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 - 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 - 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 - Sapna B. Warghane, Ajay Pethe Abstract - Modern banking institutions recognize employee engagement and retention as critical to achieving long-term goals. These elements influence productivity, innovation, and customer satisfaction. Public and private sector banks adopt different strategies shaped by political culture, regulatory environments, and competitive dynamics. Public Sector Banks (PSBs) offer job security, structured career growth, and welfare benefits, fostering stability and long-term com-mitment. These factors contribute to employee motivation and consistent performance but may limit agility and innovation. In contrast, Private Sector Banks (PvSBs) emphasize performance-based incentives, career mobility, and a dynamic work environment. While these strategies enhance engagement and drive results, they can also lead to stress and higher employee turnover. Organizational culture, compensation, career development, work-life balance, and employee well-being play significant roles in shaping these outcomes. This review compares engagement and retention strategies across both sec-tors, identifying the strengths and challenges of each. A hybrid approach—combining financial rewards, career development opportunities, and wellness initiatives—is recommended to enhance satisfaction and commitment across the board. By refining these strategies, both PSBs and PvSBs can develop a more engaged and resilient workforce, ultimately supporting long-term organizational success and stability.
Authors - Jannat Khair Chowdhury, S. M. Saikat Hossain, Farzana Sadia, Mahady Hasan, M. Rokonuzzaman, Marufa Akter Abstract - Developing an effective marketing strategy is crucial for the success of Software-as-a-Service (SaaS) businesses, given the dynamic and competitive nature of the market. This systematic literature review looks at major tactics including pricing structures, client acquisition and retention, and digital technologies. It emphasizes the problems of transferring academic findings to industry, with an emphasis on data-driven solutions such as hybrid pricing, freemium models, and tailored content. The report provides recommendations for optimizing SaaS marketing to promote scalable, long-term growth.
Authors - Juan Mejia, Valentino Galindo, Edgar Ramos, Donovan Fuqua Abstract - This study proposes a conceptual model that integrates advanced supply chain planning to enhance supply chain resilience and sustainability. Drawing from previous literature, the study explores how such planning approaches contribute to strengthening supply chains and improving long-term operational stability. The model is developed through a structured review of academic sources and expert perspectives, with an emphasis on theoretical alignment and systemic relevance. Using the Best-Worst Method (BWM) as a decision-making framework, the paper outlines key factors that may influence effective resource allocation and disruption management. While no empirical validation is conducted, the study highlights potential criteria for improving operations, particularly in terms of reducing disruptions and optimizing resource use. This work offers a foundation for future empirical research and supports ongoing discussions on resilient and sustainable supply chain strategies.
Authors - Divya Saleela, Reshma Lucos, Reji R, Teena Joseph, Shyna A, Chinchu M S, Hashna Rasheed, Arjun Rajesh Panicker Abstract - The automatic classification of mental health-related textual data is essential for enabling scalable and proactive psychological support. This study employs a fine-tuned BERT-base transformer model categorise statements into seven clinically relevant mental health conditions. The model was evaluated using a publicly available dataset from Kaggle and assessed with standard performance metrics, including accuracy, precision, recall, specificity, and F1-score. Achieving an overall accuracy of 90.00 per cent, the model demonstrated strong generalisability across a linguistically diverse and imbalanced corpus. The highest classification performance was achieved in the Normal, Depression, and Suicidal categories, with satisfactory results obtained for less frequent classes. These findings highlight the effectiveness of transformer-based architectures in mental health text classification and support their potential application in digital screening and early intervention systems.
Authors - Gokul B, Arumugam Balasubramanian Abstract - Climate change and sustainable ecosystem are gaining momentum in the public domain. Innovative technology is steering towards environmental protection enabled products and processes. Tata motors have captured the public imagination by lining up green vehicles with clean fuel. In order to accelerate the adoption of this innovative product they have embarked on an advertising campaign that not only caters to product marketing but also satisfies their inner desire towards contributing their might in the green initiatives. To this end, content analysis is used as a methodological tool to examine whether archetypal advertising strategies align with Tata Motors’ goal of promoting innovation in a sustainable manner. Analysis has come out quite clearly that archetypal advertising strategy are put to effective use to successfully kindle their desire and satisfy the consumers motivation towards adopting automotive innovation in the context of green commerce.
