ability to meet the demands of future-time-critical systems, we identified open research avenues and directions as part of our research.
Authors - Thanaporn Phattanaviroj, Massoud Moslehpour, Princy Pappachan, Mosiur Rahaman, Jirapong Pomnoi, Rinruedee Pattaradej Abstract - The tourism industry has undergone significant transformations following the COVID-19 pandemic, particularly through the adoption of digital technologies such as virtual tours. This study investigates the motivational factors influencing individuals' intention to adopt virtual tours, using Protection Motivation Theory (PMT) as the theoretical framework. It identifies two key components: threat appraisal (perceived travel and health risks) and coping appraisal (enjoyment, perceived cost-effectiveness, and sustainability motivation). Data was collected through a designed questionnaire and analyzed by applying Partial Least Squares Structural Equation Modeling, with 122 respondents. The findings indicate that threat and coping appraisals have a significant influence on the intention to adopt virtual tours. The research provides insight into the evolving behavior of tourism consumers in a post-pandemic context. It highlights the potential of virtual tourism as a sustainable and attractive alternative to physical travel.
Authors - Anju Kamal, Rajiv Prasad Abstract - The emergence of technology driven gig economy and digital labor platforms has transformed the methods of sourcing, assessing, and remunerating work. This study examines how traditional credentials like educational degrees and platform-specific ratings like Top Rated and Top-Rated Plus affect freelancer earnings in digital labor marketplaces. We use regression analysis to compare the explanatory power of ratings given by the platform and educational levels to determine whether platform ratings are more strongly associated with hourly earnings than formal education credentials for 146 freelancers on a prominent digital labor platform. Based on signaling theory, we found that platform-assigned ratings that acts as signals predict hourly earnings better than formal education. The findings indicate that digital labor marketplaces value platform signals like innovative reputation systems. The results show the association between traditional educational credentials that serve as labor market indicators and platform-specific signals in evaluating value in digital marketplaces. The study demonstrate labor signaling is changing in the era of digital work with implications for freelancers, platform developers and human resource management practices. It further challenges the traditional human capital development and raises questions about the role of traditional educational credentials in the future of digitally mediated work.
Authors - Twinkle K. Patel, Ankit Bhavsar, Arpit Jain Abstract - Automatic Speech Recognition (ASR) systems have become essential tools for the communication in natural human-computer communication across various languages. The growing need for effective communication in public and private sector has increased the need of Natural Language Processing. Gujarat is one of the prominent state in India where most of the official communication takes place in Gujarati language. ASR for Gujarati language has its own importance due to the high usage of Gujarati language. This paper, highlights the emergence of ASR for Gujarati language from the early days of creating ASR commercially to more modern advancement approaches such as deep learning, end-to-end (E2E), and various other methods. Traditional ASR systems utilized statistical methods, most commonly Hidden Markov Models (HMM) and Gaussian Mixture Model (GMM) which often reported low accuracy. With the emergence of deep learning models, including CNN, BiLSTM, and transformer-based models such as Wav2Vec 2.0 and XLSR-53, the recognition accuracy has significantly increased, especially in noisy and spontaneous speech contexts. The paper also provide the comparative study of the work done by various researchers in the field of Gujarati ASR, highlighting that the use of spell correctors as well as hybrid feature extraction methods have reduced phonetic ambiguities and diacritic errors in Gujarati language. The study strongly emphasizes the need for morphological analysis and contextual modeling to adequately address the complexity in the homophones and diacritics of Gujarati language. The paper also reviews the various methods applied to Gujarati ASR and their reported Word Error Rates.
Authors - Prathilothamai M, Nitin Ravi, Rishi Pradeepkumar, Sajith Rajan P, Anurag Nagilla Abstract - Code analysis ensures software quality, readability, and maintenance. Traditional methods such as Static Analysis Tools (SAT) and SZZ algorithms recognize errors and analyze contributions based on predefined rules and historical trends. However, complex code semantics are difficult to understand and often lead to overlooked issues and limited feedback. Despite its benefits, LLM is not practical in some business environments, facing challenges such as high computing costs, slower processing, and data protection concerns. Hybrid models may provide scalable and inexpensive solutions for large enterprise projects. This study contrasts SATs and LLMs, focusing on capabilities to detect bugs and maintain code quality, by investigating hybrid models that fuse the contextual strength of LLMs with the efficiency of SATs. In evaluations with 30 participants, the hybrid model consistently received the highest scores across accuracy, clarity, relevance, and usability (average ratings >8.5/10), outperforming both SATs and AI-alone approaches. Hybrid models may thus provide a scalable and cost-effective solution for problems arising in large-scale corporate enterprise projects. This study supports the use of case studies and benchmarks to assess the effectiveness, cost, and scalability of these various approaches. The findings aim to give practical suggestions for improving traditional code review processes, helping balance rule-based tools with AI-powered insights to enhance software development workflows.
Authors - T. A. Alka, M. Suresh Abstract - The study aims to examine the link between social innovation (SI) and sustainable entrepreneurship (SE). The major focus is to explore how SI is promoted by the SE and what the Sustainable Development Goals (SDGs) are covered by the sustainable entrepreneurship through promoting social innovation identified through a research gap by adopting the TCCM framework. The study is a systematic literature review on the publications in the Scopus database identified through keywords relevant to the theme by adopting the SPAR-4-SLR protocol. The result indicates that the sustainable entrepreneurs promoting SI innovation through innovative business models ensure social well-being. The SI is not only the technological but also non-technological aspects, which are responsible and impactful, having innovative approaches, social practices, and processes. The SEs are developing business models that are socially and sustainability purpose-driven, offering financial advantage and ensuring the social cohesion and the empowerment of the community through their participatory approaches and ensuring the local level stakeholders’ engagement. SE develops partnerships and collaboration among the different actors, which will create knowledge sharing and collaborative innovation. While mapping of SDGs to SI by SEs, the study identifies that the major SDGs covered are: SDG 1, SDG 4, SDG 5, SDG 8, SDG 9, SDG 10, etc. The study offers implications to practitioners, policymakers for actionable strategies to enhance the SI-driven SE ecosystem. This systematic review lacks empirical findings and relies purely on the published works. The limitations will open future research scope avenues by covering more databases, keywords, etc.
Authors - Shaveta Thakral, JyotiVerma, Pratima Manhas, Shankar Amalraj, Neha Shahare, Monali Tingane, Aparna Mahesh Torade Abstract - Urbanization and increasing housing density have necessitated innovative solutions to address both space constraints and accessibility for individuals with limited mobility. This paper presents a novel concept of an integrated lift-toilet system, designed specifically for elderly and disabled individuals living in multi-story buildings. By merging the functionalities of vertical transportation and sanitary facilities, the proposed system aims to provide dignity, convenience, and emergency usability, particularly in high-rise residences and healthcare environments. The research encompasses a multidisciplinary approach, including mechanical design, sanitary engineering, user-centric ergo-nomics, and real-world application assessments.
Authors - Jyotika R. Yadav, Arpit A. Jain, Ankit Bhavsar Abstract - Efficient traffic management is a major focus in smart city projects. Intelligent Transportation Systems (ITS) are revolutionize the way transportation networks are managed, to improve safety, efficiency, and sustainability. Machine learning (ML) has been developed as a powerful means for advancing ITS by enabling data-driven decision-making, real-time traffic management, predictive analytics, and automation. This paper offers a thorough summary of the current trends and advancements in the utilization of ML techniques in ITS. It explores key areas such as traffic flow prediction, autonomous vehicles, congestion management, smart mobility solutions, and road anomaly detection.
Authors - Saranya P., Rejikumar G. Abstract - This study employs a systematic literature review (SLR) to investigate the changing landscape of smart labelling in the food industry. As Customer demand for transparency, traceability, and ethical consumption increases, conventional food labels are evolving into interactive communication instruments. Smart labels facilitated by technologies such as QR codes, RFID, and intelligent packaging are now integral to real time customer interaction and business decision making. Despite increasing scholarly interest, a thorough delineation of research trends, deficiencies, and prospective directions has been absent. To tackle this, adoption of SPAR-4-SLR protocol was done to examine 43 peer reviewed papers (2015-2025) from Scopus utilizing Biblioshiny and Vosviewer. The investigation identified few predominant topics like food safety, customer behaviors, and technological integration while also highlighting under discovered topics like RFID and packaging designs. Result indicated a shift from a compliance orientated labeling to an interactive digital approach that foster consumer trust. This review provides essential in-sights for scholars, legislators, and industry executives seeking to create data-driven, customer-focused labelling systems in the digital era.
Authors - Abhishek Passwan, Srushti Karki, B M Bandgar Abstract - Accurately predicting stock prices remains a challenging task, primarily due to the volatile, dynamic, and non-linear nature of financial markets. This study introduces a robust deep learning-based framework designed to forecast the closing prices of companies listed on the Nifty 50 index. We explore and compare the predictive performance of several state-of-the-art neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), alongside two novel hybrid models: StockAugNet-f and StockAugNet-c. Historical stock data were retrieved from Yahoo Finance and pre-processed using Min-Max normalization. To effectively model temporal dependencies, we applied a sliding window technique for sequence construction during training. Model performance was assessed using key evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To enhance usability and support real-time forecasting, we developed an interactive Streamlit-based application, allowing users to dynamically visualize model outputs. Experimental findings indicate that the GRU and RNN model give better accuracy over the hybrid models accuracy and resilience for the NIFTY 50 index stocks.
Authors - Kumar Gaurav, Sudhir Agarmore, Akshay Kumar, Akshat Jha, Edidiong Akpabio, Chandan Kumar Abstract - Network architecture is examined in-depth in terms of the structural design or the underlying protocols that help in the facilitation of data transmission between networks. The paper discusses the classic models of network architecture, which include P2P, client-server, and hybrid models, and outlines the latest advancements in cloud computing, edge computing, and IoT. The paper further discusses the different types of network topologies and other devices, such as the OSI and TCP/IP models, in the shaping of data communication. This also encompasses new emerging trends, such as 5G, IoT, quantum networking, and zero-trust security, among many others. These will detail discussions of the concepts involved as well as their impacts on network protocols and architecture. As demand for faster, more secure, and scalable networks grows, the integration of these modern technologies opens the way to new opportunities and challenges of future network infrastructure.
Authors - Janani Vijayan, Bala Subramanian R Abstract - This study explores the growing body of research on leader’s gratitude, emphasizing its significance in organizational settings. Gratitude, a complex and multifaceted positive emotion, plays a crucial role in enhancing interpersonal relationships, promoting well-being, and fostering prosocial behavior. The review categorizes gratitude into trait, state, and collective forms, examining their unique characteristics and implications. It highlights key theoretical frameworks such as the Find-Remind-and-Bind Theory, Broaden-and-Build Theory, and Cognitive Emotion Theory to explain how gratitude functions within leadership dynamics. The review further delves into the antecedents of leader’s gratitude, including team error management, feedback, and appreciation programs and its consequences, such as enhanced social intelligence, team innovation, improved leader-member relationships, and increased organizational commitment. Moreover, the paper discusses the broader social impacts of leader gratitude, showing how it fosters a positive work culture. Identified limitations include the scarcity of causal studies and underrepresentation of cultural factors. Future research directions are proposed, such as exploring gender differences in gratitude expression, cultural variations, and the effectiveness of different gratitude expressions. Overall, the review underscores that leader’s gratitude is a powerful yet underutilized tool in cultivating effective leadership and healthy organizational environments.
Authors - S. Kirthica, K. Subha, S. Arul Murugan, V. Deepak Kumar, M. Dhivagar, S. Kanishka Abstract - In contemporary society, effective grievance redressal is vital for transparency, accountability, and public trust. Traditional systems often face issues like manual processing, delays, and poor user engagement. To overcome these challenges, we propose VOICE, an AI-driven grievance management platform that automates the complaint lifecycle—from classification and similarity detection to assignment and tracking. Leveraging natural language processing (NLP), speech-to-text, and automated prioritization, VOICE reduces human intervention and improves response times. It also incorporates community-driven upvoting, real-time notifications, and performance monitoring to enhance user engagement and system transparency. Designed for scalability and ease of use, VOICE can be deployed across institutions, communities, and national systems. By combining automation, intelligence, and user-centric features, VOICE represents a significant advancement in modern grievance redressal.
Authors - Siqabukile Ndlovu, Ernest Mkandla Abstract - This paper presents a novel approach to improve test case prioritisation in continuous integration environments by integrating semantic features derived from test case descriptions. The approach uses a Convolutional Neural Network (CNN) model that considers both structured and semantic features. The target variable for prioritisation is defined using a threshold on calculated priority values, helping to identify the most critical test cases. We further engineer interaction and polynomial terms with the semantic feature to capture complex relationships. These polynomial terms allows the model to capture non-linear relationships. For example, a small change in the semantic feature might not matter much in the middle range, but a large implicit_prob (e.g., above 0.9) might have a disproportionately strong effect on the priority. Through a 5-fold crossvalidation, our results demonstrate that while the semantic features do not statistically significantly improve overall F1-score, they yield a statistically significant enhancement in Average Percentage of Fault Detection (APFD) (pvalue = 0.0055). This indicates that incorporating semantic understanding enables the model to effectively reorder test cases, leading to earlier detection of critical faults and increased efficiency in testing efforts within live CI/CD environments.
Authors - Juhi Patel, Tejaskumar Bhatt Abstract - Growing realization of the need for sustainable agriculture has seen Artificial Intelligence (AI) applications implemented in precision agriculture, advocating for sustainable use of resources and enhanced yields in crops. The authors present an AI-driven model that employs multi-modal data of soil attributes, weather, and vegetation indices to predict crop yield. It develops a model with satisfactory test prediction accuracy based on starting from a Linear Regression model (MSE of 0.0835 and R² score of 0.9714). This shows that the model can potentially capture linear trends for big meteorological change influence on crop yields. Feature correlation analysis determines NDVI and soil moisture as the most significant predictors, with the important roles of monitoring vegetation health and effective water management yielding better agricultural results. Although Linear Regression is a robust starting point, this research also paves the way for integrating sophisticated AI techniques like Neural Networks and Ensemble Learning models to tackle non-linear relationships and interactions. In comparative analysis, we have confirmed the utility of interpretable models for scalability, reliability and applicability in smallholder farming. Implications: The result fills the void between the abstract AI structures and the usable application, providing a widely replicable artifact to improve decision-making in precision agriculture. In addition to prediction accuracy, the study also emphasizes on scalability and ethical consideration, the latter coupled with the area of application of IOT based AI empowered systems and data privacy. Further studies will seek to evaluate the model in different agricultural regions, explore new machine learning methods, and implement real time decision support systems. This study contributes to global efforts towards achieving sustainable agriculture, ensuring food security, and limiting the environmental impact of agriculture by providing knowledge that can be used directly.
Authors - Bhumi Patel, Mann Patel, Aum Mehta, Nishat Shaikh, Priteshkumar Prajapati Abstract - Deepfake generation techniques have advanced rapidly in recent years, posing significant risks for misinformation and privacy. In this paper, we propose an ensemble-based deepfake detection framework that leverages EfficientNet-B4 as the backbone architecture for both image and video analysis. By incorporating attention mechanisms and siamese training strategies, our system enhances feature discrimination and improves robustness against subtle manipulation artifacts. The framework is trained and evaluated on two widely adopted benchmarks—the Deep- Fake Detection Challenge (DFDC) and FaceForensics++ datasets. Experimental results demonstrate that the ensemble approach outperforms individual models, achieving higher accuracy and improved log-loss metrics, while also providing interpretability via attention maps. We further discuss the integration of temporal consistency analysis to better handle video data, and outline future directions for real-time deepfake forensic systems.
Authors - Putri Haryani, Sinatria Arka Daniswara, Randany Zevanya Sihombing, Gavikal Hanif Pasopati, Ridho Rambu Bassae, Andhika Hendra Adi Wibisono Abstract - This study investigates the implementation of the Institutional Financial Application System (SAKTI) in digitalizing public sector accounting within Indonesia’s National Research and Innovation Agency (BRIN). Recently, BRIN is a newly formed government institution resulting from the merger of several institutions. It manages budgets from various sources and operates within a complex organizational structure. BRIN faces significant challenges in achieving accurate and timely financial reporting. Using a qualitative case study method, data were collected through semi-structured interviews and observations at BRIN’s Bureau of Planning and Finance. The findings reveal that SAKTI, alongside the MONSAKTI monitoring system, plays a pivotal role in supporting accrual-based financial reporting in compliance with Government Regulation No. 71/2010. Despite progress in automation and integration, several challenges persist, including limited system performance, partial manual processing, and constrained operational budgets. The study highlights the strategic importance of aligning digital infrastructure, internal controls, and human resource capacity to sustain accountability and reporting quality. These insights are expected to inform similar public institutions undergoing digital financial transformation amid structural complexity.
Authors - Dina Sekar Vusparatih, Handy Martinus, Eshaby Mustafa, Ahmad Hidayat Ahmad Ridzuan Abstract - Malaysia has become the primary destination for Indonesian patients over the past two decades. However, international medical tourism carries significant risks due to limited information, restricted communication access caused by distance and language barriers, while the industry heavily relies on trust for critical health-related decision-making. This study focuses on how Island Hospital implements technology strategies to reduce uncertainty in making informed decisions. A qualitative approach using a case study method was employed to gather the necessary data. Interviews were conducted with Indonesian patients and their family members at the hospital, as well as with Indonesian government representatives in Penang, Malaysia. To enrich the findings, participant and documentation observations were also applied. The results indicate that the hospital integrates Human-Computer Interaction (HCI) using four approaches in designing its web-site/portal and mobile application. This enables patients to communicate with healthcare providers despite existing barriers, and supports them in planning both pre- and post-treatment procedures.
Authors - Gweneaella Lyrika R. Aguinaldo, Nellisa F. Cortez, Jocelle Marie S. Dador, Kathleen Kaye L. Getonzo, Loran Ann G. Gonzaga, Angela B. Navarro, Remelyn J. Vicente, Manuel J. Logatoc, John Kenneth M. Arcayos Abstract - This study examines how key store attributes influence shoppers’ purchase intentions and, ultimately, customer satisfaction in five selected supermarkets in Cavite, Philippines. Store attributes—defined as accessibility and cleanliness, product assortment, promotion, price, and customer relations—are hypothesized to drive the likelihood that a customer will buy (purchase intention) and feel satisfied. Employing a quantitative correlational design, the researchers ad-ministered an adaptive, 45-item Likert-scale questionnaire (4-point) both face-to-face and online to 380 supermarket patrons. Instrument reliability was con-firmed via Cronbach’s alpha coefficients between 0.8740 and 0.9379 (good–excellent). Respondent demographics indicated a predominantly young (18–28 years, 75.5 %) and female (62.4 %) sample, with over half (51.6 %) reporting a monthly income below ₱10,000. Descriptive analysis (frequency, percentage, weighted mean, and standard deviation) revealed that accessibility and cleanliness received the highest effectiveness rating (mean = 3.44, SD = 0.43), while customer relations scored lowest. Inferential analysis using Spearman’s rank-order correlation showed a moderate positive relationship between purchase intention and customer satisfaction (ρ = 0.549, p < 0.001). Among individual attributes, promotion correlated most strongly with purchase intention (ρ = 0.507), and price exhibited the highest link to customer satisfaction (ρ = 0.567). These findings suggest that supermarkets seeking to boost sales, and loyalty should prioritize promotional strategies to drive purchase intent and calibrate pricing structures to enhance satisfaction. Optimizing accessibility, cleanliness, and other in-store factors can further improve customer experience, yielding competitive advantage in the retail sector.
Authors - Pradnya H Desai, Parikshit Mahalle, Pankaj Chandre Abstract - The exponential growth of the Internet of Everything (IoE) demands secure, intelligent access control mechanisms to manage data flow and device interaction efficiently. This paper proposes a novel architecture that integrates post-quantum cryptography, federated learning, and explainable AI to ensure privacy- preserving, real-time decision-making for resource-constrained IoE environments. The edge nodes, equipped with lightweight cryptographic engines and context-aware training modules, perform preliminary data processing and secure communication via GG-ULL and PQCrypto protocols. The cloud leverages federated learning to train distributed models and uses a multi-agent policy engine for dynamic access decisions. Blockchain-backed audit trails ensure accountability, while the explainable AI module enhances transparency in access control. The system supports mission-critical services like SCADA and health data lakes, guaranteeing secure and interpretable decision flows. This architecture paves the way for resilient, scalable, and intelligent access control frameworks suitable for next-generation IoE ecosystems.
Authors - Deepika K M, Rohith H P, Srinivas D B, Lakshmi H Abstract - The need for real-time object recognition is growing in a few applications, including robotics, surveillance, and autonomous vehicles. Modern object recognition technique YOLOv5 achieves high accuracy while maintaining real-time performance. This paper proposes a real-time, highly accurate object recognition method using YOLOv5. The system, which was created using PyTorch and Python, is trained and evaluated using the COCO dataset. The proposed system enables fast object detection and achieves outstanding precision and recall rates using single-shot detector architecture. Additionally, the detection accuracy is greatly improving with the introduction of YOLO and its architectural descendants. YOLOs are frequently employed in a variety of contexts, mostly because of their speedy conclusion rather than due to the accuracy of their detection. The YOLO detection accuracy, for instance, ranges between 63.4 and 70. The suggested system is ideal for real-time object detection applications since experimental findings demonstrate that it performs better than current object detection systems in terms of accuracy and speed.
