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 - 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 - 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.