Authors - Asra Fatema Zakir Baig, Amol Mashankar Abstract - Women in business leadership must be empowered through promotion of diversity, innovation, and sustainable growth. Conceding that there have been noticeable gains, women remain way behind in their quest for leadership positions, due to multifaceted barriers, such as systemic biases, limited access to their networks, and societal expectation; this paper discusses the organizational culture and its gender-based stereotypes and the lack of mentorship, which hold back women's advancement in business leadership. Further, it goes on discussing the various opportunities that now come forth from women's leadership empowerment, further focusing on the advantages of a diverse leadership team: better decision-making and improved performance for companies. Through real examples of best practices, the authors demonstrate which organizations have applied some of the best diversity and inclusion strategies followed by positive results from investing in women's leadership development. The paper speaks to actionable strategies an organization can employ in addressing the challenges and seizing the opportunities available. It focuses first on creating an inclusive workplace culture followed by starting out with mentorship and sponsorship programs, targeted training and development. And finally work arrangements and others that also involve policies that support work-life balance and equal pay and works continually in amplifying the need for systemic change in creating an environment that avails women's advancement. Finally, women's empowerment in business leadership is more about equity and strategic advantage as a group for organizations to leverage benefits. In the end, embracing diversity in leadership can help unlock potential, drive innovation, and develop a resilient workforce for businesses. This paper, therefore, calls for collective efforts from stakeholders across all levels to advance women's leadership and pave the way for unlocking the silos of barriers in the business world.
Authors - Sazzad Hussain Farhaan, Tanvir Hussain Himel, Ahsanul Kaiser, Md. Nayon Ali Abstract - Utilising digital technology is more crucial than ever, especially social media, to address urgent societal issues and encourage creative solutions. Since traditional infrastructures and resources may be few in underdeveloped nations like Bangladesh, social media provides an affordable and easily accessible avenue for impact, creativity, and collaboration. This strategy continues the heritage of Grameen Bank and Dr. Muhammad Yunus, who pioneered creative social entrepreneurship through microfinance. The use of social media to promote social entrepreneurship in Bangladesh is examined in this qualitative study. Based on 20 in-depth interviews with people involved in social entrepreneurship, the study explores how digital platforms may support social transformation, especially in environments with limited resources. Purposive sampling was used to choose participants, guaranteeing that they had at least a year of experience utilising social media for social entrepreneurship. Social media is a vital tool for outreach, community involvement, fundraising, and creating awareness, according to the findings. By overcoming conventional obstacles, it enables business owners to reach a larger audience and rally support for social causes. Future research can be done on digital infrastructure, and community participation affect social media’s ability to promote social entrepreneurship.
Authors - Radhika V. Kulkarni, Spandan Papade, Yash Paryani, Saniya Patil, Rahul Madhawani, Prakash Sharma Abstract - Governments worldwide actively support entrepreneurs tackling pressing social issues, aiming to boost economic opportunities for all, generate employment, and accelerate tech-driven progress. Unfortunately, some recipients exploit these funds for personal gain, sidelining projects that could otherwise drive fair governance, community welfare, and environmentally conscious industries. Manually tracking thousands of funded startups is inefficient, allowing dis-honest actors to slip through the cracks. This study introduces an AI-powered solution to bridge this gap. The system scans public data—such as LinkedIn activity and online footprints—to identify red flags, like founders holding jobs un-related to their funded ventures. Advanced language analysis and pattern recognition techniques assess how closely a founder’s current work aligns with their original proposal, simplifying oversight while fostering synergy between public programs and modern technology platforms. By promoting fairness, transparency, and accountability, the system supports several United Nations Sustainable Development Goals, including SDG 8: Decent Work and Economic Growth, SDG 9: Industry, Innovation, and Infrastructure, and SDG 16: Justice, and Strong Institutions. It enables governments to channel resources more effectively to innovators committed to creating lasting social value.