Authors - Asra Fatema Zakir Baig, Amol Mashankar Abstract - Hospital leadership styles are crucial determinants of employee performance and patient satisfaction. In this research, secondary research findings are integrated to explore the influence of transformational, transactional, and laissez-faire leadership styles on healthcare delivery. Transformational leadership is always linked with greater staff engagement, job satisfaction, and better patient care outcomes. Transactional leadership is less consistent in its findings, typically enhancing short-term efficiency but with little developmental impact over the long term. Laissez-faire leadership has typically been associated with negative organizational performance because of the lack of direction and responsibility. The literature highlights the requirement for healthcare managers to embrace transformational styles as a means to create a safe work environment as well as an improved patient experience. Emotional intelligence, communication skills, and encouraging teamwork should feature prominently in any future leadership programs aimed at enabling sustainable healthcare greatness. (Alilyyani, B., Wong, C. A., & Cummings, G. G., 2018), Effective hospital leadership has a profound impact on the performance of staff as well as patient satisfaction. The current paper integrates evidence from research studies to explore how the transformational, transactional, and laissez-faire styles of leadership influence healthcare. The review emphasizes the significance of leadership in creating an optimistic workplace culture, improving staff participation, and finally enhancing patient care.(Wong, C. A., & Cummings, G. G. (2013))
Authors - Najah Najmia Halim, Okta Bayu Prihatma Putra Abstract - This paper uncovers the system-level challenges confronting freelancers in emerging markets, framing freelancing as a form of digital entrepreneurship with significant social implications. Through a systematic literature review of 23 peer‑reviewed studies (2020–2024) using the PRISMA protocol, we identified five critical challenge domains: opaque algorithmic management that limits freelancer autonomy, financial instability due to irregular income and weak social protection; regulatory ambiguity that erodes trust in digital platforms, technical barriers including limited infrastructure and digital literacy, and career development gaps marked by burnout and isolation. These challenges disproportionately affect freelancers in developing regions, highlighting structural inequalities within the global digital labor market. By synthesizing literature across geography and sector, this study positions freelancing not just as a labor trend, but as a pressing issue of digital inclusion and social sustainability. The findings inform policymakers, platform designers, and support institutions about intervention points to foster equitable, resilient freelance ecosystems. Future research must explore how technology, policy, and cross-sector partnerships can create inclusive innovation frameworks that sustain independent digital work.
Authors - Ankush Soni, Sanjay K. Sahay Abstract - The integration of unmanned aerial vehicles (UAVs) into remote and beyond-visual-line-of-sight missions has increased the demand for secure, efficient, and low-latency communication systems, particularly in scenarios where satellite connectivity is the only viable option. However, high-latency satellite links and constrained onboard resources pose significant challenges to traditional cryptographic protocols. In this paper, we present a lightweight, symmetric key-based authentication and secure communication protocol designed specifically for satellite-driven UAV applications. Our protocol replaces resource-intensive public key operations with a pre-shared key approach and incorporates AESHA3, a variant of AES that leverages SHA-3 for key scheduling, along with SHA2-512 for hashing. We implement this design within the TLS and QUIC frameworks, adapting their internal packet structures to accommodate constrained environments. Experimental evaluations on Raspberry Pi testbeds reveal that our proposed QUIC implementation significantly outperforms TLS in encryption speed, handshake latency, and overall protocol execution time. The results demonstrate the suitability of the proposed system for real-time mission-critical UAV operations over satellite links, highlighting the benefits of integrating optimized symmetric cryptography into modern transport protocols.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - Marketing automation with the use of data-driven insights will help in anticipating customer-behavior. It will enable personalized campaigns, improve the process of decision-making, and ensure market-accountability by measuring effectiveness of the system and help in optimizing strategies This will ultimately drive customer engagement, loyalty, and higher-returns on investment in the competitive business environments. The present invention relates to a method and a system for customer predictive analysis for marketing automation and market accountability. The present invention attempts to develop a layout for the job of marketing-computerization (MA) in estimating the return on displaying exercises and the difficulties related with arriving at responsibility in marketing. To explore the goal of the assessment, the creators took on a subjective methodology, leading an exploratory review among ten key witnesses. Based on the aftereffects of the subjective investigation, an applied system was proposed, which incorporates both key and functional level elements fully intent on making a worth based plan. In this plan, leaders, for example the Chief Marketing Officer. arise as worth makers, encouraging business versatility, what further contentions can be given to legitimize spending plan portion to MA exercises. Through cautious examination of the components that describe the peculiarity under study, the present invention eventually adds to a superior comprehension of MA and responsibility inside the current business worldview. Focusing on the marketing setting, an organized conversation of how AI can recognize the objective clients exactly in spite of their various practices was introduced in this contemporary invention. The uses of AI in client focusing and the extended viability all through the unique periods of client lifecycle were similarly inspected.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - The systems and regulations that control human behaviour are the main emphasis of HRM. Finding the greatest talent from around the world, training them, evaluating their performance, rewarding them, and creating a positive work atmosphere are all part of the HRM field's magnificent duty. Developing a framework that accurately forecasts the need for talent and workforce skills becomes crucial because every organization's strategy depends, in part or in full, on its skill sets. When the human resource data is accessed for study of the higher cognitive processes involved, a variety of techniques may be used to extract the most useful information from the dataset. A common strategy for using data to inform decisions is data processing. On the other side, "state-of-the-art accuracy" in decisions is what predictive analytics is known for. The goal of this study is to provide a strategic decision-making model for "human resource management (HRM)." The study develops a decision-making framework based on data processing and predictive analytics for decisions pertaining to human resources. Given that HRM has a significant impact on an organization's longevity and efficacy, the model was created for global corporations. The suggested approach will be useful in enhancing HR systems' effectiveness, which could have a favourable impact on business results.
Authors - Bryna Meivitawanli, Liu Fen Phaw, Daniella Maria Natalia Abstract - The increasing influence of social media and user generated con-tent has significantly transformed consumer behavior, with negative publicity emerging as a critical factor affecting brand perception and purchase intention. This study aims to investigate the impact of negative publicity on the purchase intentions of Generation Z consumers in Indonesia, focusing on the mediating roles of brand advocacy and brand betrayal. A quantitative research approach was employed, utilizing a structured questionnaire distributed to 158 respond-ents. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test four hypotheses regarding the relationships be-tween negative publicity, brand advocacy, brand betrayal, and purchase intention. The findings indicate that negative publicity significantly reduces purchase intention, with brand advocacy mitigating its adverse effects, while brand betrayal amplifies the negative impact. These results underscore the importance of managing brand reputation in the digital age, highlighting the need for brands to adopt ethical marketing strategies and proactive reputation management to counter the detrimental effects of negative publicity. This research contributes to the understanding of how digital media influences consumer behavior, offering valuable insights for marketers seeking to enhance brand loyalty and mitigate the risks associated with negative publicity. The findings are particularly relevant for businesses targeting Generation Z in emerging markets like Indonesia, where social media plays a pivotal role in shaping consumer decisions.
Authors - Sateesh Kumar TK, Vishnu Achutha Menon, Juby Thomas, Lijo P Thomas Abstract - This study examines emotion classification in news headlines related to Operation Sindoor, a precision military response initiated by India in May 2025 following the Pahalgam terrorist attack. The operation’s symbolic naming, reflecting cultural mourning and national resolve, resulted in emotionally charged media coverage. To analyze the emotional framing within these head-lines, a dataset from multiple news outlets was compiled and annotated with six emotion categories: anger, fear, joy, sadness, surprise, and neutral. Preprocessing involved TF‑IDF vectorization with unigram and bigram features, followed by classification using Logistic Regression and Support Vector Machines (SVM). Model performance was evaluated using accuracy, precision, recall, and F1-score, with macro-averaging to address class imbalance. Both models achieved an overall accuracy of 54%, with SVM yielding a higher macro‑F1 score (0.40) compared to Logistic Regression (0.22). The results indicate that SVM performed better in identifying dominant emotions such as fear and sur-prise, while both models struggled with underrepresented categories like joy and disgust. The findings suggest the need for advanced approaches such as deep learning architectures or data augmentation methods to improve classification of minority emotions in crisis reporting contexts.
Authors - Ananth Chiravuri Abstract - Prior studies have indicated a positive effect of a student’s digital competence on their academic performance in higher education institutes. However, the relationship between variables such as digital competence and academic performance may not be direct. There are other variables such as student engagement and self-efficacy that could mediate the role between digital competence and academic performance, warranting a further investigation Specifically, this research investigates whether students' digital skills/literacy influence their academic success through the mediating factors of self-efficacy and engagement. Findings from the study aim to emphasize the importance of empowering students with digital literacy skills to enhance their learning experiences and achievements.
Authors - Cu Quoc Le, Huy Khanh Hua, Nguyen Viet Ngo, Tien Trung Bach, Hoang Ngoc Tran Abstract - This paper presents an improved multi-camera people tracking pipeline with a focus on enhancing object detection to improve overall tracking performance. The system integrates object detection, singlecamera tracking, re-identification, and multi-camera matching. We evaluate five versions of the YOLO model, from YOLOv8 to YOLOv12 in the same tracking pipeline to ensure a fair comparison. Part of Track 1 data from AIC2024 dataset is used for both training and inference purposes. For each frame, detected objects are tracked using a Kalman filter and matched via the Hungarian algorithm based on both spatial and appearance features. Inter-camera identity association is achieved through cluster-based matching, followed by refinement steps to improve consistency. Tracking performance is assessed using the MOTA metric. Experimental results demonstrate that stronger detection models significantly improve tracking accuracy, with YOLOv12 achieving the highest MOTA score.
Authors - T. A. Akshaya, M. Suresh Abstract - This study is a journey to identify the opportunities, challenges, and future research scope in the metaverse entrepreneurship based on a systematic literature review. The Scopus database is employed for the selection of documents. For review, 25 documents are selected through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The study identified the opportunities in metaverse entrepreneurship connected with the technological enablers of entrepreneurship, innovative business and market opportunities, transformation of education and entrepreneurial learning, economic growth and social inclusion, and further development in metaverse entrepreneurship. The major challenges of the metaverse in entrepreneurship encompass economic, technological, regulatory, security, and skill-based aspects. Metaverse entrepreneurship is a galaxy waiting to be explored. This study represents the first attempt to synthesize the existing research through a systematic literature review methodology. To policymakers, this study guides to creation regulatory framework in social, economic, and legal dimensions. To entrepreneurs, this study helps to identify opportunities in metaverse entrepreneurship not just as a marketplace but as an ocean for innovation and ideation. To educators, this study is a light into the rethinking in redesigning of the curriculum and the pedagogical approach to integrating metaverse tools and entrepreneurship education to create a generation with immense knowledge in digital entrepreneurship. The limitation of this in-depth study is, it only included the documents from the Scopus database till August 2025. The novelty of the study is to the theory by informing future research possibilities in this evolving domain by highlighting current research trends.
Authors - Chirani Perera, Uvini Ranaweera, Indra Mahakalanda Abstract - This study investigates tourist perceptions of seven southern beaches in Sri Lanka using Google Reviews. With the increasing influence of online plat-forms in travel decision-making, analyzing review content provides valuable in-sights into tourist experiences and preferences. The research employs trans-former-based models from Hugging Face for sentiment analysis and topic modeling, offering a modern, data-driven approach to textual review interpretation. Word clouds and bigram visualizations are used to highlight common positive and negative expressions associated with each beach. The findings reveal themes such as cleanliness, natural beauty, surfing opportunities, crowd, and local ser-vice quality as key themes associated with the southern coastline of Sri Lanka. Sentiment patterns vary across beaches, with some consistently rated positively while others receive mixed feedback. This analysis offers practical insights for stakeholders in the field of tourism to improve destination management and marketing strategies. The study demonstrates the effectiveness of modern-day NLP techniques in understanding tourist experiences and provides a scalable frame-work for future such analysis that centres around the user responses.
Authors - Mayanka Gupta, Ayman Amjad, Arjun Prabhakaran, Bhanoday Kurma, Bhanu Prakash M, Kiran Agarwal Gupta, Chaitra Ravi, Sindhoor N Abstract - Despite significant progress in autonomous driving, detecting and segmenting obstacles under poor conditions remains a major challenge. This paper reviews deep learning models that tackle real-world difficulties like occlusion, fog, motion blur, and uneven road surfaces such as potholes and broken speed bumps—factors that heavily impact safety and detection accuracy. Over ten recent models are analyzed, including prompt-based approaches like Semantic-SAM and EPCFormer, memory-augmented ones like OOSIS and XMem, and task-specific detectors such as DR-YOLO, D-YOLO, and motion-aware YOLO variants. Each model type comes with trade-offs: prompt-based systems are flexible but depend on large vision-language datasets, while memory-based methods offer temporal consistency at the cost of increased computation. A key focus is on handling unstructured and uncertain road conditions, especially common in countries like India, where irregular infrastructure and unpredictable traffic are everyday challenges. Models trained solely on structured data often fail in these environments. To address this, the survey includes detailed comparisons and benchmark tests under difficult traffic and weather conditions. These insights inform the design of XenSense-V.1, a real-time deep learning framework using optical f low, temporal reasoning, and efficient segmentation to handle occlusion, weather issues, and complex road scenarios—particularly suited to Indian driving conditions.
Authors - Sivakami B U, M. Suresh Abstract - In a world racing toward net-zero emissions, how can MSMEs strategically align their business operations to adopt green finance? Despite MSMEs’ growing recognition of sustainable development, they continue to face persistent challenges in aligning their strategies with green finance adoption due to financial, regulatory, and knowledge-related barriers. The extant literature provides limited insights into the strategic alignment for adopting green finance. To ad-dress this gap, the study develops a Fuzzy Logic-based assessment framework to evaluate alignment levels and identify weaker attributes. The framework specifies five criteria and nineteen attributes. Findings reveal a state of “moderately aligned” with four among nineteen attributes requiring immediate attention. The framework offers MSMEs a structured pathway to assess their alignment and strategically position themselves for green finance adoption and sustainable growth.
Authors - Premchand, Jayant Kalra, Ajoy Kumar, Sahil Sankhyan, Varun Dutt Abstract - Cardiovascular diseases (CVDs) continue to be the globe's leading cause of death, and therefore, there is a need for low-cost, long-term cardiac monitoring technologies especially in low-resource environments. Current wearable ECG technologies are limited by cost, proprietary design, and single functionality. We introduce ECG Go Device, a low-cost, portable, multifunctional, wearable device with real-time ECG monitoring, heart rate variability (HRV) analysis, fall detection, and step counting in one platform. The system utilizes a high-fidelity analog front-end and inertial sensing to provide mobility aware cardiac monitoring. A 44-subject validation trial compared ECG Go Device with a clinical-grade Shimmer3 ECG system and the Fitbit Sense 2 on 12 standard time-domain HRV measures and 10-day step count. The ECG Go Device demonstrated good agreement (all p > .05; r > .93) with Shimmer3, and high correlation in step count measure (r = .94). With clinically robust signal quality, real-time wireless transmission, and safety alert capability, ECG Go Device provides a scalable solution for longitudinal cardiac and mobility monitoring especially for eldercare and underserved communities.
Authors - Sarath M, M Suresh Abstract - Enterprise Resource Planning systems and Supply Chain Management systems shape how organizations plan, coordinate, and deliver goods and services. When they function together, they improve efficiency, visibility, and decision making. Research on this integration, however, is scattered across industries and viewpoints, making it hard to see the full picture. This study clarifies that picture through a Bibliometric Systematic Literature Review (B-SLR). A PRISMA-based review identified and screened relevant studies with clear criteria to ensure transparency. Ninety-nine publications from the Scopus database, covering 2020 to 2025, were retrieved. From these, twenty studies were selected for qualitative synthesis, while the full set underwent bibliometric analysis to map the field through performance metrics, collaboration networks, keyword co-occurrence, and thematic clusters. The results show a rising focus on ERP and SCM integration, driven by digital transformation and the demand for resilient supply chains. The analysis high-lights five recurring dimensions: scope, enabling technologies, outcomes, success factors, and barriers. These insights form a Five-Dimensional ERP and SCM Integration Framework that links theory with practice and provides a foundation for future research and implementation.
Authors - Kunihiko Takamatsu, Sayaka Matsumoto, Katsuhiko Murakami, Hidehiko Kamei, Kazuya Tsuruta, Naoya Honda, Yumi Ishige, Shimpei Matsumoto, Hiroya Kawasaki, Ikuhiro Noda, Kenya Bannaka, Tetsuhiro Gozu, Tomoyuki Sakai, Ryosuke Kozaki, Aoi Kishida, Hibiki Ito, Koichi Akashi, Sayaka Hama, Gerald Prescott, Masahiro Uchida, Akira Nakamura, Yasuhiro Kozaki, Tamotsu Mori, Shintaro Tajiri, Naruhiko Shiratori, Shotaro Imai, Kenichiro Mitsunari, Yasuo Nakata, Sachio Hirokawa,Masao Mori Abstract - This paper extends the Lifelong Sustainable Inquiry-based Community Learning (LSiCL) framework, based on Edu in formatics, to address challenges and opportunities presented by the rise of Large Language Models (LLMs) in education. LLMs like ChatGPT are fundamentally transforming education, with human-LLM and inter-LLM learning relationships opening new frontiers. This research explores whether the traditional human-to-human LSiCL can be meaningfully extended to encompass human-LLM and LLM-LLM interactions while retaining its core principle of collaborative inquiry. Our analysis demonstrates that LSiCL principles are applicable across all these configurations, paving the way for sustainable and scalable Human-LLM Learning ecosystems. This frame-work is crucial for improving resource efficiency, promoting educational equity, and enhancing global educational sustainability.
Authors - Nakhoon Baek Abstract - Since the computers are introduced, there have been many meaningful attempts to use computers as education tools. Additionally, the wide use of web servers on the internet makes video-on-demand services available especially in the entertainment area. They also developed digital video lectures, which record video lectures as digital contents, and provide them also as parts of video-on-demand services. During the COVID-19 pandemic in the year of 2020, almost all schools around the world provided digital video lectures. Since the digital video lectures can provide high-quality lectures to lots of people at relatively-low costs, it is now expected to be much actively used in near future. In this paper, we will present more details of what preparations and choices are possible, what to do, and what to avoid, in the process of providing digital video lectures, especially in the field of computer science. At this time, these kinds of literature surveys are seriously lacking, and thus, our case studies will be used to further improve the overall quality of digital video lectures and expand the e-learning markets. This paper will be a good starting point for providing check points and reference items to lecturers who prepare and provide digital video lectures.
Authors - Ikram Ahamed Mohamed, Hafiz Abdulla, Mohaideen Mohamed Mohabilasha, Fiyaz Ahmed, Pankaj Chandre, Parikshit Mahalle Abstract - With the surge in digital wallet usage across fintech platforms, secure and privacy preserving Know Your Customer (KYC) mechanisms have become increasingly critical. This survey reviews the current state of digital identity verification frameworks, emphasizing the challenges posed by centralized KYC models, including data breaches, lack of user consent, and regulatory fragmentation. We systematically analyze more than 50 recent studies and industry practices addressing privacy-preserving identity protocols, decentralized identity (DID) systems, cryptographic techniques (such as zero-knowledge proofs), and access control architectures. The paper proposes a reference architecture reKYC based on common patterns found in the literature, integrating layered components for identity verification, access governance, trust evaluation, and regulatory compliance. We classify existing approaches by their trust model, cryptographic basis, and consent mechanisms. Key gaps identified include limited regulatory interoperability, weak user-centric control models, and lack of scalable trust frameworks. This survey concludes with open research directions and a future outlook for secure, privacy-aligned digital wallet verification.
Authors - Ujwala Kshirsagar, Asmita Dani Abstract - The National Education Policy 2020 (NEP 2020) encourages the inclusion of an Indian Knowledge System (IKS) in the Higher education curriculum. In traditional Indian philosophy, the search for knowledge wisdom and truth was always seen as the highest goal of human life. The scholars made significant contributions in many fields likes mathematics, medicine, astronomy, engineering, surgery, architecture, navigation, shipbuilding, arts, yoga, and even chess. These valuable traditions and contributions should be preserved, studied, developed, and used in new ways through our education system to benefit future generations This paper focused on strategies for implementing IKS Indian Knowledge System into curriculum of higher education institutes, to roadmap efficient execution of IKS and impact of addition of IKS on modern syllabus.
Authors - Vikas Shah, Aarav Shah, Aarush Shah Abstract - The crowdfunding landscape, positioned at the intersection of entrepreneurship and technological innovation, compels organizations to address rising competitiveness while embedding corporate social responsibility (CSR) and advancing employee empowerment. Traditional crowdfunding approaches often struggle to meet the primary objectives due to inconsistent mechanisms to ac-count for regulatory changes, cultural diversity, and the demand for measurable social impact. A persistent limitation of existing practices is the absence of standardized frameworks and measurable indicators capable of capturing the organizational and societal value of crowdfunding. The paper introduces a Scenario-based Crowdfunding Framework (SCRF) that categorizes scenarios and defines actionable objectives to enhance alignment between CSR and employee empowerment. SCRF is a lightweight and adaptable methodology incorporating elementary mechanisms to evaluate, refine, and strengthen the social and organizational impact of crowdfunding. An empirical case study of crowdfunding for Artificial Intelligence (AI) initiatives and programs is performed, providing evidence of how scenario-based initiatives can serve as a strategic lever for CSR and employee empowerment in the context of Society 5.0. The research identifies key determinants influencing crowdfunding effectiveness and examines their implications for CSR integration and organizational engagement with scenario-driven analysis, governance cycle, and impact evaluations. The study ex-amines and contributes to accelerating CSR initiatives, positioning employees as empowered change agents, and advancing the broader societal vision of So-ciety 5.0.