Authors - Ashiqha Sultana, Kalyani Suresh Abstract - Sustainable fashion apparel purchase on Instagram has utmost importance in recent times, as Indian consumers are currently influenced by many factors that finally lead them to purchase intention. This paper tries to examine and explore the Indian consumer mindset towards sustainable fashion apparel on Instagram and the connection between demographics and consumer identity of the consumers with the sustainable constructs. Responses were taken from 100 respondents using snowball sampling from different parts of India. Results of the study show that there is a significant association between demographics, patterns of interacting with sustainable fashion apparel on Instagram, and Consumer Identity. The study also reveals the strong connection between Indian consumers and purchase intention, consumer identity, sustainable constructs, purchase behaviour, and Instagram motivation.
Authors - Sapna B. Warghane, Ajay Pethe Abstract - Artificial Intelligence (AI) has had a significant effect on decision-making in business by providing data-driven decision-making, improving efficiencies and decision-making accuracy. This literature review will elaborate on the impact of Artificial Intelligence on decision-making in business and its various components, such as predictive analytics, automation, risk management, marketing personalization, strategy, etc. It will also explore issues, ethics, and future prospects of AI in business-related decision-making. AI has already been adopted in various industries, including finance, healthcare, retail, and manufacturing, to increase efficiencies and optimize operations while attempting to optimize the customer experience. On the other side of AI, increasing utilization creates other challenges about bias, security, and regulatory compliance must be considered in the sustainable growth of AI in business.
Authors - Seema Tripathi, Nikita Bhardwaj, Shivani Bansal Abstract - Voice recognition technology has evolved into a cornerstone in contemporary applications, from virtual assistants and intelligent homes to biometric security and healthcare. Its potential to facilitate unobstructed human-computer interaction has hastened the need for more precise and more robust systems. GMM and HMM traditionally paved the way for voice processing. However, deep learning architectures—namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models—have revolutionized the field by achieving unprecedented performance and flexibility. This research paper provides a comparative study of major deep learning models for voice recognition, critically evaluating their design rationales, merits, and shortcomings Some major challenges like scarcity of data, noise tolerance, computational efficiency, and interpretability of models are discussed. The paper also offers the perspectives of the future trends in work like the inclusion of self-supervised learning, light-weight models for edge devices, and multimodal systems that will influence the next voice recognition generation. The goal of this work is to offer an end-to-end perspective of what exists currently and stimulate future work in this very dynamic field.
Authors - Vipul Lanjewar, Sourish Joshi, Sujata Joshi, Menachem Domb Abstract - AI is changing supply chain, transport, and logistics using neural net-works to improve decisions. It helps with real-time analysis, route planning, and demand prediction. A bibliometric analysis with Scopus data was performed, using VOSviewer and Biblioshiny to track trends, citations, and new themes in AI for these fields. The focus was on areas like warehouse automation, self-driving logistics, and smart transportation, while also tackling data security and ethical issues. By finding key research and gaps, this paper shows how AI can shape the future of these industries.
Authors - Anshika Srivastava, Rojalin Patri Abstract - This study explores whether arms and armed forces increase the probability of armed conflict within a country. Though several studies in literature have highlighted the cases where arms and armed forces have influenced armed conflict within a country, none of them have provided empirical evidence to support this phenomenon. This study attempts to bridge the gap by providing a quantitative analysis across 45 countries over 28 years and further substantiates the arguments laid in qualitative studies. Random effect panel logistic estimates and year-wise logistic estimates have been used to capture the overall as well as year-wise significance of arms and armed forces on armed conflict within the country, respectively. Similarly, when the number of armed forces increase by one million, the odds for armed conflict becomes 3.97 times more and when military expenditure increase by 1%, the odds for armed conflict becomes 9.06 times more than before. These insights point towards the fact that arms, armed forces and militarism don’t necessarily help reduce violence.
Authors - Kalyani Yede, Deepak Sharma Abstract - Work-life balance is of utmost importance to career workers managing the challenges of contemporary work environments and family duties. This research employs secondary data to investigate the determinants of work-life balance, its impact on worker satisfaction, productivity, and health, and organizational support as a facilitator of balance. The study finds that flexible work arrangements, supportive management, and organizational policies all contribute strongly to helping employees balance work and personal life. Excessive workload and a lack of support, on the other hand, lead to job dissatisfaction and stress. The study emphasizes a need for cooperative efforts between employers and employees to create a good work-life balance. This study offers useful insights to organizations that want to enhance organizational performance and employee well-being by implementing focused work-life balance programs.