Authors - Nurianna Thoha, Stephen Wijaya Abstract - The purpose of this research is to explore the motivational factors of Generation Y (Gen Y) to work in Chinese-owned garment companies and to analyze how these factors are linked to Herzberg’s theory of motivation and the Chinese business culture since based on the previous research many companies do not properly motivate Gen Y employees. A qualitative method by utilizing exploratory study was applied for this research. In-depth semi-structured inter-views with 25 Gen Y employees were conducted in two Chinese-owned garment companies in the Greater Jakarta area of Indonesia, to collect the primary data. The interviews were recorded, and the data was used to develop the transcripts, which were then analyzed using thematic data analysis. The research results showed that Herzberg’s theory of motivation and Guanxi are not always aligned with the motivations of Gen Y employees to work in Chinese-owned garment companies. For example, quality of relationship and status are aligned with Guanxi but not aligned with Herzberg’s theory of motivation. However, several Guanxi factors are aligned with Herzberg’s theory of motivation, such as trust and responsibility, and recognition. In conclusion, there is a connection between motivations, Chinese business culture, and generation cohort which is GenY employees who are currently working in both garment companies in the Greater Jakarta area of Indonesia.
Authors - Akash Patel, Hardik kumar Jayswal, Rishi Patel, Prem Trivedi Abstract - This paper provides an in-depth analysis of various methods and concluding things for plant disease detection and classification, utilizing combinations of machine learning and deep learning approaches. The literature review highlights three primary methods: computer vision-based ML models, DL architectures, and spectroscopy-based techniques. While computer vision methods, including segmentation and manual feature extraction, have been extensively used, they are time-consuming and less effective for datasets with minimal visible symptoms. Deep learning, with its capability for automatic feature extraction, demonstrates significant potential, although challenges such as high computational costs and overfitting persist. Spectroscopy methods present alternative strategies, yet they are not universally applicable across all types of plant diseases. Our analysis suggests that optimized methodologies, capable of handling diverse datasets and overcoming the limitations of visual-based techniques, are essential for the timely and accurate classification of plant diseases. Future research must ad-dress these challenges to develop more robust, scalable, and efficient disease detection systems.
Authors - G Baby Lakshmi Prasanna, R. Pradeep Kumar Reddy Abstract - In a time of increasing dependence on digital identity, facial biometric systems have become important for access control and authentication. However, their vulnerability to advanced presentation attacks is especially 3D masks and media -based (photos, videos and replay’s like gif) reflects serious security threats. Due to liveness detection will show some impact on the major threats which are unseen attacks. This literature survey systematically examines the development of real -time detection techniques developed to combat these challenges. We analyze deep learning methods and hybrid models such as classic texture-based methods, 3D depth and thermal sensing, CNN and VITs, customized distribution on edge units. The paper data set discusses availability, real -time performance, generality of the domain and hardware efficiency. Research intervals have been highlighted in adapting cross -film, unfavorable strength and distribution of low resources. This review serves as a basis for researchers and doctors, who aim to develop flexible, real-time anti-spoofing systems for safe biometric authentication. Apart from this some of review highlights the importance of standardized evaluation protocols, privacy-preserving deployment and fairness across demographics groups.
Authors - Ch Sahyaja, D. Harsha, G. Sruthi, Ch Shankar Abstract - The digital marketplace presents significant challenges for firms, making customer loyalty a crucial strategic goal for sustained success. This study develops and tests a mediated-moderated structural model to uncover the mechanisms behind customer loyalty formation. Drawing on Relationship Marketing Theory and Customer Engagement Theory, the model examines Customer Engagement (CE) as a mediator between Customer Satisfaction (CS), Trust (TR), and Brand Image (BI), with Perceived Value (PV) moderating the CE–Customer Loyalty (CL) relationship. Data from 406 digital consumers were analyzed using Structural Equation Modeling (SEM) and moderation analysis. Results reveal that CE significantly mediates the effects of satisfaction, trust, and brand image on loyalty, accounting for up to 47% of the relationship. Additionally, PV moderates the engagement–loyalty link, enhancing the impact of engagement on loyalty in online contexts. The constructions demonstrated strong reliability and validity. These findings highlight the central role of engagement in converting relational drivers into loyalty and emphasize the importance of perceived value in strengthening this effect, offering valuable insights for firms seeking to build enduring digital customer relationships.
Authors - Sreesankar R S, Durgalashmi C V Abstract - This study examines the year-on-year percentage change in Goods and Services Tax (GST) collections across five South Indian states—Karnataka, Kerala, Tamil Nadu, Telangana, and Andhra Pradesh—from 2017–2018 to 2023–2024. Employing a comparative and trend analysis framework, the research traces the initial post-implementation surge, the pandemic-induced contraction, and the subsequent recovery and deceleration phases. While all states experienced robust growth immediately after the introduction of GST, the subsequent trends reveal significant divergence. Karnataka consistently outperformed its peers, benefiting from digital readiness and administrative efficiency, whereas Andhra Pradesh, despite an early lead, now lags. The findings highlight the influence of state-specific economic structures, governance models, and digital infrastructure on tax performance.
Authors - D.K. Chaturvedi, Sachin Diwaker Abstract - Footwear is critical in maintaining foot health, comfort, and overall well-being. Leather and non-leather materials are widely used in footwear manufacturing, each with distinct properties affecting breathability, durability, and foot support. This study examines the impact of leather and non-leather footwear on foot health and comfort by analyzing existing research, material properties, and biomechanical factors. Findings indicate that leather footwear generally offers superior breathability and ergonomic support, whereas non-leather alternatives provide cost-effective and ethical advantages but may lead to foot discomfort over prolonged use. Future research should focus on developing sustainable yet comfortable alternatives.
Authors - Oleksii Shapoval Abstract - Stackable micro-credentials have a significant potential for making the study process more flexible and personalized. The paper introduces an approach for managing educational processes related to the acquisition of stackable micro-credentials with the use of blockchain technology. The approach allows learners to build verifiable personalized skill trees with cryptographic provenance of their progression. The paper first outlines related works in the fields of micro-credentials, stackable credentials, and the application of blockchain technology in education and knowledge certification. Then, the approach is described, starting with a high-level overview, elaboration on incorporated technologies, and a description of the main processes. The contextualization of the work is then provided, elaborating on implications and practical advantages of the presented approach. The research contributes to the field of knowledge management by providing a practical blueprint for how blockchain technology can be applied for personalization of learning process. This approach can be applied for a number of use-cases, from small-scale workshops to online courses and education institution environments.
Authors - Sandali Tharumini, Indra Mahakalanda, Prasanga Jayathunga Abstract - Advertising is a major element in the promotional mix that plays a crucial role in both brand development and success. Advertising messages reach end customers through multiple mediums like television, radio, digital media, outdoor display advertising (OOH), and print publications. The selection of an optimal media mix serves as one of the critical aspects. This research addresses the issue of the absence of a structured methodology for media mix selection in the advertising industry, which generally leads to inefficient media planning. Applying the Multi-Criteria Decision Analysis (MCDA) technique, Fuzzy Analytic Hierarchy Process, the study evaluates five qualitative criteria to determine the optimal media mix. By using a real-world relaunch campaign, "My Zesta Story" from the Sri Lankan tea brand "Zesta", the study provides a campaign-specific framework for media mix optimization. The analysis reveals that learning effectiveness is the most influencing criterion in media selection, and overall, in terms of performance, digital media and TV are the best performers. Digital is slightly leading due to its affordability, learning effectiveness, and campaign fit. TV outperforms on communication fit and competitiveness
Authors - Takumi Kato, Ryosuke Ikeda, Emiko Yamada, Kenta Kasahara Abstract - In the current big data era, excessively rich data may cause information overload in employees, resulting in reduced productivity. Therefore, this study seeks to address the question: "Is it more effective to generate ideas by showing factors at the factor level, as with structural equation modeling (SEM), rather than showing the effects of a large number of individual variables, as with multiple regression analysis (MRA)?" We randomly assigned 131 students to use the brand image factors of 10 sports in Japan as reference data for ideas. We provided the control group with the MRA analysis results and the treatment group with the SEM analysis results, and asked them to plan a new gym concept. As a result of a randomized controlled trial conducted on 2,521 people in their 20s to 60s, 29.1% (control group: MRA analysis) and 36.2% (treatment group: SEM analysis) found the concept attractive (quality of the concept); a significant difference was detected. However, the amount of concepts was fewer in the treatment group. Presumably, the motivation to generate more ideas decreases once a good idea is generated through SEM. Therefore, SEM should be actively adopted for data analysis within a company to reveal employees’ idea-creating abilities. Additionally, the motivation to produce more ideas decreases once a good idea is produced; hence, managers should not easily demand large amounts of output from their subordinates. This study is novel since it demonstrates the impact of the comprehensive analytical results of aggregated information from SEM on human creativity.
Authors - Patel Nirmal Rajnikant, Ritu Khanna Abstract - The relevance of conventional Economic Order Quantity (EOQ) models to today's manufacturing systems with varying and unpredictable parts demand is small, as they often use closed-form analytical approaches with constant demand. In this paper, we propose a numerical scheme that integrates Newton-Raphson iterative techniques with finite difference methods to address complex EOQ models with variable demand. To achieve this, the EOQ problem is reformulated into a nonlinear minimization problem of a total cost model where a non-linear first-order condition needs to be solved to find the order quantity. Due to the variability of demand, it is often impossible to analytically derive the cost function’s gradient, thus the finite difference method is employed within the Newton-Raphson iteration process to approximate the gradient.To assess the method's effectiveness, several manufacturing case scenarios with different demand profiles are used. The results show that the proposed hybrid procedure finds accurate solutions even for non-linear and non-stationary demand. The paper fills the gap between the theory of optimization and actual inventory management in the dynamic production environment by providing a flexible and widely applicable solution procedure for EOQ models with advanced elements.
Authors - Patel Nirmal Rajnikant, Ritu Khanna Abstract - We provide a rationale on how businesses can adapt inventory control techniques and simultaneously integrate sustainability strategies to reach economic goals. An up-to-date version of the Economic Order Quantity (EOQ) model is developed with sustainability prerequisites around the greenhouse gas emissions limit and the ratio of renewable resources used during procurement, aiming to lower total inventory cost while observing the legal green compliance requisites. These prerequisites focus on the scope of emissions generated and emissions reduction efforts undertaken by the business. Subordinate to emissions caps, we set forth a decision variable proportional to renewably sourced material per order. Valuable analytical responses to questions related to the impact of cap levels and renewably sourcing on order size, frequency and cost are presented. Numerical example based on business of a medium-sized manufacturing company is implemented. Outcomes reveal that investments for strengthening renewably sourced materials to drive green purchasing significantly improve environmental footprint measurements at reduced cost. Such awareness significantly boosts operation planning cost sustainability objectives.
Authors - Adwaitha K Raj, Pranesh P, Durga Lashmi C.V, Taramol K.G Abstract - The sale of insurance products through the bancassurance channel has rapidly gained popularity in India. Consumers who make use of joint services provided by banks and insurance companies weigh different considerations when making purchases and evaluate different standards to gauge their degree of satisfaction. This study examines the factors that influence customer purchasing behaviour and satisfaction with bancassurance, using a sample of 150 respondents from three districts in Kerala. The findings suggest that after purchasing insurance through agents, banks are emerging as the next best option, due to their commitment, cost savings and return on investment. Majority clients prefer to buy future insurance policies through bancassurance, citing advantages like current policy information, fast premium payments, and efficient claims processing. Therefore, the future of bancassurance in India looks promising if partnering companies effectively channel their efforts to meet the customers' needs.
Authors - Md. Mizanur Rahman, Ankan Roy, Shovan Kumar Paul, Anupam Singha Abstract - Nowadays, energy or power dissipation has become a major concern in digital integrated circuit design. This high-density design of the chip increases power dissipation. One of the primary causes of energy dissipation is irreversible computation, necessitating improved power optimisation techniques. Consequently, the reversible logic methodology provides an efficient means to minimise energy dissipation in the circuit. Addition is a fundamental arithmetic operation that underpins other regularly utilised operations, including multiplication, division, and subtraction. In computing systems, adders, which are digital circuits that add numbers, are a fundamental component. With the continuous development of technology, the need for efficient and high-performance processing units has become inevitable, and these must be made from reversible logic elements. The Carry Skip Adder(CSA) is among the most efficient adders utilised in numerous data processing units to do swift arithmetic operations. This study introduces a design that is effective of a CSA utilising reversible logic gates and evaluates its performance. The proposed design achieves notable improvements over existing works by reducing garbage outputs, optimizing constant inputs, minimizing delay, enhancing quantum cost efficiency, and decreasing the overall gate count. Additionally, the performance of the suggested adder surpasses that of others in terms of transistor count and power dissipation.
Authors - Badrun Nahar Luna, Nazifa Jerin Reshni, Shefat-E-Ara Khan, Md. Munna Khan, Md. Rakibul Hasan Antor, Sadah Anjum Shanto Abstract - This autonomous healthcare monitoring system enhances patient care by tracking vital signs and automating essential needs via IoT-Edge computing. It uses MAX30100 and DS18B20 sensors to measure heart rate, oxygen levels, and body temperature, with real-time data displayed on an LCD screen and transmitted to the Blynk app for remote monitoring. The system automates food and medicine dispensing using a servo motor, ensuring timely intake through Blynk notifications for caregivers. To maintain uninterrupted operation, a hybrid power system integrates solar panels, battery storage, and an SMPS, with a relay switch enabling automatic transitions based on power availability. The Blynk app also monitors the voltage and efficiency, comparing the solar and SMPS performance. Paired NodeMCU microcontrollers power the system, making it extensible and sustainable. Anticipated for hospitals, clinics, and home healthcare, this smart monitoring system leverages IoT technology to strengthen patient safety, accommodation, and authenticity while reducing manual interference.
Authors - Suruchi Pandey, Hemlata Gaikwad, Vatsala Saxena, Sweta Rani, Sonal Kumari Abstract - The fast pace of development in blockchain technology is transforming corporate Learning and Development (L&D) by addressing challenges related to credential verification, transparency in training, compliance tracking, and work-force mobility. Current training management systems are typically plagued by inefficiencies in the form of manual verification, data security breaches, and lack of interoperability with Learning Management Systems (LMS). This research explores how blockchain can enhance training management through decentralized, tamper-evident learning records, providing trust, security, and efficiency for corporate training programs. The study utilises a secondary data research design, scrutinizing industry reports, peer-reviewed scholarly articles, firm case studies, and government policy documents. Results indicate that blockchain enables secure credentialing, time-real skill verification, automated compliances verification, and easy transfer of certification from one company to another. The study employs the ADDIE Model to assess the use of blockchain in L&D for effective training program analysis, design, development, implementation, and evaluation. This study concludes that blockchain will play an important role in the future of business education but requires standardized models, policy backing, and scalable integration models to achieve large-scale adoption. Companies must leverage blockchain-based AI learning systems to enhance workforce training, compliance automation, and global credential recognition in the digital economy.
Authors - Anika Yadav, Ananya V Holla, Animesh Giri Abstract - With the growing use of connected devices, geographic barriers diminish, enabling communication of critical data in near real-time. As the frequency of natural disasters increases, timely communication forms the backbone of efficient disaster management. With the number of recipients varying based on the population density, any disaster notification framework must be highly scalable. Big Data technologies like Apache Kafka, Apache Flink, RabbitMQ, Apache Spark, and Apache Hadoop can be employed to enhance scalability. This study proposes a hybrid framework that classifies disasters based on severity and then routes them through one of three pipelines. This approach ensures that the Quality of Service requirements for each disaster severity type are satisfied. The pipeline assigned to handle high-severity traffic demonstrates notification delivery with latency in the order of 0.88 seconds on average across the disaster types.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - The integration of wind-solar hybrid systems presents a transformative pathway to bolster renewable energy resilience, yet their optimal deployment in geographically diverse environments remains hindered by spatiotemporal intermittency, conflicting stakeholder priorities, and dynamic ecological constraints. This study proposes a novel fuzzy meta-goal programming (FMGP) framework to reconcile techno-economic, environmental, and social objectives in wind-solar hybridization, with a focus on arid and coastal ecosystems—regions characterized by contrasting meteorological volatility and land-use sensitivities. By embedding fuzzy set theory into meta-goal structures, the model quantifies uncertainties in renewable resource availability (e.g., wind shear variability, solar irradiance fluctuations) while balancing antagonistic criteria such as levelized energy cost minimization, carbon footprint reduction, and biodiversity preservation. The FMGP approach uniquely incorporates synergistic complementarity metrics to exploit temporal offsetting between wind and solar generation cycles, enhancing grid stability in resource-erratic zones. Empirical validation through case studies in a hyper-arid desert and a storm-prone coastal region reveals Pareto-optimal solutions that achieve up to 23% improvement in annual energy yield reliability and 18% reduction in land-use conflicts compared to conventional multi-objective models. Furthermore, the framework introduces a stochastic acceptability index to evaluate policy robustness under climate change scenarios, demonstrating adaptive capacity in mitigating energy-water nexus pressures in arid areas and storm resilience trade-offs in coastal grids. This research advances sustainable hybrid system design by harmonizing multi-scale environmental governance with precision energy planning, offering policymakers a decision-centric tool to navigate the socio-ecological complexities of the renewable transition.
Authors - Kala V Krishnan, Ramanathan P V Abstract - India is currently experiencing a demographic dividend, with the youngest working population predicted to remain until 2050. Scholars emphasize that this demographic advantage alone cannot guarantee the economic growth of the country unless accompanied by effective policy interventions. Skill development has thus emerged as a national priority to harness this opportunity, leading to large-scale initiatives such as the Pradhan Mantri Kaushal Vikas Yojana (PMKVY). As India’s flagship skill certification scheme, PMKVY aims to provide industry-relevant training to youth. Despite its magnificent scale and substantial investment, the program faces a major challenge of high dropout rates among participants, which warns its long-term effectiveness and results in significant economic and social costs. Existing research highlights the need for analysing these dropout trends, especially at the state level, where disparities may reflect underlying regional and structural factors. This study adopts a quantitative cross-sectional comparative approach to assess the dropout rates of PMKVY across Indian states and examine the impact of selected state-specific factors: poverty rate, literacy rate, population density, and industrial development. The objective is to identify the extent of regional variation and analyse the relationship between these state-specific factors and program attrition. By doing so, the study aims to generate insights that can inform policy reforms and targeted interventions to improve retention and enhance the efficiency of government-supported skill development programs in India.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - The strategic allocation of marine oil resources involves complex trade-offs between economic performance, operational efficiency, and environmental sustainability—often under significant uncertainty. This paper develops a robust decision-support framework that integrates Fuzzy Logic with Nonlinear Goal Programming (NLGP) to address the multi-objective optimization problem inherent in marine oil extraction and resource allocation. Uncertainty in key parameters— such as extraction costs, production yields, and environmental impact limits—is modeled using fuzzy sets, enabling a more flexible representation of real-world ambiguity. The model simultaneously optimizes multiple nonlinear and conflicting goals, including profit maximization, cost minimization, and ecological risk reduction. To efficiently solve the resulting nonlinear programming problem, a hybrid solution approach is proposed that combines fuzzy goal programming techniques with metaheuristic optimization, specifically a tuned Genetic Algorithm. The framework is applied to a representative offshore oil field scenario, demonstrating superior performance in solution quality and robustness compared to traditional linear and crisp optimization methods. The results underscore the potential of fuzzy NLGP models in supporting high-stakes operational decisions in uncertain and dynamic environments. This work contributes to the growing body of operations research methods that address multi-criteria decision-making under uncertainty, with direct implications for energy resource planning and sustainable marine operations.
Authors - Smita Kalokar, Ritesh Sule, Dinesh Mirkute Abstract - A nation's progress is supported by its effective administration. To ensure the betterment of the country the governments of all the world has accepted the sustainable development goals which is announced by United Nation Development programme (UNDP) in 2015,as acceptable target by the member country.The transformation in digital Technology has introduced a new focus for government in India. Nowadays, Information and Communication Technology (ICT) is essential. In particular, for developing nations, it is viewed as vital for economic, social, and political progress. The introduction of low-cost smart phones and the lowest-priced data packages have increased the opportunities for both citizens and government equipment to take advantage of e-Government's advantages. In recent years, e-Governance initiatives in India have shown their effectiveness in reducing processing expenses, enhancing transparency, and fostering economic growth through income-generating activities, increased agricultural output, and advancements in health and education sectors. These improvements collectively enhance the quality of life for Indian citizens. Nevertheless, e-Governance has not fully reached all demographics, particularly in rural areas. In rural India, there are specific basic and cultural challenges that hinder the achievement of e-Governance objectives. Consequently, the government must address these issues and ensure that service delivery mechanisms are user-friendly for these communities. This paper will explore ICT and Governance in India, along with the fundamental challenges and acceptance of e-Governance.
Authors - Chauhan Priyank Hasmukhbhai, Ritu Khanna Abstract - Real-time optimization of virtual reality (VR) models in dynamic systems demands adaptive decision-making frameworks capable of reconciling conflicting objectives such as computational efficiency, latency reduction, and user experience fidelity. While traditional optimization techniques often struggle with the non-linear, high-dimensional, and time-sensitive nature of VR environments, this paper introduces a novel hybrid framework that synergizes Goal Programming (GP) and Neural Network Artificial Intelligence (NN-AI) to address these challenges. The proposed methodology leverages GP to formalize multi-objective decision-making under constraints, while a dynamically trained neural network predicts and prioritizes system states in real time, enabling context-aware adjustments to VR model parameters. By integrating GP’s structured optimization with NN-AI’s predictive adaptability, the framework achieves Pareto-optimal solutions that balance competing objectives across fluctuating operational conditions. The study validates the framework through a series of simulated and real-world VR scenarios, including immersive gaming and industrial training systems, where dynamic variables such as user interactions, environmental complexity, and hardware limitations are present. Results demonstrate a 22–35% improvement in rendering efficiency and a 40% reduction in latency compared to conventional single-objective optimization approaches, without compromising visual quality. Furthermore, the system exhibits robust generalization capabilities, adapting to unseen scenarios within 5–10 iterations. This research bridges a critical gap in real-time multi-objective optimization for VR, offering a scalable, AI-driven solution for industries reliant on immersive technologies. The framework’s ability to harmonize human-centric objectives with computational constraints positions it as a transformative tool for next-generation dynamic systems in entertainment, healthcare, and Industry 4.0 applications.
Authors - Thuan Nguyen Dinh, Truong Nguyen Xuan Abstract - In this paper, the authors suggest and compare models for industrial oven Time-to-Failure (TTF) prediction within a critical 60-minute timeframe using sensor data. They compare traditional methods such as LSTM, GRU + Attention, and XGBoost with a new hybrid approach: CNN-Autoencoder + XGBoost (CNN-AE+XGBoost). Experimental outcomes, in terms of RMSE, MAE, and R², confirm the performance superiority of the proposed system. For the complete dataset, R² for the hybrid model was 0.89, well ahead of LSTM (0.27), GRU + Attention (0.47), and XGBoost (0.83). Importantly, for the focused TTF ≤ 60 minutes frame, it also had a low Mean Absolute Error (MAE) of 6.57. These results present the CNN-AE+XGBoost model as an effective predictive maintenance tool for curbing production downtime within the food processing sector.
Authors - And Julie D. Rosal Abstract - This study checks on emerging research trends and conducts a bibliographic analysis on smart waste bins as foundational elements for cost-effective, automated waste management solutions. The escalating urbanization and environmental pressures necessitate advanced waste management technologies, where smart trash bins equipped with IoT (Internet of Things), AI (Artificial Intelligence), and robotics offer transformative solutions such as automated segregation, real-time monitoring, and optimized collection routes. Employing a systematic literature review facilitated by Scopus AI, the study identifies consistent, rising, and novel themes in smart bin research. IoT-enabled systems consistently emerged, underscoring their role in operational efficiency and sustainability. Rising trends prominently featured AI integration, robotic automation, and advanced waste segregation methods, indicating a clear shift towards intelligent automation. Security and privacy issues constitute novel themes, reflecting the growing importance of ensuring trust and reliability in technology adoption. The bibliometric analysis revealed significant global research contributions with notable peaks aligning with heightened global sustainability goals. Additionally, the study proposes a conceptual prototype integrating sensors, AI-driven categorization, and autonomous collection mechanisms, validated through simulation testing. Findings demonstrated improved operational efficiency, higher recycling accuracy, and substantial cost savings. Addressing integration challenges, infrastructure compatibility, and public acceptance, the study provides critical insights for urban planners, policymakers, and stake-holders. This work significantly contributes to the discourse on sustainability and smart city initiatives, highlighting technological innovation as pivotal in solving contemporary environmental management issues.
Authors - Akheel Mohammed, Sameera Khanam, Ayesha, Mohammed Shah Alam Rasool Khan Abstract - The proliferation of cloud computing presents a significant challenge for enterprises: entrusting mission-critical data to remote service providers. The widespread adoption of cloud services is fundamentally hampered by a lack of robust and reliable trust evaluation mechanisms. To address this critical obstacle, this paper introduces a novel reputation-based trust model designed to accurately assess the trustworthiness of Cloud Service Providers (CSPs). Our proposed model uniquely integrates three key metrics: direct customer feedback, historical server rejection rates, and real-time server workload. A specialized trust evaluation algorithm processes these inputs to generate a comprehensive reputation score. Experimental results validate the efficacy of our approach, demonstrating a more efficient and accurate evaluation of CSP trustworthiness compared to existing models.
Authors - Ashwini Matange, Jibi Abraham Abstract - Major component of research is searching for relevant publications in the domain of research from varied sources, exploring them, organizing them and finding interconnections between them. Different platforms ranging from Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Springer are to be visited and explored to find research papers in the relevant field of the researchers. Research Rabbit is a free to use literature mapping tool that provides a one stop venue to locate papers of interest effortlessly with effective visualizations and smooth navigation.
Authors - Seema Shrawne, Aishwarya Jadhav, Saniya Gupte, Sayali Panch, Sakshi Thombre, Vaibhav Dhore, Vijay Sambhe Abstract - Accurately detecting and counting fruits is crucial for improving yield estimates in Precision Agriculture. However, challenges such as varying fruit types, overlapping objects, and cluttered backgrounds make this task difficult. In this study, we use RetinaNet, a deep learning model known for detecting dense objects, to address these issues. By replacing its ResNet50 backbone with SEResNet18, we improve both accuracy and speed, increasing mAP by 1.65%at IoU 0.5 and 1.79%at IoU 0.7. We also fine-tune the model using Differential Evolution to optimize anchors, further boosting mAP by 0.61% and 7.3% at IoU 0.5 and 0.7, respectively. Our enhanced model performs better on KFuji apples, mangoes, and holly fruits, while the default model slightly outperforms ours on strawberries and oranges at higher IoU thresholds.
Authors - Meeradevi, Maria Rufina P, Prathik B, C S Parthasarathy, Janya V, Neeha Ritvika Mandava Abstract - PathoVision is an AI-powered framework that enhances cancer diagnosis by integrating histopathology images with clinical text using multimodal learning. Unlike traditional systems focused solely on image analysis, it employs the PLIP model to generate combined visual-text embeddings for more accurate and contextual understanding. A custom decoder pro- cesses these embeddings to classify cancer subtypes, while Grad- CAM provides interpretable heatmaps, ensuring transparency in decision-making. Designed for use in clinical diagnostics, research, and education, PathoVision addresses the limitations of manual pathology—such as subjectivity and time constraints—by offering a reliable, interpretable, and accessible solution. This framework represents a significant advancement in computational pathology, blending modern AI with traditional diagnostics to support expert pathologists and improve patient outcomes.
Authors - And Julie D. Rosal, David Livingstone T. Ramos, Randy Joy M. Ventayen Abstract - This study explores the quality assurance (QA) practices of Pangasinan State University (PSU) from 2016 to 2025, emphasizing key strategies that enhanced its national and global reputation. Using a multi-method approach—including literature review, surveys, and interviews, examines stake-holder perceptions, institutional challenges, and achievements in quality assurance recognition, and improved international rankings. The findings highlight the importance of strong leadership, continuous improvement, faculty development, and stakeholder engagement in fostering a culture of quality. These elements have strengthened PSU’s quality management system and its responsive-ness to academic and institutional demands. As a forward strategy, the study presents a five-year roadmap for 2026–2030 focused on sustaining quality through digital transformation, global benchmarking, research-driven planning, and performance-based evaluation. This roadmap is intended to solidify PSU’s status as a globally competitive university and offers practical insights for other state universities aiming to institutionalize QA and improve their academic standing at both national and international levels.
Authors - David Livingstone T. Ramos Abstract - Quality assurance (QA) in higher education is a critical mechanism for promoting academic excellence, institutional ac-countability, and societal impact. State Universities and Colleges (SUCs) in the Philippines serve as gateways to education for under-served regions but face significant challenges in maintaining and im-proving quality due to the absence of standardized frameworks, reliance on manual processes, and limited technological integration. These issues hinder their efficiency, compliance with accreditation standards, and ability to respond to evolving educational demands. This study examines the role of a Quality Assurance Information System (QAIS) as a solution for addressing these challenges. Employing a literature review methodology, it evaluates current QA practices in SUCs and identifies gaps, such as fragmented processes, inconsistent standards, and insufficient data management capabilities. The research emphasizes the potential of QAIS to streamline processes, automate routine tasks, and provide real-time data for evidence-based decision-making. Key components of an effective QAIS include modular design for scalability, compliance with international standards, user-friendly interfaces, and robust analytics for monitoring and continuous improvement. By implementing QAIS, SUCs can align their practices with global benchmarks, enhance stakeholder engagement, and improve institutional performance. The study also highlights challenges in adopting QAIS, including resource limitations and resistance to change, and proposes strategies to mitigate these barriers. The findings offer actionable recommendations for SUCs to implement and scale QAIS effectively, aligning with national and international quality standards while addressing local needs. By leveraging QAIS, SUCs can enhance their competitive-ness, improve educational outcomes, and contribute to broader national development goals.
Authors - Pravitha N R, Sumedha N Varma Abstract - This study reflects the pivotal role of financial technology (Fin-tech) in reshaping the IT and Corporate sectors while playing a role in achieving sustainable development. Fintech has evolved into a significant role as a game changer, driving innovation in financial contribution through digital platforms, blockchain technology, artificial technology, and big data analytics. In the IT sector, Fintech facilitates secure, efficient transactions and enhances digital infrastructure, leading to improved service delivery and client engagement. Within the corporate domain, it streamlines operations, increases financial inclusion, and supports strategic decision-making. In addition, the integration of Fintech services with principles of sustainability is enhancing green finance and promoting efficient digital solutions. This paper studies the factors affecting the adoption of fin-tech services and how sustainable development goals are achieved by adopting fin-tech services. By using regression analysis, the study expresses that Digital Financial Literacy and Perceived Performance Expectancy have a positive impact on the Behavioural Intention in adopting fintech services. The analysis further states that the actual use of fin-tech services plays a vital role in promoting sustainable development.
Authors - Sasini Gandhari Dabare, Indra Mahakalanda, Prasanga Jayathunga, Nathali Fernando Abstract - Media Mix Modeling (mMM) is a data-driven analytical approach that quantifies the influence of multiple advertising channels on sales performance while adjusting for external factors such as seasonality and economic conditions. This study explores machine learning-based mMM to predict weekly sales performance in the Sri Lankan home appliances industry by combining ad spending variables with external factors such as exchange rates and holidays. The study applies time series decomposition, ad stock transformation, and data normalization to capture delayed and nonlinear media effects. TimeSeriesSplit cross-validation was used to test four predictive models: Linear Regression, XGBoost Regression, Support Vector Regression (SVR), and Bayesian Ridge Regression, along with key metrics for performance (MSE, RMSE, R² Score, MAPE). The tuned XGBoost model had the best accuracy (R² = 0.9145; MAPE = 2.87%), making it most useful for predicting. The findings demonstrate the importance of machine learning-enhanced MMM as an effective strategy for optimizing media spending and increasing advertising efficiency in competitive, budget-constrained markets.
Authors - Hera Khairunnisa, Nayla Nandhita Nuril Hadi, Surya Anugrah, Rochma Sudiati, Windy Permata Suyono Abstract - First objective of this study is evaluating MSMEs tax compliance with Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Second objective of this study is evaluating the role of Core Tax Administration System (CTAS) for MSME. The study uses a descriptive qualitative approach, with semi-structured interviews with three tax consultants with extensive experience in MSME taxation. This study also employs the literature review approach. From the interview, this study shows that MSMEs typically have straightforward cost structures given their simple business operations. Government support, such as the reduction of the final tax rate for MSMEs from 1% to 0.5%, is viewed as an opportunity in taxation. However, the study highlights ongoing challenges in MSME operators' tax competence, particularly their understanding of recent tax regulations and literacy in tax matters. This study also shows that the implementation of the Core Tax System serves as a strategic advancement in enhancing tax administration for MSMEs in Indonesia. By leveraging digital integration and automation, the system aims to promote greater efficiency, transparency, and ac-countability in tax processes.
Authors - David Livingstone T. Ramos, And Julie D. Rosal, Randy Joy M. Ventayen, Timothy Joshua M. Ventayen Abstract - This study look into the practical acceptance and compliance of AI-generated music on major digital distribution platforms, including Spotify, Apple Music, and other popular online music stores. Leveraging advanced generative AI tools such as Udio and Suno, the researchers created original musical tracks and submitted them for distribution. Although many platforms have explicit policies against the use or upload of AI-generated content, all tracks produced through these tools were accepted without issue and made publicly avail-able through the distribution systems. This outcome reveals critical gaps in the enforcement of platform policies, raising serious questions about the current ability of digital platforms to detect and regulate AI-generated content. The findings bring to light the ethical, legal, and economic challenges posed by the growing presence of generative AI in the music industry. In response, the study recommends the implementation of more robust detection technologies, trans-parent labeling of AI-generated works, and the development of consistent regulatory frameworks to address the complexities of AI-driven creativity in the evolving digital music ecosystem.
Authors - Masood Ahmed Shariff, Sushma R Abstract - In today’s complex financial environment, identifying optimal stock investments is challenging. This study applies Multi-Criteria Decision-Making (MCDM) techniques to evaluate NIFTY 50 stocks using nine financial indica-tors, including returns, P/E ratio, dividend yield, and market capitalization. Daily data from April 2020 to March 2025, sourced from the Prowess database, was analyzed. The Entropy Weighting Method ensured objective assignment of metric importance. Four MCDM models—TOPSIS, VIKOR, SAW, and COPRAS—were used to rank stocks. Ranking discrepancies led to the development of a hybrid model combining normalized ranks with entropy-based weights, creating a composite ranking. Spearman’s rank correlation helped compare the models. The hybrid approach enhances ranking consistency and supports data-driven investment decisions. This methodology can also be ap-plied to sectoral or global equity markets, offering a flexible tool for portfolio evaluation and financial analysis.
Authors - Tawsif Hasan Kirkire, Smita Mehendale, Ashish Thatte Abstract - This paper is bibliometric analysis of the academic literature related to e-commerce platforms within the chemical supply chain. From the 17 documents published between 2000 and 2025, this study identifies inconsistent, but off late accelerating, research journey, with a notable rise in publications and citations post-2020, most likely influenced by the global imperative for digital transformation. Co-word analysis reveals "electronic commerce" as a central thematic node, connecting traditional "sales" and "chemical industry" applications with emergent areas like "learning systems" and "chemical activation." The analysis highlights China's dominant intellectual contribution, suggesting a concentrated epistemic center. Inspite of recent contributions from diverse sources, the field displays significant temporal and thematic gaps, particularly regarding sustainability and resilient practices, alongside a uneven collaborative network. This study outlines critical future research directions, promoting for the integration of AI/ML applications, exploration of chemical manufacturing, diversification into Global South economies, and the adoption of enhanced methodological rigor to foster a more mature, interconnected, and impactful interdisciplinary research domain.
Authors - Juanito P. Maneclang Jr. Abstract - The Philippine salt industry, particularly in culturally significant regions like Pangasinan, has faced a steep decline due to a reliance on antiquated, laborintensive production methods. This study addresses this challenge through a three-part objective-driven approach: (1) identifying the technological gaps in artisanal salt production through a comprehensive literature review and needs analysis; (2) developing a prototype salt vibrating screen classifier with an integrated grinder to address these gaps; and (3) evaluating the prototype's acceptability among experts and end-users. The methodology involved a systematic re-view of academic and industry literature, which pinpointed a lack of appropriate-scale post-harvest machinery. A prototype, fabricated from stainless steel and powered by a 5-horsepower motor, was subsequently developed. The machine's acceptability was determined through a mixed-method evaluation with technical experts and salt farmers. The results from the evaluation demonstrated high acceptability, with participants confirming the machine's robust performance, efficiency in grinding and classifying salt, and significant practical advantages over traditional methods. The study concludes that the developed prototype is a highly acceptable and viable technological solution that directly addresses the needs identified in the literature, offering a means to enhance productivity and quality for Filipino salt farmers.
Authors - R. Komala, B. R. Arun Kumar, A. Shreyas Abstract - Service Level Agreements (SLAs) are essential for defining performance and compliance expectations in cloud computing ecosystems. However, traditional SLA models often lack adaptability, failing to address dynamic workloads, evolving regulatory demands, and rising cybersecurity threats. This study presents an adaptive SLA governance architecture driven by Machine Learning (ML), enabling predictive violation detection, real-time compliance monitoring, and dynamic policy adjustments. The system integrates legal frameworks such as GDPR, ISO/IEC 27001, NIST, and India's DPDPA, embedding enforceable standards into SLA logic. Our framework employs ML techniques including violation forecasting, unsupervised anomaly detection, privacy risk quantification, and NLP-based clause synthesis, creating self-adjusting, legally resilient SLAs. In simulations with cloud workload datasets, the system achieved 94% prediction accuracy for SLA breaches, 98% anomaly detection, and over 91% success in automated clause alignment. While promising, broader testing in real-world deployments is needed to validate generalizability. This framework bridges regulatory obligations and dynamic service delivery through explainable, transparent SLA orchestration.
Authors - Akshay Kumar, Sudhir Agarmore, Kumar Gaurav, Edidiong Akpabio, Akshat Jha Abstract - Cyber threats now come in an advanced form and, therefore, call for a proactive rather than a passive cybersecurity strategy. Threat intelligence helps to complement security defences through methodical acquisition, analysis, and utilization of information regarding competitors and new risks. This article gives an insightful review of the approaches to threat intelligence, covering open-source intelligence (OSINT), closed-source intelligence, monitoring of the dark web, and live feeds of intelligence. We classify threat intelligence into strategic, tactical, operational, and technical levels, explaining their functions in cyber security architectures. Particular emphasis is given to the incorporation of artificial intelligence (AI) and machine learning (ML) in the automation of threat identification and intelligence analysis. In addition, we address challenges of implementation, including data glut, verification, and ethics. To provide real-world practicality, we examine actual case studies demonstrating practical threat intelligence implementations in reducing ransomware, blocking phishing, and countering supply chain attacks. We also recommend enhancing intelligence sharing, predictive analytics, and automation to strengthen future threat intelligence systems. Through the integration of dynamic threat intelligence into security operations, organizations are able to effectively detect, analyze, and block cyber threats beforehand, ultimately fortifying their cybersecurity resilience as a whole.
Authors - Romary Reyes Lincod Abstract - This paper provides an review of the pedagogical practices and governance perspectives related to technology integration in Philippine State Universities and Colleges (SUCs). The global pandemic served as a catalyst, accelerating a system-wide shift from emergency remote teaching to a strategic push for sustained flexible learning, underpinned by national policies from the Commission on Higher Education (CHED) and cross-agency collaborations. However, a significant gap persists between ambition and institutional reality. This review examines four key domains: institutional governance and leadership, pedagogical practices in the digital classroom, the human element of faculty and student experiences, and the foundational pillars of infrastructure and finance. Findings reveal a governance paradox where strategic planning is robust, but leadership needs to translate vision into faculty competency. Pedagogically, technology adoption outpaces deep integration, with tools often used to replicate traditional methods. Faculty and students are caught in a "Cycle of Unpreparedness," marked by skill gaps, anxiety, and infrastructural barriers. Foundational weaknesses, including a pervasive digital divide and unstable funding models, create a mismatch between the vision for digital transformation and the capacity for implementation. The paper concludes with multi-level strategic recommendations for policymakers, SUC administrators, and future research, aimed at creating a more resilient, equitable, and innovative digital education ecosystem.
Authors - Caren C. Orlanda-Ventayen, Renato E. Salcedo Abstract - The tangible heritage of a region, particularly its textile traditions, of-ten serves as a primary marker of its cultural identity. This literature review studies the role of fabric arts in the cultural expression of Pangasinan province in the Philippines. A systematic review of historical backgrounds, cultural publications, and recent news archives reveals a notable absence of a deeply rooted, continuous, and widespread indigenous textile weaving tradition for clothing in the province. Historically, Pangasinan's material culture was characterized by non-textile crafts and a reliance on imported fabrics obtained through extensive trade networks. This historical context makes the contemporary situation particularly compelling. As of mid-2025, Pangasinan is the site of new, institution-ally-driven initiatives aimed at establishing a local textile industry. These projects are twofold: a technology-focused hub for developing bamboo-based textile fibers and a culturally-focused center for reviving the transplanted hand-loom weaving traditions of Cordilleran migrant communities. This paper argues that Pangasinan is not merely preserving a dormant craft but is actively designing a new and evolving cultural identity in fabric, one that is uniquely shaped by the confluence of technological innovation, cultural migration, and institutional support. The findings suggest that Pangasinan offers a unique case study in the contemporary construction of cultural heritage.
Authors - Claro S. Sison Jr. Abstract - The rapid shift to remote education has compelled a significant trans-formation in the teaching methodologies for technical subjects like AutoCAD. This paper investigates the technological interventions implemented by Auto-CAD instructors in response to the challenges of online and modular learning environments. The study adopts a mixed-methods approach, beginning with a systematic literature review to identify prevalent challenges and technological solutions documented in academic and technical sources. This is followed by a local survey of 30 AutoCAD teachers in the Schools Division of Pangasinan I, Philippines, to assess the available technological resources, their utilization rates, and the specific challenges faced in a developing country context. Key findings from the literature indicate that while a plethora of advanced software, platforms, and tools are available, their effective implementation is often hindered by issues such as the digital divide, lack of institutional support, and the inherent difficulty of teaching hands-on skills remotely. The local survey corroborates these findings, revealing a heavy reliance on personal resources, unstable internet connectivity, and underutilization of advanced CAD-specific tools. The study reveals that successful interventions often involve a blend of synchronous and asynchronous technologies, including video tutorials, cloud-based CAD solutions, and virtual reality simulations. A strong positive correlation (r = 0.785) was found be-tween the level of technological intervention and teachers' perceived instructional effectiveness. This research concludes by synthesizing the findings to provide a holistic view of the current state of AutoCAD education, offering recommendations for educators, institutions, and policymakers to enhance the quality of technical instruction in remote settings.