Authors - Namrata Patil, Pravin Desai, Ashvini Patil, Pragati Patil Abstract - In the context of multimedia content of huge scale, effective and efficient video summarization has become more and more important for improving web browsing experiences and information overload. In this paper, we introduce a new video summarization method by modeling it as a sequential decision-making problem. A Deep Summarization Network (DSN) is proposed for predicting the probability of considering each video frame in terms of its possible contribution to the final summary. For training the DSN, we suggest an end-to-end deep reinforcement learning system that works in a fully unsupervised way, without requiring annotated labels or human intervention. Our approach has its foundation in a well-crafted reward function that assesses generated summaries along two primary dimensions: diversity and representativeness. These elements are designed to ensure that the chosen frames not only convey the main content of the video but also encompass a good number of visual and contextual variations. The DSN is trained to maximize these rewards, resulting in brief, informative, and non-redundant summaries. Extensive experiments are performed on two standard datasets to confirm the efficacy of the new method. The experimental results show that our method is capable of rendering competitive performance compared with state-of-the-art methods, especially under unsupervised scenarios, thus revealing excellent potential to be applied in real-world systems for large-scale video analysis and multimedia retrieval.
Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle Abstract - The machine learning algorithms used for the automated predction of esophageal cancer, a highly destructive disease that frequently exhibits at an advanced stage because it lacks early symptoms, is examined in this work. A huge dataset including clinical, demographic, and diagnostic information is used in the study to relate the classification algorithms of logistic regression and decision trees. With a incredible accuracy of 99.75% compared to 64.87%, the Decision Tree model clearly beaten Logistic Regression. Its capability to recognise complex, non-linear relationships in the data is responsible for Decision Tree's better performance, which makes it a useful tool for early cancer detection. The study identifies the need for validation on a variety of datasets and techniques to reduce overfitting, even as the results illustrate how machine learning may improve diagnostic accuracy. Improved clinical processes and better patient outcomes are made possible by us research's successful development of scalable, interpretable, and effective diagnostic tools.
Authors - Jatin Kumar Sangewar, Chandan Kumar Sangewar, Gaurav Kumawat, Chirag Joshi Abstract - In this research paper, we introduce a novel approach to improving research paper recommendation systems using the Universal Sentence Encoder (USE) to analyze and understand the textual content of academic papers, such as their title, abstract, and publication year. Our ultimate objective in the proposed methodology is to greatly enhance the accuracy and personalization of paper recommendations in favor of scholars, researchers, and students. Our research provides a thorough system architecture review, extensive experimentation with regard to state-of-the-art benchmarks, as well as an assessment of its probable impact on the world of academia. In addition to the technicalities of our method, we explore the larger research paper recommendation system context. This involves a close look at the issues that plague these systems, including data sparsity and the cold start problem, and the potential directions they offer for enhancing academic discovery. We also look at possible futures for the discipline, including the incorporation of user profiling, collaborative filtering methods, and ethical considerations into the building of these systems. The goal of this study is to push the field of research paper recommendations into a new generation based on the use of state-of-the-art Natural Language Processing and Machine Learning methods while ensuring a commitment to enhancing the academic experience. The new use of the USE model in this system offers intriguing possibilities for making more accurate and user-specific recommendations, which will eventually enable a more productive and efficient academic experience for users in general.
Authors - Naina Kokate, Aoudumber D. Londhe, Priyanka Swapnil Raikar, Nikeeta Pande, Sanjana Sanjiv Nikam, Nethra Nadar, Indranil Nayak, Rhia Nevgi, Neil Oswal Abstract - This project demonstrates about working of Peltier module with an new approach, we have tried making a smart way of using it by making a smart water temperature modulation bottle. The targets we wanted to achieve through this project was to make it energy efficient along with cost effective. the dual purpose of heating and cooling to be done as per our wishes. the main components which are used in this project are TEC1-12706 Peltier module, W1209 digital thermostat, two heatsinks and two fans. the project can be used for personal as well as professional fulfilling of needs such as getting water to a certain temperature or maintaining a medicine at a given temperature. the project can work on both battery and electricity.