Authors - Corlito P. Cruz Abstract - The proliferation of portable computing devices has historically been accompanied by an equally diverse and incompatible array of proprietary power adapters, creating significant issues for consumers and contributing to a growing electronic waste crisis. This scientific review charts the rise of the USB integrated laptop charger adapter as a pivotal solution to this long-standing issue. It presents an in-depth analysis of the technological underpinnings, centered on the USB Power De-livery (USB-PD) specification, that enable a universal charging standard. Key enabling technologies, most notably the material science advancements of Gallium Nitride (GaN) wide-bandgap semiconductors, are analyzed for their role in creating a new generation of compact, highly efficient, and intelligent power adapters. To ground this technological analysis in practical application, this paper further presents a targeted case study involving a survey of Bachelor of Industrial Technology (BIT) students to identify tangible user needs. The results of this survey, which indicated a strong demand for integrated USB ports on laptop chargers, led to the development of a functional proof-of concept prototype, validating the broader market trends dis-cussed. The paper examines the technical evolution of the USB-IF standards, the current technoeconomic landscape, and the persistent challenges in engineering and usability, including protocol fragmentation and safety compliance. Finally, this re-view looks to the future, assessing the impact of the 240W Extended Power Range (EPR) of USB-PD 3.1 and the trajectory toward a truly universal and intelligent power ecosystem. It concludes that the USB-integrated adapter represents a critical, user-driven inflection point in consumer electronics, moving the industry from a fragmented past to a standardized, efficient, and interconnected future.
Authors - Ramesh Chandra Poonia, Vishal Singh Rathore, Ajay Kumar Abstract - In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments.
Authors - Smit Patel, Priyanka Patel Abstract - Speech Emotion Recognition (SER) plays a vital role in enhancing human-computer interaction by enabling machines to interpret and respond to human emotions. This study focuses on SER using the RAVDESS dataset, emphasizing speech-only modalities. A comprehensive set of audio features including MFCCs, chroma, spectral contrast, tonnetz, and wavelet transforms is extracted, and the performance of four deep learning models— CNN, LSTM, BiLSTM, and CNN-LSTM—is evaluated. Among them, CNN achieves the highest accuracy (68.37%), with strong F1-scores across several emotion classes. The results underscore the effectiveness of spatial feature extraction in emotion classification and suggest further enhancements using attention mechanisms and transformer-based models.
Authors - Sundar S, Prathilothamai M Abstract - Accurate prediction of monsoon rainfall remains a persistent challenge due to the intricate interplay among meteorological conditions, oceanic influences, and broader climatic patterns. While significant effort has been devoted to improving model architectures, the effect of input feature composition on prediction accuracy has received relatively less attention. This study addresses that gap by conducting an extensive empirical evaluation of 2,047 feature group combi-nations, systematically derived from eleven curated sets of climate-related variables. Using a hyperparameter-tuned XGBoost model, each configuration was evaluated independently to assess the predictive contribution of domains such as lagged climate indices, cyclical temporal encodings, and event-based indicators. The results show that model performance improved significantly from an R² of 0.5801 (RMSE: 10.8999 mm, MAE: 4.9009 mm) using only meteorological features to an R² of 0.7606 (RMSE: 8.2297 mm, MAE: 3.8752 mm) when combined with oceanic and climatic inputs, particularly lagged MJO and ENSO indices and temporal signals. These insights reinforce the value of domain-informed feature fusion and provide a replicable approach to enhancing monsoon prediction models through thoughtful feature group design and empirical validation.
Authors - Atharva Godkhindi, Anjali Naik Abstract - Fraud detection in financial transactions presents a persistent challenge due to extreme class imbalance and evolving attack patterns. While several machine learning (ML) and deep learning (DL) methods have shown promise, these solutions are fragmented and use traditional methods to address the severe class imbalance, leading to models with inflated metrics and poor generalization. In this study, we propose a unified ML-DL-XAI pipeline that integrates Variational Autoencoders (VAE) not only for data augmentation but also for feature engineering. Unlike traditional resampling, the VAE enables representation learning that preserves underlying data distributions while mitigating overfitting. Our pipeline incorporates interpretable machine learning models alongside neural networks to ensure both high performance and explainability. Empirical evaluations on a large-scale financial dataset demonstrate superior and reliable performance, that achieves an accuracy of 99.6%, a precision of 92%, and a recall of 85%, outperforming several recent benchmarks. By combining augmentation, feature engineering, and explainability in a single pipe-line, this work offers a robust and practical answer for real-world fraud detection applications.
Authors - Gitanjali S. Poothuvallil, Dhanya Manayath Abstract - This paper explores the use of generative AI in social entrepreneurship education through the REFLECT model—a justice-informed ethics framework emphasizing participatory, empathic, and power-sensitive reasoning. Using ChatGPT, we simulated context-rich dialogues based on real-life renewable energy cases to examine ethical dilemmas, systemic failures, and power dynamics. Through iterative refinement, we developed a prompt template capable of producing realistic, grounded dialogues. These were evaluated on three criteria: depth of empathy, systems complexity, and presence of ethical tension. Findings suggest that integrating the REFLECT framework enhanced the pedagogical quality of AI-generated cases, aligning them more closely with the aims of values-based social entrepreneurship education. The approach demonstrates how generative AI can foster critical thinking and ethical reflection, helping students engage with complex social justice and sustainability issues. Our study presents a novel application of AI in the classroom, showing its potential to ad-vance pedagogy in socially responsible entrepreneurship. The paper concludes by exploring the future potential of integrating AI into educational pedagogy, highlighting both its ethical implications and the ongoing need for refinement in its application to ensure responsible and effective use in teaching and learning environments.
Authors - Bharateesha lvn, J Vignesh, Jabez Lawrence G, Bhaskarjyoti Das Abstract - The emergence of sophisticated cyber threats calls for the evolution of sophisticated Network Intrusion Detection Systems (NIDS). Even though graph-based approaches have been promising, they have mostly was concerned with node classification to determine the bad actors. This paper provides a new framework that recontextualizes the NIDS challenge as a marginal concern classification problem on a heterogeneous graph. We assume that classifying the boundaries (relations) between network objects as harmful or benign offers a more timely and efficient means of intrusion detection. In order to achieve this, we build a heterogeneous graph from network flow data and employs a Heterogeneous Graph Transformer (HGT) model, which is designed to maintain the integrity of instructional and semantic detail formation present in such graphs. The model is trained and tested on a large dataset from the UNSW-NB15 dataset. Our experiments show that the edge classification method greatly outperforms a conventional node classification baseline, achieving superior accuracy, precision, and recall. These results illustrate the potential of edge-centric GNN models for constructing more efficient and complete network intrusion detection systems.
Authors - Poojitha Panchakarla, Sarvani Kocherlakota Abstract - Cryptocurrency has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. To investigate its current research landscape and emerging trends, we conduct a Bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, and collaborations among countries, authors, documents and the contributing academic journals during the study period. Lecture Notes in Networks and Systems is having the highest publications and IEEE Access is having the most cited document. Kumar A is the most contributing author and Song H is the most cited author. India is the most cited country and contributing country. By research gap analysis, the future direction in the domain of cryptocurrency can be machine language forecasts, sustainable energy, and regulatory framework.
Authors - Jiri David, Jan Fabry, Josef Bradac Abstract - With the growing importance of electromobility, the efficient planning in the production of lithium-ion batteries has become a critical factor in maintaining competitiveness. This article focuses on the optimisation of batch scheduling on parallel processors – an essential challenge in a complex manufacturing environment characterised by hybrid (sequential-parallel) processes, technological dependencies, and high variability. Based on a formal mathematical model of the P|batch, rj , sj | Cmax type, a method is proposed that integrates batch planning, nonlinear setup times, multi-objective optimisation, and robust scenariobased control. The model was implemented in the AMPL (A Mathematical Programming Language) environment and tested using real production data from battery manufacturing at the famous production company. The results demonstrate significant improvements over conventional methods (e.g., FCFS, LPT), including a reduction in production time of up to 10%, a 6% decrease in setup operations, and an increase in capacity utilisation (OEE) by more than 10%. Moreover, reductions in energy consumption and enhanced schedule predictability were observed. The model is designed to be integrable with MES/ERP systems and offers both scalability and adaptability across varying production scenarios. The findings confirm that the combination of a rigorously defined optimisation model and operationally validated data can significantly enhance planning efficiency in battery system manufacturing for electric vehicles.
Authors - Etty Gurendrawati, Hera Khairunnisa, Aji Ahmadi Sasmi, Andrew Saw Tek Wei, Nayla Nandhita Nuril Hadi, Rohadatul Aisy, Shoofiyah Nur Aliifah Abstract - This study examines how Information Quality (IQ), System Quality (SQ), and Service Quality (SeQ) influence Use (U) and User Satisfaction (US). It also investigates the impact of U on US, and how both US and U affect Net Benefit (NB) among Public Sector Accounting students at an Indonesian State University. Data from 114 student questionnaires were analysed using SEM PLS by SMART PLS 4. Seven of nine hypotheses were supported. SQ significantly impacted U and US, while SeQ significantly affected U. Both U and US positively influenced NB, with US having the strongest effect. The insignificant role of IQ suggests students prioritize system functionality and support over data output. These findings emphasize that enhancing system and service quality drives user satisfaction and use, ultimately boosting the perceived net benefits of nonprofit accounting information systems.
Authors - Rhytheema Dulloo, Kirti Biradar, Srijaa M Abstract - The aviation industry faces mounting pressure to achieve net-zero carbon emissions by 2050, yet passenger adoption of sustainable aviation practices remains inconsistent, highlighting the urgent need to understand the psychological and behavioral factors influencing passenger decision-making in sustainable aviation contexts. This study develops and tests an integrated theoretical framework combining Theory of Planned Behavior (TPB), Value-Belief-Norm (VBN) theory, and Technology Acceptance Model (TAM) to explore how environmental values, technology perceptions, and travel context shape attitudes, intentions, and behaviors. A quantitative cross-sectional study of 847 airline passengers across seven metropolitan hubs in India was conducted. Environmental values emerged as the strongest predictor of attitudes toward sustainable aviation behavior (β = 0.42, p < 0.001), while the integrated model explained 62% of variance in behavioral intentions and 34% in actual behavior. Perceived usefulness and ease of use were found to significantly affect sustainable aviation technology acceptance and TPB constructs effectively predicted behavioral intention and actual behavior towards sustainable aviation, with per-sonal norms adding further explanatory power. Significant differences were found between business and leisure travelers, with leisure travelers showing stronger relationships between environmental values and attitudes (p = 0.032) and between intentions and behavior (p = 0.007), while the intention-behavior gap was more pronounced among business travelers, highlighting structural barriers to sustainable aviation adoption. The findings suggest that airlines should adopt differentiated strategies, with sustainability messaging emphasizing personal environmental responsibility for leisure travelers, while structural interventions such as corporate partnerships and policy changes are needed for business travelers to address organizational barriers. This study provides the first comprehensive integration of TPB, VBN, and TAM theories in sustainable aviation contexts, offering novel insights into travel context moderation effects and actionable guidance for industry stakeholders seeking to enhance passenger adoption of sustainable aviation practices.
Authors - Shital Khaparde, Rakesh Verma Abstract - In the field of Speech Emotion Recognition (SER) the research is gaining more attention, with the ability to augment human-computer interaction by allowing systems to recognize emotional states from speech. The research area has evolved significantly, with most of the advancements being due to deep models of learning which are particularly good at extracting subtle patterns from raw audio data directly. The review discussed the current research, with focus on the use of prevalent deep learning architectures—e.g., Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), , and hybrid models like CNN-BiLSTM—for SER tasks. The area of research has come a long way, some of the major challenges still persist. The design of dependable SER systems is still beset by issues such as imbalanced datasets, the ongoing difficulty of effective feature selection, noise sensitivity, and computational efficiency problems. Although deep learning has advanced remarkably, the review emphasizes that significant obstacles must still be overcome before the area can provide consistently trustworthy performance in actual situations. This paper proposes several future research including broadening and diversifying datasets, increasing the cross-linguistic strength of the model, and better real-time system performance.
Authors - Sharon Elizuba Koshy, Shyam A.V, Padmadas Sundaram, Sofia Rani Shaik Abstract - The growing complexity of urban ecosystems, fueled by rapid population expansion, is intensifying the challenges cities face in achieving sustainability, circularity, and resilience. This evolving landscape demands innovative management approaches that integrate technological advancements and resource optimization strategies to support resilient urban development. This research proposes a management-oriented framework for Artificial Intelligence enabled Industrial Symbiosis networks within smart cities, focusing on enhancing resource recovery and establishing closed-loop systems. By leveraging AI technologies of predictive analytics, dynamic matchmaking, and optimization algorithms, the framework aims to identify, coordinate, and scale symbiotic partnerships among industries, utilities, and municipal stakeholders. Rather than emphasizing the technical development of AI algorithms, the study addresses strategic governance models, stakeholder engagement mechanisms, and policy interventions that facilitate the effective integration of AI in managing industrial symbiosis networks. Drawing on principles of the circular economy and smart infrastructure governance, this research highlights how AI can drive adaptive and resilient resource flows within urban environments. By bridging technological innovation with strategic management practices, the study contributes to the evolving discourse on sustainable smart city development and offers actionable insights for policymakers, city planners, and infrastructure managers.
Authors - Mohar Banerjee Biswas, Srikant Das Abstract - Individuals display different levels of innovativeness towards technology and this innovativeness is a crucial personality trait to understand adoption behavior. Personal Innovativeness in Information Technology (PIIT) has emerged as a strong personality trait that influences the adoption behavior towards Information and Communication Technologies (ICT) in higher education. This research analyses the influence of PIIT on the perception of utility and simplicity of ICT by higher education students. The study extends the Technology Acceptance Model (TAM) by adding PIIT and analyses how it affects the perceived usefulness (PU), perceived ease of use (PEOU) and subsequent behavior intention (BI) towards ICT adoption. For the data collection, 493 sample data were obtained from university students using an online structured questionnaire and was processed using Structural Equation Modeling (SEM). The findings of the study reveal that PIIT significantly impacts the PU and PEOU, indicating that students who are more innovative will view ICT as beneficial to their academic task and will find it easy to use. PIIT also has an indirect influence on BI via the construct of PU and PEOU, emphasizing its mediating role. The findings propose that developing PIIT amongst learners can improve students' preparedness to adopt ICT in academics. The research has important implications for stakeholders in developing specialized interventions, training, and curriculum designs that nurture creative mindsets, thus enabling stronger integration of technology in learning.
Authors - Santanu Mandal, Srinija M, Sukruthi M Abstract - This study investigates the structural barriers hindering digital well-being among Generation Z (Gen Z) through an integrated lens of Self-Determination Theory (SDT) and Cognitive Load Theory (CLT). Using a cross-sectional survey conducted between June and July 2025, 126 valid responses were collected from Indian Gen Z participants, comprising students and early-career employees. Ten barriers—including lack of awareness, absence of digital literacy, mixed media messages, platform design addiction, and peer pressure—were examined for their interrelationships using Interpretive Structural Modelling (ISM) and MICMAC analysis. Results reveal that foundational barriers such as lack of awareness, absence of digital literacy, and mixed media messages act as high-driving factors, creating cascading effects on dependent barriers like inconsistent sleep patterns, low perceived control, and productivity guilt. From the SDT perspective, these drivers undermine autonomy and competence, while CLT explains how cognitive overload from addictive design features exacerbates maladaptive digital behaviours. The findings highlight the need for multi-stakeholder interventions—spanning policy, education, workplace culture, and ethical platform design—to address root causes and promote sustainable digital wellness. This research advances theoretical integration between SDT and CLT, offering a diagnostic framework for targeted, systemic interventions aligned with SDG 3 and SDG 12.
Authors - Rupsa Sarkar, Jaydev Mishra Abstract - Natural language processing (NLP) has several key applications, including sentiment analysis (SA). SA can bed escribed as a procedure that identifies the polarity of a sentence as well as its goal through analysis. SA is now the most active NLP research area. Aspect-based sentiment analysis, which is a subset of SA, is the process of examining a sentence's structure and determining its polarity. This field has been grown because now people feel free to share their thoughts, opinions, expressions. Internet, social media are now the massive resource of opinion assuming. In this paper, we have used Sem Eval 2014 task 4 dataset to focus on Aspect-based Sentiment Analysis (ABSA) problem. We have focused on previous work Based on Aspect-based Sentiment Analysis and Some work of Sentiment Analysis based on conversational data. We have used GPT2 for this task.
Authors - Mark Anthony C. Ochoa, Aloysius J. Aurelio, Stephan Kupsch Abstract - This sub-study evaluates the application of descriptive and inferential statistics in graduate research at DMMMSU-CGS. Findings show that while nominal and interval/ratio scales are often treated with appropriate measures (frequency counts and means), ordinal data is frequently misrepresented using the mean instead of median or mode. Such lapses compromise validity and highlight gaps in statistical literacy. This misuse underscores the need for statistical diagnostics prior to test selection and the inclusion of assumption checks in research guidelines. Moreover, analysis of appropriateness and accuracy revealed that while proper methods generally produce accurate results, some accuracy still arises from misapplied tools due to compensating factors. These findings stress the importance of enhanced statistical training and methodological rigor. . . .
Authors - Jordan Cardenas, Fabian Cardenas, Marcos Levano, Billy Peralta Abstract - In today’s digital economy, where personalization has become a cornerstone of effective marketing strategies, companies face the dual challenge of increasing advertising impact while safeguarding sensitive customer information. Despite the rapid progress of large language models (LLMs), existing commercial solutions often neglect the integration of synthetic data to reduce privacy risks and enhance adaptability, leaving organizations dependent on external providers. To address this gap, our work fine-tunes open-source LLMs (LlaMa2, Mistral, and Zephyr) with synthetic datasets generated via GPT, aiming to produce customized marketing emails tailored to demographic and behavioral features. This thesis demonstrates not only the feasibility but also the competitiveness of such models by evaluating outputs with standard metrics (BLEU, ROUGE) and human-like scoring through GPT-4, showing that open-source models can approximate the performance of proprietary alternatives at significantly lower cost. The results confirm that fine-tuned LLMs with synthetic data represent a viable solution for enterprises seeking efficiency, personalization, and internal control of data.
Authors - Rricky Tim Solomon Sison, Romary Reyes Lincod Abstract - Diversity management has become a strategic priority in the hospitality industry, particularly in regions marked by increasing demographic variation. This study investigates the perceived effects of diversity management on the work environment in Department of Tourism (DOT) accredited hotels in Western Pangasinan, Philippines. It also examines the relationship between administrative diversity management application and employees’ experiential perceptions. This quantitative-descriptive research employed an adapted and expert validated instrument, refined through pilot testing. Using purposive and stratified sampling, data were collected from 53 participants (10 supervisors/managers and 43 employees) across five DOT-accredited hotels. Four sub-variables were examined: employee engagement, creativity and innovation, stereotyping and bias, and employee retention. Analytical tools included mean, average weighted mean (AWM), one-way ANOVA, and Pearson correlation. The study revealed that diversity management was perceived to be highly effective, particularly in fostering stronger employee engagement and enhancing creativity in the work-place. Overall, its impact on the organizational environment was regarded as very positive. Nevertheless, the findings also indicated that administrative strategies showed a weak and statistically non-significant relationship with employee perceptions, suggesting that structural approaches alone may not be sufficient to shape employees’ views. Among the demographic factors considered, marital status emerged as the only variable with a meaningful influence, highlighting its relevance in understanding how diversity initiatives are experienced within the hospitality context. The study suggests a disconnect between diversity policies and their perceived impact, highlighting the need for inclusive implementation. Limitations include the small, localized sample. The findings emphasize the im-portance of participatory diversity planning, inclusive leadership training, and institutional mechanisms to ensure meaningful and sustainable diversity out-comes in hotel organizations.
Authors - Sebastian Carrasco, Pablo Schwarzenberg, Marcos Levano, Carla Taramasco, Billy Peralta Abstract - In an era where rapid, accurate keyboarding underpins virtually every academic, professional and everyday digital interaction, conventional drill-based tutors still struggle to sustain user engagement and to adapt difficulty in real time. Addressing this gap, we present Terra INVicta, a browser-native serious game that defends Earth from procedurally generated “cosmic” threats only when the player types their associated words correctly, thereby combining cognitive processing and motor skills practice in an engaging and accesible way. This paper details the game’s design workflow and a first-round evaluation of its gameplay mechanics and adaptive-difficulty engine, which modulates challenge through a mixed time–score progression factor. Initial tests indicate that Terra INVicta delivers fluid animation, near-instant keystroke-to-action responsiveness, and sustains a “flow” state across diverse player abilities, confirming its viability as a cognitive training game. Beyond validating its educational value, we highlight lessons learned and propose future work—including their application as a tool for cognitive assessment and improvement of cognitive and motor-skills in older adults. An interactive prototype is available at https://inverosimilitudes.github.io/ TerraINVicta/.
Authors - Sanjeeb Prasad Panday, Ravi Gautam, Basanta Joshi, Aman Shakya, Anunaya Pandey Abstract - The proposed approach focuses on autonomous UAV (Unmanned Aerial Vehicle) navigation and obstacle detection using only visual sensors specifically, the on-board front camera. Unlike traditional methods that rely on multiple sensors (e.g., LiDAR, radar, or GPS), this vision-based system aims to reduce hardware complexity and cost while maintaining robust performance in diverse environments. By leveraging computer vision and deep learning techniques, the UAV processes real-time camera feed data to detect obstacles, map surroundings, and plan collision-free paths. Key challenges include handling dynamic environments, varying lighting conditions, and real-time processing constraints. The system employs feature extraction, depth estimation, and semantic segmentation to interpret visual data, enabling the UAV to navigate autonomously without external aids. Advantages of this approach include reduced sensor dependency, lower power consumption, and improved adaptability in GPS denied or cluttered spaces (e.g., indoor settings or dense urban areas). However, limitations may arise in low-visibility conditions (e.g., fog or darkness) or with texture-less surfaces that complicate depth perception. The method aligns with advancements in lightweight AI models optimized for edge computing, ensuring efficient onboard processing. Future enhancements could integrate multi-camera setups or hybrid sensor fusion for increased reliability. Overall, this vision only navigation strategy offers a scalable and cost-effective solution for UAV autonomy, particularly in applications like surveillance, inspection, and disaster response where simplicity and agility are critical.
Authors - Smita Kalokar, Ritesh Sule, Dinesh Mirkute Abstract - Demographic dividend is an economic theory that suggests a potential for growth when a population has a higher ratio of working-age people. This concept highlights the economic advantages that can arise from shifts in a society's age structure. In India, there has been a growing trend of dual-career couples, where both partners are employed. This phenomenon has emerged as a response to escalating inflation and elevated living standards, necessitating both partners to work concurrently. While this arrangement contributes to financial stability and social standing, it also presents numerous challenges. These challenges encompass psychological strain on the couples, difficulties in child-rearing, struggles with work-life balance, inadequate support from organizations and families when required, insufficient spousal aid in home responsibilities and career development, and disruptions in personal life. Women, who often shoulder the primary responsibility for childcare and family matters, face additional pressure when entering the workforce as they must juggle job duties with familial obligations. The research seeks to investigate the challenges encountered by dual-career couples and their strategies for managing the demands of work and personal responsibilities. The paper examines these issues and suggests various strategies at both individual and organizational levels to address and manage these concerns. This study utilizes a descriptive methodology, drawing upon an analysis of existing scholarly literature and direct observations in the field
Authors - Rivu Bhattacharjee, Ramisa Nawar, Farzana Sadia, Mahady Hasan, Rifat Ara Rouf, M. Rokonuzzaman Abstract - The core idea of DevOps is to integrate development and operations teams to improve software delivery, yet existing Critical Success Factor (CSF) frameworks remain largely untested in emerging economies where resource constraints exist and organizational cultures differ substantially. This study seeks to quantitatively validate and refine existing CSF models within the developing countries’ software industries. A cross-sectional survey of 23 DevOps professionals from leading software firms mapped 19 prior CSFs against perceived project success. Multiple linear regression analysis evaluated model explanatory strength, checked multicollinearity via Variance Inflation Factors (VIFs), and applied two-tailed t-tests to identify predictive factors. The regression model explained 95.6% of the variance in DevOps project success, identifying five key CSFs: regular DevOps events, collaborative team culture, complex performance engineering integration, build automation, and CI/CD pipe-line challenge mitigation. Regular DevOps events, collaborative culture, and streamlined automation emerged as primary success drivers. Results confirm core CSF frameworks while providing empirically grounded recommendations for resource-constrained contexts. Future research should employ larger multi-regional samples and qualitative methods.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho, Ulises Edison Ruiz Mendoza Abstract - The main objective of this study was to create a Geomechanical Zoning Map for a polygon located in the northwestern area of Loja, based on the soil bearing capacity derived from lithological and topographic surveys, at a 1:6000 scale. The lithological map identified six zones with different soil classifications. DCP tests were conducted at six demonstration points, including one comparison point, to evaluate the bearing capacity. Results showed varying soil strengths, with the highest CBR value of 79.36% in the center of the polygon (ML lithology) and the lowest CBR value of 18.1% in the upper left margin (OH lithology). Overall, the soils ranged from good to acceptable in bearing capacity. It was also noted that approximately 70% of the polygon area is already urbanized.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho Abstract - This study presents an integrated geotechnical analysis to evaluate the slope stability of a critical sector located along the lateral road of Ángel Felicisimo Rojas, near the Punzara lagoon in Loja, Ecuador. The site, characterized by steep slopes and weak lithological units such as silty sands, sandy clays, and weathered conglomerates, was identified as highly susceptible to landslides. A comprehensive methodological framework was employed, combining field surveys, geophysical profiling using the dipole-dipole method, laboratory soil classification, and shear strength testing. Mechanical properties such as cohesion and internal friction angle were determined and used to model slope stability under static and pseudo-static conditions using GEO5 software. The initial safety factor was found to be below recommended limits, indicating severe geotechnical risk. Through the evaluation of stratigraphic profiles and failure modes, the study out-lines the potential failure mechanisms and proposes slope reinforcement strategies to mitigate hazards. This research underscores the importance of integrating geological, geomorphological, and geotechnical data to support informed engineering decisions in landslide-prone Andean environments.
Authors - Grant Royd Howard Abstract - Oversight of autonomous organizational systems (AOS) decision making is imperative for the betterment of and the prevention of harm to humanity. Human decision making oversight literature is extensive across many disciplines, but little exists about AOS decision making oversight. This was the research problem addressed, guided by the question, what fundamental concepts are essential for AOS decision making oversight? The question was answered by developing an original conceptual framework. To this end, a conceptual research design was applied to answer the research question. Conceptual research is effective for generating new knowledge using intellectual tools, logical argumentation and assimilation of interdisciplinary empirical research. The framework exposed fundamental oversight concepts for overseeing decision making in AOS, namely governance framework, delegation and autonomy, human involvement, transparency, traceability and explainability, accountability, ethical and legal compliance, risk management, and feedback, adaptation and improvement. The framework is important for all AOS and artificial intelligence (AI) scientists, academics, programmers and organizations for averting vulnerabilities, threats, failures, stagnant models, opaque decisions, auditing difficulties, undetected biases, responsibility vacuums, unclear liability, unethical behavior, ethical violations, erosion of trust, business continuity risk, legal and regulatory non‑compliance, and missed learning opportunities. Furthermore, the framework advances AOS and AI oversight and governance research, establishing fundamental concepts for theory development and knowledge progression in the domain of AOS decision making oversight.
Authors - Shiwani Wagh, Shubham Kishor Kadam, Utkarsha Wanjari Abstract - The coupling of quantum computing and behavioural analytics signals broader opportunities for human resource management (HRM) and, in particular, talent management. In an age where we are using massive, multidimensional data sets simply for workforce planning and retention strategies, traditional HR approaches falter too frequently beneath the weight of such complexity. Moreover, that brings us to the flip side of the coin: quantum computing, the trading depth of behavioural analytics, for qubits for data processing power. This research explores how these technologies con-fluence to provide new solutions for Recruitment, Employee retention, Workforce development, Organisation Planning, etc. By combining predictive analytics and personalized interventions to make treatment decisions, quantum-enhanced behavioural analytics can support a targeted approach to behavioural analytics. Additional applications include pipeline optimization, attrition risk prediction, and alignment of development programs with business goals. On the other hand, while we have these new frontiers, we also have technical limitations, ethical concerns about data privacy, and the potential for biases in decision-making. To address these, they need to be driven by robust governance, ethical frameworks and upskilling of our HR professionals. The potential synergy with this is brought out in this paper, with a recommendation for its ethically implemented use in HR practices. The next frontier in HRM includes quantum computing and behavioural analytics, which will aid in the bolstering of workforce engagement and organizational success through data-driven, human-centred strategies.
Authors - Priyanka Patel, Priyanshi Desai Abstract - This research examines the relationship between Diversity, Equity, and Inclusion (DEI) strategies and organizational performance across global business environments. Through extensive analysis of empirical studies, international corporate case examples, and industry best practices, this study demonstrates that effectively implemented DEI initiatives contribute significantly to enhanced organizational outcomes, including increased innovation, improved financial performance, higher employee engagement, and expanded market reach. The paper addresses implementation challenges with particular focus on leadership accountability frameworks and strategies to prevent diversity fatigue while main- training long-term employee engagement. This analysis provides valuable insights for business leaders seeking to leverage DEI as a strategic advantage while navigating cultural and regulatory differences across inter- national markets.
Authors - Anurag Agarwal, Mahesh Jangid, Prashant Vats Abstract - Breast cancer starts as a problem with breast cells that multiply too fast, forming growths called lumps. Breast cancer commonly appears in milk ducts before advancing to other body parts, if found too late. The sooner doctors find and diagnose breast cancer, the better patients respond to treatment and survive longer. Better breast cancer detection comes from using large collections of imaging data through AI technology, especially machine learning and deep learning. With transfer learning tools and hybrid networks combined with CNNs sophisticated systems achieve strong results in breast cancer evaluation and characterization. Through their use of artificial intelligence, medical researchers are developing better diagnostic tools and creating wider accessible healthcare systems for worldwide needs.
Authors - Rangith B. Kuriakose Abstract - Learning is a complex process shaped by psychological and social factors. Psychological theories examine mental processes like memory and problem-solving, while sociological theories analyze how culture and societal structures influence education. Key psychological perspectives include behaviorism, which emphasizes reinforcement, and cognitivism focusing on knowledge construction and active learning. Vygotsky’s constructivism highlights social inter-action and the Zone of Proximal Development (ZPD), where guidance enhances learning. Sociologically, functionalism (Parsons) sees education as promoting social cohesion, whereas conflict theory (Marx, Freire) critiques its role in perpetuating inequality. Freire’s Pedagogy of the Oppressed advocates for empowering, reflective education. This paper explores cognitivism’s application in higher education, using an Electrical Engineering module as a case study. It identifies learning challenges ("learning knots") and proposes chunking, a cognitivist strategy, as an intervention. A comparative study (control vs. test groups over five tutorials) evaluates the method’s effectiveness in fostering meaningful, sustained learning. The paper bridges theory and practice, offering insights into curriculum design and findings suggest structured knowledge segmentation improves comprehension, demonstrating cognitivism’s practical relevance in technical education.
Authors - Mageshwari A. J., Jothi Prabha R., Karthiga N., Lekshmi R. R. Abstract - Ensuring the quality of printed circuit boards is crucial for the reliability of electronic devices. Traditional manual inspection methods are laborintensive and prone to errors, underscoring the need for automated solutions. This study presents an automated deep learning-based approach for printed circuit board inspection and defect detection by leveraging segmentation and object detection models. The investigation focuses on the performance of the SAM and an integrated YOLO-SAM pipeline. A high-resolution dataset com-prising PCB images annotated with six common defect types—missing hole, mouse bite, open circuit, short, spur, and spurious copper—is collected and preprocessed for training and evaluation. Initial experiments with SAM reveal limitations in segmenting defects without guided prompts, particularly board with missing holes. To address this, YOLO is integrated with SAM to provide localized bounding box prompts, enhancing segmentation accuracy. The YOLO-SAM model is trained for 100 epochs and evaluated using precision, recall, F1-score, mean average precision, Intersection over Union, and Dice score. Results demonstrate that the YOLO-SAM pipeline significantly outperforms standalone SAM, achieving more accurate and robust detection across multiple folds. This two-stage approach offers a reliable solution for automated, high-precision printed circuit boards’ defect analysis.
Authors - Ronald C. Barriga, Dan Jeward Rubis, Arlene A. San Pablo, Rochelle Joy S. Tagle, Helinor Y. Medina Abstract - This study investigates the influence of AI-enabled self-service food kiosks on customer satisfaction and their intention to use such technology in quick service restaurants. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the research examines key factors such as convenience, efficiency, security, and enjoyment that mediate customer perceptions. Findings indicate that positive experiences with AI kiosks significantly enhance customer satisfaction, suggesting that strategic implementation of AI technologies can lead to improved operational efficiency and customer engagement in the food service industry.
Authors - Michael Savariapitchai, Priyanka S. Dhore Abstract - This study investigates the levels of burnout among healthcare professionals, its impact on job performance, and its relationship with organizational sustainability. The research employs a descriptive and correlational design, utilizing a sample of 101 healthcare workers from various roles and organizations. A structured questionnaire with Likert-scale questions was used to collect data, which was then analyzed using descriptive and inferential statistics, including correlation analysis. The findings reveal moderate levels of burnout across dimensions such as emotional exhaustion, physical fatigue, and motivation. Burnout significantly affects job performance, including quality of patient care, concentration, efficiency, and job satisfaction. Furthermore, burnout is perceived to contribute to organizational challenges, including employee turnover and decreased organizational sustainability. Although there is some recognition of burnout’s impact, respondents indicated that organizational support systems are inadequate in addressing these issues. The study underscores the need for comprehensive burnout prevention strategies, including improved workload management, employee well-being programs, and enhanced support systems to promote both individual and organizational health. Recommendations include enhancing leadership initiatives, fostering work-life balance, and strengthening mental health support within healthcare organizations.
Authors - Moksha Patel, Anuradha Desai, Happy Patel Abstract - Despite advancements in health care, cervical cancer is still one of the most prevalent causes of death amongst women globally. This highlights the importance of early diagnosis, which significantly improves the chances of positive treatment outcomes. Traditionally, the Pap sample screening has been used; however, its interpretation is human-dependent, which causes variability and prospective delays in patient treatment. To address these issues, this study offers a model called CerviScan, which utilizes Convolutional Neural Net-works, specifically InceptionV3, to automatically multiclass cervical cell image classification. The SIPaKMeD dataset, containing 5 different types of cervical cell images, served as the training dataset for our model. Through transfer learning and extensive augmentation, we sought to improve generalization capability. As a result, CerviScan was able to surpass a classification threshold of 96.8%, exhibiting remarkable accuracy and recall for all cell type categories. Concerning this medical imaging problem, InceptionV3, along with fine-tuning techniques, has deeply enhanced classification performance owing to its deep feature extraction capabilities. This system is highly accurate, cost-effective, and efficient, which offers remarkable assistance in eliminating repetitive tasks for pathologists and thereby accelerating the diagnosis process. Adapting deep learning methodologies to enhance the early detection of cervical cancer, as discussed in CerviScan, demonstrates the potential it has to improve cervix cancer detection and healthcare equality on a global scale.
Authors - Bhargav Dhengre, Reena Satpute Abstract - Lightning Web Components (LWC) is a new, ambient framework for creating dynamic and responsive user interfaces for the Salesforce ecosystem. LWC is built on web standards and uses these standards to enhance user experience (UX) through improved performance, customizability, accessibility, and integration. This report outlines the areas in which LWC enhances UX through faster rendering (through Virtual DOM, lazy loading), reusable and themeable components, and ARIA support/semantic HTML for accessibility. In addition to accessibility, LWC is built for seamless integration with Salesforce data and third-party libraries, as well as other developer-friendly tools that speed up application development while being consistent and scalable. Use cases for LWC have already shown tremendous improvements in existing applications, enhanced e-commerce platforms, enhanced load speeds, packaging checkout information to streamline checkout, and responsiveness for all mobile devices. The modularity or components in LWC can help decrease the effort to maintain components while increasing engagement, session creation, and conversion rates. The future of LWC has great potential in terms of AI integration, more and more component libraries, and added compatibility with cross-platform applications in the Salesforce ecosystem. LWC is likely to become a key technology for Salesforce development. Organizations that adopt LWC are likely to provide users with a fast and intuitive application to satisfy users and help organizations operate more efficiently in their business operations. For that reason, Salesforce is and will continue to be an elite leader in the development of enterprise CRM technologies.
Authors - Rochma Sudiati, Ayatulloh Michael Musyaffi, Eka Ary Wibawa, Dinda Rahma Tiara, Putri Haryani, Ellis Annisa, Hera Khairunnisa, Eka Septariana Puspa, Surya Anugrah Abstract - Digital transformation in tax administration, particularly through the implementation of the Core Tax Administration System (CTAS), has brought significant changes in the way taxes are managed and reported in Indonesia. This study points to assess the viability of using CTAS in learning tax collection for bookkeeping understudies. The most center of this study is to analyze students' state of mind towards advanced system-based learning innovation and survey their level of fulfillment after utilizing CTAS. The inquire about strategy utilized is pre-experimental with a one gather pre-test and post-test plan, where understudies take after a recreation of utilizing CTAS to report charges carefully. Information were collected through multiple-choice questions and a Likert scale-based survey to degree students' understanding, attitude, and fulfillment with the utilize of the framework. The comes about appeared that in spite of the fact that there was a slight decrease in positive attitude towards advanced learning, students' fulfillment level expanded altogether after taking part within the recreation utilizing CTAS. This consider concludes that although innovation encompasses a positive impact on understudy learning fulfillment, its impact on learning demeanor isn't exceptionally noteworthy. Therefore, there's a require for change in learning strategies to extend students engagement with charge innovation, as well as guaranteeing that advanced learning can be more effective in making strides students' understanding and aptitudes in tax assessment. This inquire gives understanding into the significance of innovation integration in taxation instruction to form graduates who are ready to confront challenges in an progressively digitized world of work.
Authors - Harsh Chauhan, Henrijs Kalkis Abstract - To analyze the process of internal competition within organization’s structure, the concentration is on employee’s perspective of internal social comparison followed by intra-organizational competition developing between subordinates. The organization’s perspective of internal competition is subjected to intra-organizational evolution for developing competitiveness in business operations. For evaluation of internal competition in organization a comprehensive literature review has been developed in 2 parts. Part 1: “Intra-organizational competition”: Intra-organizational competition is the resultant of Internal social comparison. Not all employees are competition ready. Employees benchmark their performance and contribution against each other. They compare remuneration and work behavior. Part 2: “Intra-organizational evolution”: Based on ‘Variation’ associated with decentralization of command, ‘Selection’ in terms of allocating scares resources and ‘Retention’ depending on managerial ability for implementing organizational strategy for exhibiting internal competition. Scientific database such as Google Scholar, Emerald Insight, EBSCO host and Science Direct has been researched. 37 research papers includes internal social comparison and intra-organizational competition, similarly 23 research papers are selected for intra-organizational evolution. Methodology of selecting papers is based on PRISMA 2020. The literature review emphasize on treating co-workers as competitors, however organizations management focuses on increasing productivity by developing internal competition. A conceptual framework of internal competition has been developed. The level of competition and control between employees and business management derives the span and scale of internal competition within the organization’s boundaries.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Camila Nickole Fernandez Morocho, Tyrone Alexander Guarderas Cabrera Abstract - This study investigates the ability of vetiver grass (Chrysopogon zizanioides) to improve the geomechanical characteristics of fine soils in the province of Loja, Ecuador. Given the problem of erosion and slope instability in the region, the effect of the vetiver root system on soil shear resistance was experimentally evaluated. Undisturbed soil samples were extracted, both with the presence of vetiver and control (without vetiver), at depths between 0.80 and 1.5 meters. Laboratory tests included physical characterization of the soil and geomechanical tests such as the undrained consolidated triaxial test (CU) and the direct shear test (CD). The results obtained demonstrate a notable increase in the apparent cohesion and shear resistance of the samples with vetiver roots compared to the control samples. The presence of vetiver markedly enhanced the soil's shear resistance capacity, reflected in a considerably greater apparent cohesion in the reinforced samples. These findings validate vetiver grass as an effective and sustainable bioengineering solution for slope stabilization and mitigation of erosive processes in soils with characteristics similar to those studied.
Authors - Sunil Sangve, Prathmesh Kalaskar, Yash Kathoke, Tejas Joshi, Amartya Khandare, Anish Kadu Abstract - Elderly individuals and those with chronic illnesses often struggle to stick to their medication schedules, especially when they do not have someone around to help. To address this challenge, this paper introduces an IoT-based medicine dispenser designed to automatically dispense medication at specific times of the day—morning, noon, and night. The system is built using an Arduino microcontroller, servo motors, LEDs, an LCD display, and a buzzer to provide clear visual and audio reminders. The design of the medicine dispenser facilitates easy management of medication regimens. A basic cardboard container holds three compartments designed for medication at different times of the day. The servo motor operates the dispensing system to deliver medications at the correct times. Through a app users and caregivers can effortlessly manage medication schedules which enhances medication adherence and leads to better health results.
Authors - Happy Patel, Anuradha Desai, Moksha Patel Abstract - Breast cancer can be considered one of the fatal disorders distinguished by unusual and uncontrolled development of breast cells, which requires early and precise detection in order to be effectively treated. Ultrasound image is frequently used for breast cancer screening as it is widely available and non- invasive. Diagnostic difficulties may arise from the subjective and variable manual interpretation of ultrasound pictures. In this work, we suggest a Deep Learning Model utilizing EfficientNetB4 for automated classification of ultrasound images of breast cancer. There are 647 images in the collection that have been categorized as either benign or malignant. Binary masking, histogram equalization, and grayscale conversion are some of the steps to improve feature extraction. The proposed model outperforms traditional CNN architectures achieving an accuracy of 90.24% on the testing dataset after being trained with a transfer learning approach. The model indicates exceptional sensitivity in identifying malignant cases, decreased the rate of false-negative outcomes, and increased diagnosis accuracy. To ensure efficient model training, accurate iterative improvement, and consistent performance improvement, learning rate scheduling and check pointing are used. The experimental results demonstrate how well EfficientNetB4 performs feature extraction and classification, making it a potentially useful tool to help radiologists diagnose breast cancer. By providing a better approach for identifying and categorizing breast cancer, this research promotes the use of deep learning in the field of medical imaging.