Authors - Ketaki Paranjape, Rajasi Barapatre, Sandhya Arora Abstract - This study conducts a statistical analysis of the Israeli-Palestinian conflict to uncover patterns and relationships that characterize the dynamics of hostilities. The research examines critical questions about the association between event location, age, citizenship, and types of ammunition, employing statistical methods such as Z-scores, Chi-square tests, and F-tests on a comprehensive dataset. Key findings include significant regional variations in participation in hostilities, with Gaza showing the highest levels of engagement, and the predominance of the 21–30 age group in active involvement. Additionally, asymmetrical impacts were observed, with Palestinian civilians primarily affected by Israeli security forces, and Israeli civilians targeted by Palestinian actors. Analysis of ammunition usage reveals targeted engagement against younger age groups and highlights the varied means of violence in the conflict. These findings provide valuable insights into the conflict's demographic and regional dimensions, offering a foundation for data-driven conflict resolution strategies and humanitarian interventions.
Authors - Kalyani Yede, Deepak Sharma Abstract - This study looks into the effect of work-life balance on IT professionals in Pune, India, through analysis of determinants that affect this balance and measures to enhance it. A thorough review of literature demarcates important areas for study, such as the organizational policy, socio-cultural determinants, and perceptions. The research will fill the gap between research and practice by presenting culturally responsive suggestions to organizations looking to improve employees' job satisfaction and productivity. The research will, with a mixed-methods design, offer insights for policy-making and enhancing the quality of life of IT professionals.
Authors - Shamama Kamal, Shailesh Saxena Abstract - This paper examines the transformative impact of Internet of Things (IoT) technologies on agriculture, highlighting their role in enhancing efficiency, productivity, and sustainability. By integrating sensor devices, data analytics, and real-time connectivity, IoT enables precision farming practices and interconnected farming ecosystems. Key components include soil monitoring, crop health analysis, and smart irrigation systems, optimizing resource management and enhancing crop yields. Despite challenges like data security and interoperability, the potential benefits of IoT in agriculture, such as resource optimization and sustainable practices, underscore its significance. In conclusion, IoT emerges as a vital enabler for sustainable and efficient farming practices, promising a more resilient agricultural sector and contributing to global food security.
Authors - Priteshkumar Prajapati, Jugal Mehta, Prince Tadhani, Parth Shah, Amit Thakkar, Dipak Dabhi Abstract - In the current era of mobile technology, Android’s widespread use and customisation by developers have also made it simpler for hackers to identify and exploit security flaws. Android malware continues to evolve, and one of the biggest concerns is how it quietly exploits app permissions and runs in the background to carry out harmful tasks without users even being aware of it. To address this emerging issue, the research proposes a static analysis-based solution for Android malware detection, utilising features derived from application permissions and API call behaviour. The study investigated a range of machine learning algorithms, including traditional methods such as Logistic Regression, SVM, and KNN, as well as more advanced approaches like Neural Networks and ensemble techniques like Random Forest, XGBoost, and CatBoost. We trained and tested the models using a well-organised set of Android app datasets and then measured their performance by analysing the results with a confusion matrix, focusing on key metrics such as accuracy, precision, recall, and F1-score. The confusion matrix clearly shows where our approach performs well, especially in real-world situations where there’s an uneven number of malicious and safe apps. Our results highlight the effectiveness of ensemble learning methods in enhancing mobile security while also providing a clear comparison of how different algorithms approach the task of detecting Android malware.