Authors - Prince Kelvin Owusu, Philomina Pomaah Ofori, Moses Aggor Ofori, Dzordzoe Koffie-Ocloo, Gibson Afriyie Owusu, Martins Larweh Nuertey Abstract - The Internet of Everything (IoE) extends traditional IoT by integrating people, processes, data, and things into a highly dynamic and context-sensitive ecosystem. This convergence introduces complex security, privacy, and trust challenges that cannot be effectively addressed using static or device-centric models. In this paper, we propose CAT-M, a novel Context-Aware Trust Management framework that integrates dynamic trust evaluation, semantic policy enforcement, and context-sensitive privacy controls to secure heterogeneous IoE environments. CAT-M leverages fuzzy logic, semantic translation, and lightweight cryptography to ensure scalable, interoperable, and human-centric security. Through simulation in a smart healthcare scenario, we demonstrate the framework’s effectiveness in reducing privacy leakage, improving trust accuracy, and enabling real-time access control. The results highlight CATM’s potential as a unified approach to building secure and trustworthy IoE systems, while paving the way for future enhancements through intelligent trust prediction and cross-domain interoperability.
Authors - Samiksha Andeo, Supriya Narad Abstract - How we've been interacting with technology has altered with the presence of chatbots, which have now become a part of our digital society. These artificially intelligent virtual personal assistants are being applied on a vast scale in customer services, health care, and education sectors since they are capable of answering the customers, addressing their queries, and directing them in real-time. In relation to their application in healthcare, in this research study, the his-tory of chatbots is discussed, the technology employed by them, their real-world applications, and the problems faced by them. Modern-day chatbots can understand human language, learn from past conversations, and communicate in a customized way by employing Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI). They may be anything from AI-based chatbots able to engage in meaningful, dynamic conversations to rule-based assistants that utilize pre-defined scripts. With the advent of technology, chatbots are increasingly being applied in the field of health care such as patient assistance, mental assistance, and diet and nutrition guidance, all of which enhance access to health care services. However, despite all their benefits, their potential is also hindered by limitations such as privacy, ethical concerns, and the inability to understand human feelings. As the future of deep learning, IoT connectivity, and more interactive AI is on the horizon, chatbots appear to have a very bright future. The purpose of this research is to give an authoritative and illuminating perspective on how chatbots are changing various businesses and the future of AI communication.
Authors - Sadman Hafiz, Md. Ataur Rahman, Tahmid Zamee, Md Sacklain Hossain, Marufa Akter, Ahmad Mostofa Kamal, Mahady Hasan, M. Rokonuzzaman, Farzana Sadia Abstract - The organic agriculture sector in Bangladesh faces significant challenges due to the absence of a national organic product certification system. As a result, local producers are often forced to rely on expensive international certification agencies, creating barriers to market entry, increasing costs and limiting consumer trust. To solve this, our proposed system is a blockchain-enabled, AI-powered web platform designed to establish a secure, transparent, and efficient organic product certification system tailored for the Bangladeshi context. A key innovation is its hash-based file verification to minimize gas fees to store data on the chain. Farmers or producers submitted product data like images, videos, and documents, which are encrypted (AES-256) and stored locally, while cryptographic hashes (SHA-256) are saved on-chain. Any alteration in a file changes its hash, and QR-code scanning will instantly flag tampering, ensuring product integrity. It also integrates tokenized payment systems using ERC-20 tokens alongside SSLCOMMERZ payment gateway, ensuring secure, traceable, and accessible transactions for all stakeholders. Key features include smart contract automation, real-time certification tracking, a decentralized dispute resolution mechanism, and QR code-based traceability for consumers. This project, therefore, aims to democratize organic product certification, lower operational costs, and build consumer trust, while aligning with global trends in digital food supply chains.
Authors - Wani Tejaswi, Pawar Suvarna Abstract - The number and intensity of car accidents around the world are rising, which makes it even more important to have smart systems that can quickly and correctly process accident pictures. We need more advanced systems to quickly find accidents, label data, and record them. Manual methods take a long time, are prone to mistakes, and are not good enough for real-time apps. This paper tries to solve these problems by suggesting a system that uses advanced machine learning (ML) models to automatically process, label, and report images of car accidents. Convolutional Neural Networks (CNNs) are used for feature extraction, YOLOv8 is used for real-time accident recognition, and Transformer-based models are used for complex multi-object labeling Global Road Accidents Dataset is much better at generalizing models than the Car Crash Dataset and the Road Traffic Accidents Dataset, which are more focused on crash types and vehicle damage. This is because it has more images and a wider range of features, such as location, severity, and weather. It was tested and found that the proposed hybrid model is more accurate than the CNN- based and YOLO-only methods, which got 94.5% and 93.7% accuracy and precision, re-spectively, on the Global Road Accidents Dataset. The combination model also has better accuracy for annotations and fewer fake hits. Comparative research shows that combining Transformer designs with object recognition models makes it easier to understand features and make reports. Findings from this study show that the AI-driven framework can be used to automate accident investigation processes. This is a big step towards smart traffic control and emergency response systems.
Authors - Hasin Mahir, Tahfizul Hasan Zihan, Md. Shirazim Munir, Khondkar Ayaz Rabbani, Rifat Ara Rouf, Ferdows Zahid, Mahady Hasan, Md. Tarek Habib Abstract - Indoor air quality (IAQ) is critical for health, comfort, and cognitive performance in classrooms. Yet, many classrooms in lowermiddle-income countries (e.g., Bangladesh) rely on natural ventilation and lack any warning system for impending air-quality guideline breaches. This study is motivated by the need to maintain healthy learning environments in resource-constrained settings, where students and teachers are often exposed to elevated CO2 and particulate levels. To address this, we present the first comparative evaluation of three forecasting models: Prophet, Random Forest (RF), and Long Short-Term Memory (LSTM), on daily PM2.5 and CO2 time series covering six months of continuous operation in Dhaka, Bangladesh. Our goal is to anticipate IAQ deterioration to enable proactive ventilation or filtration interventions. The nonlinear machine-learning models substantially outperform the Prophet baseline. Random Forest performed best for PM. (RMSE 1.87 μg/m³, R² 0.992), showing its ability to capture complex pollutant dynamics. LSTM excelled at forecasting peak CO2 (RMSE 159.7 ppm, R² 0.693), which is critical for timely interventions. These findings demonstrate the feasibility of accurate, low-cost IAQ forecasting in resource-constrained classrooms and underscore the potential of data-driven forecasting to maintain healthier learning environments.
Authors - Thuy-Vi Thi Ha, Phuoc-Hung Vo, Thanh-Nghi Do Abstract - In this paper, we propose an enhanced deep learning approach for early-stage rice yield prediction by embedding self-attention layers into established deep neural networks (DNNs) such as VGG-16, DenseNet, MobileNet, ResNet, Inception, and Xception. The addition of self-attention significantly improves the models’ ability to capture longrange dependencies and global context, which traditional convolutional layers often fail to represent adequately due to their inherently local receptive fields. We evaluate the proposed method on a dataset of 18,642 RGB images collected from 47 rice field plots spanning over 28 hectares in An Giang and Tra Vinh provinces. The images were acquired using digital cameras, smartphones, and fixed-wing UAVs during the heading stage of rice growth. Experimental results demonstrate that DNNs enhanced with self-attention layers consistently outperform their original fine-tuned counterparts. Furthermore, these hybrid models also achieve higher prediction accuracy compared to Vision Transformers (ViT), highlighting the effectiveness of integrating self-attention into conventional DNN architectures for agricultural yield forecasting.
Authors - Soham Paithankar, Khushi Khedkar, Supriya Narad Abstract - In this research paper, there is a new forecast on the programme that will be able to predict the weather for agriculture. Enhanced by the Internet-connected weather and climate models, earth observation data, and artificial intelligence algorithms, the system returns to farmers accurate, location-based outlook. It does this by factoring preceding day’s temperature data and other factors to determine the needed plant growth, therefore offering comprehensive and precise directions. Furthermore, the alarm systems help in giving the farmers a prior indication of any future possible weather-related incidents that cause a threat to human and their stock’s safety so that measures could be taken accordingly. It also uses real information especially in agriculture sector thus transmits present information to the farmers. The main objective that the system has for farmers is to provide them with information on crops during a favourable time for crop production and the main idea of achieving this is to boost agriculture sustainability and productivity, this is because climatic conditions are very volatile thus would have a great negative impact on farming. Also, the expected audience becomes more diverse and can principally grow, as we would like to consider farmers, who may not even have a basic computer knowledge, empowered by the platform.
Authors - Ankit Shah, Roshni Rawal Abstract - Artificial intelligence-powered learning analytics (LA) shows great results for assisting teachers with the time-consuming task of providing feedback in context to ethical consideration, leads to fairness, maintaining privacy and transparency in feedback. The study investigated two GPT model versions (3.5 and 4) that produce evaluation advice on students' writing assessment based on assignments in context of data science subject. Researchers evaluated GPT generated advice in comparison with human instructions on the basics of effective-ness, readability, and reliability. The paper concludes that both versions could constantly produce more decipherable advice in comparison with human instructions, but GPT-4 performed much better than GPT 3.5 and human instructors. Researchers talked about the unique blend of ethical contemplation and the impact of LLM’s on automated feedback and data analysis provides a perfect match to the LLMs to achieve ethical horizons and set a new height to be achieved for upcoming generations.
Authors - Shalini S, Mamatha A, S. Sheela, Mala B A, Nagaraj M Lutimath, Koustav Biswas Abstract - Sustainable soil management is essential for ensuring food quality, environmental health and climate resilience. This systematic survey examines the evolution and integration of Artificial Intelligence (AI) and Machine Learning (ML) within green agricultural technologies aimed at enhancing soil health. This paper focuses on sensor networks, remote sensing, Internet of Things (IoT), robotics and data-driven decision-support systems to assess their efficiency in precision soil diagnostics, fertility management and resource conservation. While these green AI technologies show promising benefits, improved resource efficiency, reduced environmental footprint, regenerative soil practices challenges persist, including uneven soil data availability, high implementation costs and limited interpretability of models and lack of standardization.
Authors - Aida Ouedraogo/Rakissga, P Justin Kouraogo Abstract - We're interested of the control plane's fault tolerance and the solution we're proposing is proactive thanks to its modularity. Our solution proposes a distributed SDN (Software-Defined Networking) architecture, comprising a controller specifically dedicated to processing packet_in messages and a generic controller in charge of other network management functions. The goal is to prove the effectiveness of this role specialization with regard to performance in message processing and the ability to resist failures.
Authors - Shyamali Thasale, Seema Kedar, Rutuja Khedkar, Kartik Naphade, Prajakta Gaikwad, Vijay Kale Abstract - Sentiment analysis of tourist reviews examines common points of view to help users choose destinations, accommodations, and services. Provides businesses with information on customer satisfaction and helps travelers make better choices based on a variety of common experiences. The proposed method uses a mix of the Skip-Gram models and Continuous Bag of Words in addition to Word2Vec vectorization to transform textual data into numerical values. This method maintains the sentiment of the text and also captures the context and word relationships. Convolutional neural networks boost the accuracy of classification and feature extraction. Along with CNN, models like Random Forest, AdaBoost, LSTM and CNN-BiLSTM have also been used in order to hold a comparison between them. Among these, the CNN model showed the highest accuracy of 96.3 %. This system provides users with specific recommendations for vacation spots, accommodations, and activities.
Authors - Vedansh Verdia, Upesh Patel, Trushit Upadhyaya Abstract - In this paper, we take a look into the world of communication protocols from the ones we've relied on for years like the Ethernet, to newer technologies made just for the Internet of Things (IoT), and even those that are just starting to emerge. As our need for faster, more reliable, secure, and energy-efficient communication keeps growing, these protocols have continued to evolve. We look at how they work, what they're built on, and how well they perform in real-world situations. Along the way, we break down their strengths, their drawbacks, and where they’re best used. Our review spans a broad spectrum—from classic wired systems to wireless options like Wi-Fi and cellular networks, as well as industrial communication setups and cutting-edge technologies like Time-Sensitive Networking. We break down how these protocols compare in terms of speed, ease of implementation, and security, helping readers understand which ones are best suited for different needs and situations. Finally, we talk about the big questions researchers are still trying to answer, and where things might be headed especially when it comes to making systems smarter, more unified, and ready for future challenges like quantum-safe communication. This paper is meant to be a helpful guide for anyone researchers, engineers, or tech enthusiasts—trying to make sense of today’s complex communication landscape.
Authors - Vikas Sihag, Kanika Panwar Abstract - Predictive analytics is reshaping crime mapping and policing by leveraging tools like GIS, machine learning, and big data. This paper reviews current techniques and real-world applications, highlighting their potential to identify crime hotspots and inform proactive strategies. It also examines key challenges, including biased data, ethical concerns, and privacy risks. Through case studies analysis and literature review, the research outlines the limitations of existing systems. The study stresses the importance of fairness, transparency, and accountability. Furthermore, the paper presents a current state of challenges and solutions advocated in research, thus supporting responsible and effective crime prevention practices.
Authors - Moses Moyo, Hanifa Abdullah Abstract - This scoping review was conducted to have an overview of how Artificial Intelligence (AI) influenced cybersecurity posture in South African small and medium enterprises (SMES) utilizing cloud computing technologies. The study extract 57 articles from six electronic databases. After rigorous screen, only eighteen studies were eligible for analysis. Of the eighteen remaining studies, eight were empirical and ten desktops. A qualitative thematic approach was used to extract relevant data from the study to understand the current state of cyberse-curity in SMEs. The study found that South African SMEs were using AI but did not harness opportunities it can offer in cybersecurity. The level of cybersecurity among the SMEs was very low due to lack of proper knowledge about threats that AI technologies could bring about if used by criminals. The study concluded SMEs were poorly prepared for AI use in general and in cybersecurity. The study was limited due to lack of literature related to the research questions. Therefore, it is recommended that a longitudinal empirical study be conducted with South African SMEs to have deeper understanding standing of how the adoption of AI is influencing their cybersecurity posture.
Authors - Aswathi Mary Varghese, Jisha K Abstract - Digital public infrastructure (DPI) denotes foundational digital systems that serve as essential enablers for delivering digital services at scale across society. It has emerged as a key enabler of innovation in developing countries by reducing systemic entry barriers and fostering an inclusive digital ecosystem. DPI serves as a vital connective tissue within the digital ecosystem. Bridging the gap between physical infrastructure—such as internet connectivity, devices, data centers, and the cloud—and a diverse landscape of applications, DPI empowers societies to deploy innovative solutions across sectors like e-commerce, social protection, re-mote education, and telehealth. Real-world illustrations abound: India’s Aadhaar system has revolutionized digital identification, Brazil’s Pix has made fast, real-time payments accessible nationwide, and Australia’s Consumer Data Right is pioneering user-centric data sharing. By enabling dynamic, cross-sectoral innovation, DPI stands as the engine powering the next generation of digital transformation. This paper analyses the evolution and impact of India Stack, focusing on how its layers enable innovation across sectors such as fintech, health, and e-commerce. By mapping the structural features of India Stack against the outcomes in financial inclusion, digital health, and market decentralization, the paper offers a conceptual framework to assess the role of public policy in driving innovation. The findings indicate that when government platforms are built as public goods with interoperable standards and strong institution-al backing, they can create an enabling environment for grassroots and enterprise-level innovation. The India Stack model, while context-specific, holds valuable lessons for other nations seeking to integrate public policy with digital transformation goals.
Authors - Mohammed Hashim Kalam, Guhanathan Poravi Abstract - Time-series data has been increasingly utilized in critical domains making it vulnerable to stealthy adversarial attacks. Explainable AI (XAI) has made major enhancements to model transparency specifically in non-sequential domains (Image, NLP and tabular) but is less researched in time series data due to complexity of temporal dependency in sequential data. This paper systematically reviews around 60 studies published up to June 2025, examining XAI-based approaches for time series, as well as recent XAI extensions tailored for time series data. Various attack types are discussed in terms of their effectiveness, and the performance of recent detection frameworks against these attacks is analyzed, with particular focus on XAI-integrated systems, including SHAP, LIME, LRP, and formal verifications. Despite their usage, most XAI-enhanced approaches, especially those relying on SHAP, face challenges in efficiency and risk explanation instability under noise, with limited evaluation against subtle partial adversarial attacks. While LRP offers computational efficiency vital for high stakes domain real-time detection, its architectural dependency restricts its adaptability across diverse models. This review highlights major unsolved limitations and concludes with prioritizing the need for efficient, robust, temporally aware, and architecture-agnostic frameworks, possibly extending LRP, tailored specifically for time series data.
Authors - Shalini Singh, Amala Siby Abstract - In the last twenty years, efforts have been made to address how financial literacy and inclusion can empower rural women entrepreneurs. However, one essential factor tends to be left behind—the contribution of incubation centers to enabling these women to succeed. This research examines the academic landscape in closer detail through a bibliometric analysis of 2,012 peer-reviewed articles between 2005 and mid-2025 based on the Scopus database. Using tools such as Biblioshiny and VOSviewer, we delve into how the discourse on financial capability, women entrepreneurship, and economic empowerment has progressed throughout time. The research indicates a meteoric increase in interest following 2020, fueled primarily by vulnerabilities uncovered with the COVID-19 pandemic. Despite expansion, however, something is certain: there remains relatively little research linking incubation assistance to rural women directly or financial literacy programs. Our analyses of keywords and co-authorships indicate a splintered discipline with ample space for richer, more holistic research. This article not only charts what is known but also reveals what is lacking, presenting new directions for researchers and policymakers dedicated to building stronger safety nets for rural women entrepreneurs.
Authors - Manupriya P, Rajeshwari.Mattimani, Haripriya G, Hema B, Saroja.V.Siddamal, Vijay H M, Suhas Shirol, Ganga I Abstract - At present, encryption is key to safeguard confidential information from unauthorized access. It means using special algorithms and encryption keys to change readable data into unreadable data. AES is symmetric encryption algorithm adopted by the U.S.Government as a replacement of DES algorithm. AES supports 3-key sizes: 128 bit, 192 bit and 256 bit, with AES -128 being the most commonly used due to its balance of security and performance. Because it is fast and simple, symmetric key encryption which uses a single key for both encryption and decryption, is used in many cases. Advanced Encryption Standard (AES) is a symmetric block cipher that is used extensively in the encryption of electronic data. Out of its various implementations, AES-128 is the most commonly used, with a 128-bit key size and encrypting 128-bit-sized blocks of data and to get 128-bit cipher text. The encryption process goes through 10 rounds of processes like SubBytes, ShiftRows, MixColumns, and AddRoundKey, which are computed to have high confusion and diffusion properties. AES-128 is optimally balanced in terms of security, efficiency, and performance and thus finding use in hardware and software in various industries. Due to its strength and immunity against known attacks on electronic data, passwords etc. AES-128 has been the standard for encrypting data in commercial and government systems worldwide. AES algorithm has many applications such as online banking, ATM machines and military and government communication.
Authors - Anirban Chowdhury, Parul Sachdeva Abstract - This study explores the product-market response to breastfeeding equipment by leveraging user data analytics to evaluate consumer behavior, preferences, and satisfaction. As maternal health awareness grows and parenting practices evolve, breastfeeding products have seen significant innovation in design, functionality, and accessibility. However, market success relies not only on product features but also on understanding real-world user experience and response. The research employs a quantitative method, from user ratings sales metrics to get insights from data collected from amazon.in. Findings reveal key determinants influencing user acceptance, including ergonomic design, portability, ease of use, and aesthetics. The study also identifies gaps between user expectations and product performance that affect repeat purchases despite of product brand. By contextualizing user experience data within broader market trends, the research highlights the role of user-centered design and feedback loops in enhancing product relevance and competitiveness. The outcomes of this study can inform product developers, marketers, and health professionals in creating more effective, user-aligned breastfeeding solutions that support ma- ternal well-being and infant health outcomes.
Authors - I Gusti Ketut Agung Ulupui, Gentiga Muhammad Zairin, Septi Nurmalita, Hera Khairunnia, Dwi Kismayanti Respati, Eka Septariana Puspa Abstract - This study examines the relationship between voluntary risk disclosure and Environmental, Social, and Governance (ESG) performance among Indonesian publicly listed companies. While ESG metrics have become central to investment evaluations, the influence of narrative risk reporting on ESG scores remains unclear, particularly in emerging markets where such disclosures are not standardized. Utilizing content analysis of 2022 annual reports and ESG ratings provided by Sustainalytics through the Indonesia Stock Exchange, this study investigates whether risk disclosure serves as a credible signal of corporate sustainability. The findings reveal that risk disclosure does not significantly influence ESG performance, suggesting that investors and ESG rating agencies may discount voluntary disclosures when they lack precision or strategic alignment. In contrast, firm-specific characteristics, namely profitability (ROA), leverage, and firm size—are positively associated with ESG scores. These results indicate that internal financial and structural factors may offer more reliable indicators of ESG commitment than unregulated disclosure narratives. The study contributes to the ESG literature by questioning the signaling power of voluntary disclosures and highlights the need for stronger disclosure standards in emerging markets to enhance ESG credibility and comparability.