Authors - Nayan Suresh Patil, Shabana Pirjade, Yash Dattatray Londhe, Yogeshwar Anil Patil, Tej Tushar Jadhav Abstract - This study presents an AI-powered mobile application designed to enhance athletic performance and promote active lifestyles. By evaluating existing sports applications, it explores their roles in boosting user engagement, motivation, and achievement of fitness goals. Challenges such as user consistency and data privacy are also considered. The findings suggest that well-designed sports apps, supported by responsive feedback systems, can play a significant role in personal training. To address the difficulties faced by aspiring athletes, especially in urban settings, this project introduces a feature-rich platform that connects users to nearby sports academies and offers structured guidance across various disciplines like cycling, athletics, and running. A key innovation is the integration of IoT-based monitoring devices, which provide real-time feedback through sensor-based data analysis. This facilitates accurate performance tracking and injury prevention, empowering users with personalized insights for continuous improvement.
Authors - Radhika V. Kulkarni, Gaurav Sulsule, Sanika Kolawale, Urmila Kakarwal, Prathmesh Deshmukh, Prakash Sharma Abstract - The burgeoning startup ecosystem necessitates robust and efficient methods for evaluating the credibility and historical performance of founders and their ventures. Traditional due diligence processes are often manual, time-consuming, and susceptible to information overload and inherent biases. This paper presents "Founder Verifier," an Artificial Intelligence (AI) driven web application designed to automate and enhance the initial stages of founder reputation analysis. The system integrates web scraping capabilities with Natural Language Processing (NLP) techniques to provide a multifaceted assessment. Key functionalities include sentiment analysis of web-derived text using the NLTK VADER lexicon, Named Entity Recognition (NER) for geographical context ex-traction via spaCy, keyword-based potential controversy detection, and assimilation of failure/industry insights. These analyses culminate in a consolidated web report featuring a heuristically derived reputation score, categorized sentiment snippets, links to relevant external profiles, and downloadable PDF summaries. An operational dashboard further provides analytics on system usage and observed trends. This paper details the system's architecture, the methodologies employed for data acquisition and multi-modal analysis, the formulation of the reputation score, and discusses the structure and utility of its outputs. The Founder Verifier aims to provide a rapid, data-driven preliminary assessment tool for stakeholders in the startup ecosystem.
Authors - Ch Sahyaja, Gorripotu Eekshitha Bhagavathi, Karthick.M, Ch Shankar Abstract - This study presents a comprehensive, systematic, and bibliometric review of Green Human Resource Management (GHRM) literature from 2000 to 2025, highlighting key trends, influential authors, research clusters, and emerging themes. Using Scopus as the primary data source, a total of 137 articles were analyzed with tools including VOSviewer, R (Biblioshiny), and Excel. The findings reveal a significant growth in GHRM publications, especially after 2020, with South and Southeast Asian countries leading in research output and collaboration. Co-authorship and co-citation analyses revealed some well-known contributors, including Ramayah T., Yusliza M.Y., and Jabbour C.J.C., and important publication outlets such as the Journal of Cleaner Production and IJHRM. From the keyword co-occurrence mapping, we observe dominant research themes surrounding sustainability, environmental performance, green behavior, and innovation. Besides, this study provides a roadmap for future studies by outlining areas of theoretical void, emergent domains of study and a call to an interdisciplinary and empirical approach.
Authors - Aishwarya V. Kadu, KTV Reddy Abstract - This research investigates the effectiveness of machine learning and deep learning models in multi-class crop classification using an agricultural dataset comprising 3,450 records from the Wardha district of Maharashtra, India. The dataset includes essential soil and climatic parameters such as pH, nitrogen (N), phosphorus (P), potassium (K), Mn, Fe, Cu, Zn, S i.e Nutrients and micronutrients temperature, humidity, wind speed, Cloud and rainfall, covering 11 crop types. Data preprocessing involved label encoding for su-pervised learning tasks. The study developed and evaluated three predictive models: Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed using classification metrics like accuracy and confusion matrix, as well as regression metrics such as MAE, MSE, RMSE, and R-squared. Feature importance analysis was performed to deter-mine the influence of input variables on model predictions. XGBoost showed the highest classification accuracy, while MLP demonstrated strong learning capability. The findings emphasize the value of combining predictive performance and interpretability, highlighting the applicability of AI-driven methods in enhancing crop recommendation systems and advancing precision agriculture across varied agro climatic zones.