Authors - Devanjan Khuntia, Anirban Chowdhury, Subhra Chakraborty Abstract - Cybercrime poses a significant threat to India’s business environment, disrupting economic ecosystems, revenue growth, innovation, and employee well-being. As digital adoption accelerates, sectors like banking, e-commerce, and manufacturing face escalating vulnerabilities. This study analyses cyber crime’s multifaceted impact, revealing projected losses of ₹20,000 crore in 2025, with banking (₹8,200 crore) and retail (₹5,800 crore) hardest hit. Beyond financial damage, attacks erode trust, delay deliveries (30% of manufacturers), and slow AI/cloud adoption (40%). Intellectual property theft affects 22% of firms, while SMEs struggle with weak defences (73% prioritize cybersecurity). Employees face data breaches (32% via PUPs) and stress (45%). The paper advocates for stronger policies (e.g., enhancing I4C), AI-driven security, and workforce training to build resilience. A holistic approach—combining regulation, public-private collaboration, and awareness—is critical to safeguarding India’s digital economy.
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, N Vijayalakshmi, Pellakuri Vidyullatha Abstract - Ambient Intelligence (AmI) systems are intended to support responsive and adaptive services by understanding the context of their users. The present paper proposes a machine learning framework for context prediction in ubiquitous environments on the basis of multi-sensor ambient data. The method classifies user activity into four high-level contexts: Sleeping, Working, Cooking, and Outdoors, exploiting features like Temperature, Light, Sound, Motion, and Time. The model described here consists of three steps: Preprocessing, Normalization, and Classification based on Random Forests. Visualization techniques, such as KDE plots, correlation matrices, and 3D scatter analysis, are used for the interpretation of contextual separability and the influence of features. Experimental results show that outdoor activity strongly dominates the dataset, while other contexts are almost neglected. The proposed framework shows high predictive accuracy with interpretable insights into context-dependent behavioral patterns. This work thus contributes to the design of such adaptive, context-aware systems that can find a home in intelligent environments, especially in the realms of smart homes and assistive technologies. The abstract makes it abundantly clear how crucial Ambient Intelligence systems are to contemporary ubiquitous surroundings, particularly for assistive technology and smart houses.
Authors - Adrian Diepeveen, Siphesihle Sithungu Abstract - Traditional electricity grid networks have approached critical resilience thresholds, with archaic grid architectures demonstrating inadequate capacity to accommodate volatile demand fluctuations. South Africa's current energy crisis, characterised by unpredictable power outages and extensive economic losses from electricity outages, emphasises the need for novel optimisation frameworks. This re-search proposes a framework named AMPERE (Autonomous Multi-Agent Predictive Electricity Reinforcement Learning Engine), a dual-phase solution which com-bines hierarchical multi-agent deep reinforcement learning (MADRL) and evolutionary algorithms (EAs) in order to address electricity distribution challenges through decentralised multi-agent coordination. A significant gap exists in research integrating distributed hierarchical knowledge-sharing MADRL with EAs at both regional and national levels, since current approaches focus on singular artificial intelligence (AI) methodologies or non-hierarchical frameworks. Ultimately, AMPERE introduces a novel three-tier hierarchical agent architecture consisting of smart grid national agents, smart home regional agents, and smart home battery agents, utilising Pecan Street's real-world Internet of Things (IoT) time-series datasets. The Centralised Training with Decentralised Execution (CTDE) paradigm enables effective multi-agent coordination across regional and national scales, subsequently enhancing smart grid stability and scalability while minimising electricity lost in distribution.
Authors - Jayan V, Sreejith Alathur Abstract - The COVID-19 pandemic not only strained global healthcare systems but also exposed healthcare professionals to unprecedented psychological challenges. Among the various stressors, the rapid spread of misinformation on social media emerged as a significant contributor to emotional distress, confusion, and burnout among frontline workers. This study explores the impact of social media misinformation on the mental health of healthcare professionals in India during the pandemic, using qualitative interviews with 41 public sector healthcare workers. Guided by the honeycomb social media framework and stress coping models, the re-search examines how misinformation intensifies psychological stress and how individuals and institutions respond to it. Findings reveal that misinformation created confusion in clinical protocols, fueled public panic, and intensified professional anxiety. Participants emphasized the need for verified communication channels, stronger digital literacy, and mental health support systems. The study underscores the critical role of individual responsibility, institutional action, and policy reform in combating the "infodemic" and protecting the well-being of healthcare workers. Recommendations include the establishment of centralized information platforms, community-driven myth-busting initiatives, and proactive involvement of enforcement agencies. This research calls for a multi-stakeholder approach to address misinformation as a public health risk in its own right.
Authors - John Paul C. Masuhay, Reynaldo R. Corpuz Abstract - This piece of work, setting up a new stage, introduces a novel optimization approach for Torus Fully Homomorphic Encryption (TFHE) using Genetic Algorithms (GA). TFHE protects data privacy by facilitating calculations on encrypted data. The study integrates GA with TFHE to optimize key parameters, such as LWE dimension, decomposition level, and security level, aiming to achieve an optimal trade-off between encryption accuracy, bootstrapping time, ciphertext size, and security. The GA optimization uses a multi-objective fitness function, in which performance metrics are derived from synthetic datasets such as text, alphanumeric, and special characters for simulation. The results indicate that the GA framework using DEAP was effectively integrated with TFHE. After five generations, the optimization process attained a best fitness value of 0.9691, marking significant enhancements in system performance. Specifically, the GA optimization improved TFHE parameters, such as increasing the LWE dimension from 1024 to 1514.02 by reducing the noise standard deviation from 3.2 to 2.53 and improving the security level from 128 bits to 242.57 bits. The decomposition level was adjusted from 4 to 2, optimizing computational performance while slightly reducing security. Variation in bootstrapping times across datasets reflects how efficiently the optimization balances efficiency and performance. These remarkable findings suggest that GA could potentially be applied to im-prove TFHE parameters, making it a more efficient and secure method to perform privacy-preserving computations, particularly within edge computing environments.
Authors - Alpa R. Barad, Ankit R. Bhavsar Abstract - A good quality of forage is essential to feed cattle to improve its health and productivity. Quality of grass has been affected by various factors such as weather condition, leaf disease, and diverse species with texture similarity. Over multiple species of grass and variation over sessions leads quality recognition more complex and difficulty. Proposed study uses unsupervised approach to reduce dependence of annotation over multiple species of grass. Proposed work uses median and fast denoising algorithm to enhance input image. Article present novel approach with integration of Gabor filter with k-means++ model. Gabor filters helps to identify optimized features over texture similarity problem of grass. Simulation of proposed study also measures denoising input grass image with 0.72 SSIM. Simulation of proposed study also finds remarkable performance with 79.86% accuracy with median and Gabor filter. Simulation of study also explore possible variations with hybrid unsupervised approach to enhance performance of the model.
Authors - GGS Pradeep, Thrilok. Kolla, N Vijayalakshmi, U Ananthanagu, Rajesh Sharma R Abstract - The booming volume of genomic data requires computational techniques that will allow the efficient extraction of biologically meaningful patterns. This work investigates advanced data mining algorithms for extracting frequent patterns and motifs in DNA and RNA sequences based on k-mer analysis. The nucleotide sequences are divided into shorter pieces called k-mers, where frequent pattern mining, positional frequency mapping, and dimensionality reduction techniques are applied to expose conserved motifs and structural characteristics. The proposed methodology employs Apriori for mining, followed by Principal Component Analysis (PCA) and a variation of a sequence logo style for visualization, which exposes latent relationships and biologically relevant sequence patterns. The experimental data show uniformly distributed occurrences of dominant k-mers and positionally clustered functional motifs like the “ATG” codon. The PCA projections in 2D and 3D space further highlight the structural diversity and possible clustering of sequences according to k-mer profiles. This research extends the frontier of bioinformatics by aiding in the interpretation of genomic sequences, with applications in genome annotation, disease-associated gene prediction, and regulatory motif identification. It serves the purpose of informing about the methods being brought together under k-mer analysis, frequent pattern mining, PCA, and motif visualization.
Authors - Zlatinka Kovacheva, Kalinka Kaloyanova, Ina Naydenova, Mariana Trifonova Abstract - This paper concerns project-based learning. The aim of the study is to compare the results of group and individual projects of undergraduate students studying Information systems. The research is based on statistical inference and hypotheses tests. A hypothesis test is presented regarding a difference between means of two dependent samples for quantitative indicators that have a normal distribution. The result analysis is based on 3-years experiments with student pro-jects. For all experiments, the null hypothesis is rejected, and in both methods of assessment (group and individual projects), different results are obtained. For the majority of the groups involved in the experiments, the marks of the group project are higher than the average marks of the individual project but there are also exceptions. The results show that students who performed better on the individual project often received lower grades on the group project. Also, students with low individual grades received higher scores on the group assessment. As a conclusion, we can recommend applying both methods – group and individual projects for students’ assessment, paying attention at their advantages and disadvantages.
Authors - Nguyen Hoang Kha, Su Truong Phuc, Huynh Tu Anh, Ha Cao Vi, Tran Quoc Hao, Vinh Dinh Nguyen Abstract - Face recognition tasks often suffer from poor performance when limited real training data is available per class. To address this, we propose a novel pipeline that combines LoRA-based fine-tuning of a generative model with synthetic image generation and YOLOv8 classifier training. Our method fine-tunes Stable Diffusion using just a few real face images per identity, then generates realistic, identity-preserving synthetic images to augment the training dataset. The augmented dataset is then used to train a YOLOv8n classifier for face recognition. We evaluate our pipeline on a 31-class face dataset and achieve a Top-1 classification accuracy of 80.05%, significantly outperforming the baseline YOLOv8n model trained only on resized real images (60.94%). These results demonstrate the effectiveness of our method in low-resource settings for face recognition.
Authors - Keshav Dudani, Nischay Jain, Rishi Raj, Nitesh Patnaik, Vishal Meena Abstract - Stock market volatility and the effect of financial trends and external news perspectives are reasons why predicting the direction of the stock market is so intrinsically tough. Conventional models may fail to provide accurate forecasts when they fail to react to the quick changes in the marketplace. Our proposed method boosts the success of predicting by employing StockVisionX, which constructs classifications by combining past stock movements and sentiment analysis of financial news sources. The sentiment polarity that is integrated into StockVisionX delivers a more thorough market movement projection, thereby forecasting whether a stock will gain or drop in price. StockVisionX adds even more strength in its predictability with the usage of vital technical indicators such as SMA, EMA, RSI, MACD, ATR, and OBV. Supplementing these criteria with sentiment analysis ensures a better and evidence-based manner of categorizing stock movement. The model is trained and tested on diverse datasets to analyze sentiment and forecast prices, and task-specific optimization is seen. Our Naive Bayes technique is 54.5% more accurate when compared to Vader sentiment categorization. Our Prophet + XGBoost model, which has been trained on non-correlated real-world data, outsmarts ARIMA and LSTM by 49.0% and 29.6%, respectively, when predicting price; however, it is hard to determine if these models would work better with highly correlated data. This is evidence of the power and generalizability of our process. StockVisionX is efficient and precise in its classification model on predicting stock movement through the meticulous integrating of sentiment-related information with the trend history.
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, N Vijayalakshmi, Pellakuri Vidyullatha Abstract - The ever-increasing implementation of IoT sensor networks in smart environments has created a multitude of multivariate spatiotemporal data streams. The effective identification of temporal patterns and anomalies in those streams becomes necessary to ensure environmental awareness and operational integrity. This paper proposes an elaborate framework for temporal pattern recognition in IoT sensor data through the integration of spatiotemporal reasoning, statistical modeling, and anomaly detection. The approach uses statistical descriptors, kernel density estimation, correlation matrices, and spatiotemporal visualization to capture local anomalies as well as trends prevalent in aggregation over distributed sensors in order to illustrate its strengths in analyzing the data. One of the case studies depicts the environmental awareness based on monitoring temperature: deviation detection, inter-sensor coherence, and a conceptual view of anomaly propagation. The results indicate the potential of establishing a system capable of real-time and interpretable monitoring in dynamically evolving IoT settings. This work contributes toward more adaptive and transparent sensing architectures capable of operating under uncertainty and environmental variability. The abstract is coherently structured, flowing from motivation to case study, methodology, results, and contribution. It also accurately frames the rationale for the study and successfully defines the fundamental difficulty in IoT sensor networks, which is the recognition of temporal patterns and abnormalities.
Authors - Tushaar Yenduri, S S R Subramanya Hemant Konduri, Kalyan Netti Abstract - This study examines three inference strategies—serial, multi-core parallel (Joblib), and distributed memory (MPI)—to address scalability challenges in applying machine learning models to large tabular datasets. Using the HIGGS dataset as a representative benchmark of high-volume scientific data, a Random Forest classifier was trained and validated with performance metrics of 73.3% accuracy and 81.25% ROC-AUC. The results offer concrete guidance on selecting appropriate inference backends based on system architecture and dataset size, particularly for practitioners in high-performance computing and scientific machine learning domains.
Authors - Kevin De la Cruz, Alberto Taza, Edgar Ramos, Donovan Fuqua Abstract - The textile industry is facing growing pressure to transition toward production systems that are sustainable, efficient, and aligned with circular economy principles. This study develops a structured multi-criteria decision-making framework designed to integrate lean practices, circularity, and sustainability into strategic planning for textile manufacturing. The methodology applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to evaluate and rank key operational practices considering their environmental, operational, and economic impacts. Results indicate that prioritizing lean initiatives with embedded circularity practices provides a dual advantage: improving short-term efficiency while simultaneously supporting broader sustainability goals. The proposed model offers decision-makers in the textile sector a practical and evidence-based tool to address challenges of competitiveness, environmental regulation, and sustainable value creation. Ultimately, this research contributes to advancing integrated planning approaches that strengthen the ongoing transition toward sustainable manufacturing systems.
Authors - Matana Charoenwongsa, Sangkama Sarawat, Laknara Chandraramya, Waralak Vongdoiwang Siricharoen, Thirachit Saenphon Abstract - The global media landscape is experiencing a profound shift, shaped by the rise of artificial intelligence (AI), growing cross-cultural content demands, and evolving patterns of work. This study investigates how active stakeholder participation can guide the development of an AI-integrated curriculum within global media education. Emphasis is placed on nurturing technological fluency, interdisciplinary perspectives, and ethically grounded innovation. Drawing on a curriculum design initiative from Thailand’s Global Media Industry and Innovation Master’s Programme, the research explores how AI-driven tools, media production technologies, and entrepreneurial approaches are brought together in educational practice. Insights were collected from over 50 stakeholders—including media entrepreneurs, policymakers, educators, and students—and examined through thematic content analysis. The research highlights three core priorities that shaped the curriculum design: strengthening technological fluency, deepening global media literacy, and encouraging social innovation through entrepreneurship. It proposes a collaborative model that addresses technological change while centring human agency, with practical implications for curriculum reform and the future of sustainable media ventures in an AI-influenced era.
Authors - Chethana N S, Anithadevi M D Abstract - We introduce a groundbreaking steganographic technique that merges non-linear cost evaluationmethods with binary triangular matrix structures to achieve superior data concealment in color images.Our approach tackles the inherent weaknesses of conventional linear cost models by implementing amultiplicative framework that more effectively maintains the statistical characteristics of images whileoptimizing embedding performance. The integration of structured binary matrices enables intelligentposition selection through organized randomization, preserving both visual fidelity and cryptographicsecurity. Our methodology incorporates sophisticated texture evaluation, self-adjusting parameter tuning, andmulti-channel analysis to deliver outstanding results across various image categories. Through rigoroustesting, we demonstrate remarkable improvements over current leading methods, achieving averagePSNR values of 52.34 dB and SSIM scores of 0.9847 across standardized test collections from multipleimage databases. The system exhibits substantially enhanced resilience against contemporarysteganalysis tools while supporting high embedding rates up to 0.8 bits per pixel with strengthenedsecurity assurances.
Authors - YURY MIKHEEV, URSULA PODOSENIN, SERGEY V. SYCHOV Abstract - This study investigates the reliability and validity of the 360-degree feedback method by focusing on three primary sources of bias: respondent roles, interpersonal relationships, and individual evaluative styles. Drawing on data from over 15,000 respondents across multiple organizations, it applies a Python-based statistical algorithm to quantify the impact of these biases on competency ratings. Findings reveal that such factors can account for up to half of the variance in assessments. Mitigation approaches include evaluator anonymity, structured training, and correction coefficients. With these measures in place, 360-degree feedback continues to serve as an effective tool for fair and accurate performance appraisal, supporting personnel decisions and sustainable talent management.
Authors - Zarif Wasif Bhuiyan, Zaed Bin Monir Atib, Mahady Hasan, Farhad Alam, Md. Tarek Habib Abstract - Early and accurate identification of common skin conditions is essential for appropriate therapy and better clinical outcomes. However, diagnostic expertise is often limited in resource-constrained healthcare or remote settings. We present a robust deep learning approach based on a custom convolutional neural network (CNN) to automatically classify five prevalent skin diseases (acne, contact dermatitis, nail fungus, scabies, and urticaria) from clinical images. A curated dataset of 8,900 clinical images (1,780 per class) was compiled, with standardized resizing, normalization, and extensive augmentation applied to simulate real-world variability and enhance generalization. The CNN architecture consists of five sequential convolutional blocks with progressively increasing filters to extract high-level features, accompanied by dropout layers to mitigate overfitting, and includes a fully connected layer prior to the SoftMax output. On evaluation, the model achieved an overall classification accuracy of 99%, demonstrating robust performance and high diagnostic precision across all categories. It notably outperformed conventional machine learning classifiers as well as state-of-the-art pretrained deep networks on the same task. This exceptional performance, combined with strong generalization capabilities, underscores the model’s reliability and suitability for deployment in real-world clinical practice and tele dermatology settings. In such scenarios, rapid and accurate screening for multiple skin conditions can significantly improve patient care and enable earlier interventions.
Authors - Amruta Khanaj, Sourabh Khanaj, Smita S. Sangewar Abstract - This research investigates the security implications of reusing code snippets of Stack Overflow in open-source software projects. We present a novel framework that proactively identifies and mitigates security risks associated with outdated Stack Overflow code snippets. Our methodology combines static code analysis techniques, continuous monitoring of Stack Overflow post changes, and an automated developer notification system. We analyzed 1.5 million code snippets from Stack Overflow approximately across 5000 popular open-source GitHub projects, focusing on programming codes. Our observations reveal that there are a significant number of reused snippets becoming outdated over time, with developers often missing critical security updates made to the original Stack Overflow posts. We showcase the effectiveness of our framework in detecting potential vulnerabilities and alerting developers to security relevant updates in reused code. The research emphasizes the need for continuous monitoring of community-sourced code and suggests solutions to ensure its secure integration in software development practices. Our work contributes to the broader understanding of software supply chain security and the evolving nature of code reuse in the open-source ecosystem.
Authors - Mst.Sadia Siddika, Sherin Sultana, Md. Tarek Habib, Sabrina Alam, Md. Mahbub Alam, Mahady Hasan Abstract - Agricultural products are graded based on their quality and adherence to standards. Managing the grading, packaging, and transportation of products, especially perishable items like food, is crucial for maintaining freshness and safety. Packaged goods are transferred to storage facilities and prepared for transportation. To maintain product freshness, a well-organized and traceable supply chain is essential. This study demonstrates how technologies such as the Internet of Things (IoT) can facilitate an agricultural supply chain system that uses maps, computer vision, smart packaging, and smart barcodes. These technologies help track products in real-time, ensure data security via databases, and streamline supply chain operations through web-based platforms. Using an app-based system, the solution highlights traceability, automation, and user-friendly interfaces to optimize efficiency from farm to market. The app also provides automated alerts and real-time inventory updates for both customers and sellers. Additionally, the system should integrate IoT-based temperature and humidity monitoring to maintain the quality of perishable goods. Overall, this study suggests that combining these advanced technologies into a unified system can address challenges in traditional methods, such as delays and product quality loss.
Authors - Kostaryev D.B., Tevyashev A.D., Sizova N.D., Tkachenko V.P. Abstract - An intelligent information system for proactive management of a printing enterprise based on artificial intelligence is proposed. Approaches to proactive enterprise management grounded in Industry 4.0/5.0 are substantiated, including the use of relevant scientific methods and mathematical models. The architecture of the information system, based on mathematical models and scientific principles, has been developed. The results of evaluating the effective-ness of implementing and using this intelligent information system at a printing enterprise are presented.
Authors - Zarif Wasif Bhuiyan, MD. Humayun Kabir, Paramita Saha, Md Mahbub Alam, Mahady Hasan Abstract - Urban–rural evidence in Bangladesh is often fragmented or short-lived, limiting its value for tracking change. This study leverages the Life in the Field Experience at Independent University, Bangladesh to build a pre-COVID baseline from eight annual waves of student-collected surveys spanning 2013–2020 across six districts: Jessore, Bogra, Mymensingh, Sylhet, Barishal, and Dinajpur. Heterogeneous spreadsheets are harmonized into standardized sector indices for education, medications and health service use, employment, marital history, acute morbidity, and chronic morbidity. A transparent pipeline handles header inconsistencies, variable shifts, and missingness, producing comparable annual series. ARIMA models are estimated for Jessore to generate five-year projections and are interpreted alongside cross-district benchmarking using z-scores. Results show steady gains in education that level off, an employment dip followed by recovery, rising medication use, low and stable acute morbidity, a gradual increase in chronic morbidity, and largely stable marital patterns. Forecasts are framed as extensions of pre-COVID dynamics with explicit uncertainty. The pipeline provides a reusable template for short annual series and supports policy diagnosis at district level. Future work will extend forecasting to all districts, incorporate structural-break tests and hierarchical models, and enrich indices with external covariates and administrative validation.