Authors - Purva Agarwal, Somya R. Goyal Abstract - Diabetic Retinopathy (DR) is a widespread stage of diabetes that influences the retina and, if untreated, can lead to blindness. DR is one of the primary reasons for blindness, especially among working-age populations in developing countries. Treatment can solely assist in preserving vision due to the irreversible nature of the disease. Timely identification and intervention substantially diminish the likelihood of blindness. Manual diabetic retinopathy diagnosis with retinal images is costly, time-consuming, and labor-intensive. The automatic detection of diabetic retinopathy disease can be possible using deep leaning methods specially CNN. In this paper, several diabetic retinopathy detection methods based on deep learning are evaluated. The accuracy of the suggested method is 94.04%.
Authors - Damini Wankhede, Shubham Kishor Kadam, Utkarsha Wanjari Abstract - Quantum computing is a ground-breaking new technology, and its disruptive potential can affect workforce management and HR analytics. This paper talks about its prominent role in addressing new challenges, including workforce honing, talent management, employee retention, cobalt matrix personalization, and diversity management. Organizations can leverage quantum algorithms to optimize their allocation of resources, recruitment and finding patterns in multidimensional datasets for better decision-making. These innovations are not only improving operational efficiencies — they are also facilitating more personalized and equitable approaches to the workforce. However, the addition of quantum computing to HR systems poses its own set of problems. Technical roadblocks, like high infrastructure costs and a shortage of skilled workers, prevent wider adoption. HR schedules powered by automation can lead to biased results when data privacy and fairness of algorithms-related ethical concerns are addressed. Implementing such technology is beset by further complications like organizational readiness and change resistance needing to be effectively planned for and managed. The paper recommends actionable recommendations for HR leaders, including phased integration, investment in quantum literacy, and the establishment of pilot projects. Further studies should employ realistic architectures that prioritize scalability, cost-efficiency, and the reduction of algorithmic biases that make quantum solutions more widely accessible and equitable. Because of that, quantum computing can change the way HR analytics are performed, and new opportunities for diverse industries emerge.
Authors - Rohini Chavhan, Michael Savariapitchai, Priyanka S. Dhore Abstract - Hospital administrators contribute substantially to healthcare quality advancement through their dual performance as operational managers and strategic leaders who drive changes. This study examines the multifaceted responsibilities of hospital administrators who enhance patient care quality by implementing leadership roles and executing policies and managing resources and developing their workforce. This study surveyed healthcare workers across the Sawangi region through 104 structured questionnaires which measured training delivery and resource distribution along with communication channels and leadership activities. The analysis of administrative behaviors against quality improvement outcomes utilized Spearman's Rank Correlation with Independent Samples t-tests. The research demonstrates that standardized staff workshops together with adequate financial resources and well-defined communication goals lead to improved staff involvement as well as superior patient security alongside superior clinical outcomes. Administrators who underwent formal training displayed superior abilities to lead continuous quality improvement efforts. The study demonstrates how hospital administrators drive patient-centered and safe healthcare delivery systems through strategic leadership execution which connects organization objectives to evidence-backed procedures. Strong leadership development along with policy support stands essential for maintaining quality care at high levels within transforming healthcare systems.
Authors - Pranesh P, Adwaitha K Raj, Durga Lashmi C.V Abstract - Many individuals are interested in the Unified Payments Interface (UPI) technology since it is secure, user-friendly, and widely used in India. UPI (Unified Payments Interface) is incredibly popular in India, with millions of users and billions of transactions processed daily. This study explains about the UPI usage and the awareness about the migrant workers in Tamil Nadu. This study consists of 100 responses from the migrant workers, and their opinion towards the UPI usage. Most of the people think that the UPI usage is convenient for them to use in their smartphone. This study also examines the UPI preference of the respondents and their purposes. It also has the dominant results that all the respondents are aware of the UPI systems and pursuing it. This study has also consisted an insight of the digital literacy of the migrant workers. It also brings us the challenges that are faced by the people at the time of using UPI. UPI, the Unified Payments Interface, has been experiencing intermittent technical issues leading to partial transaction declines, according to the National Payments Corporation of India (NPCI). These issues can cause problems with fund transfers, payments, and app functionality.
