International Journal of Innovation Studies
The International Journal of Innovation Studies (IJIS) is a peer-reviewed academic journal that focuses on enhancing the comprehension and application of innovation across multiple fields and disciplines. It provides a forum for researchers, scholars, practitioners, and policymakers to discuss groundbreaking research, theories, methodologies, and practical insights related to innovation. In addition to publishing high-quality research articles, IJIS also seeks to foster interdisciplinary collaboration and dialogue. By bringing together diverse perspectives and expertise, the journal aims to drive forward the frontiers of innovation studies, offering valuable contributions to both theoretical and practical advancements in the field.
FEDERATED LSTM FRAMEWORK FOR REAL-TIME INSULIN PREDICTION USING EDGE-TO-CLOUD CGM INTEGRATION
Background: Effective diabetes management relies heavily on timely and accurate insulin prediction, particularly with the increased use of Continuous Glucose Monitoring (CGM) systems. However, conventional centralized machine learning (ML) approaches often require the transfer of sensitive health data to remote servers, raising concerns about patient privacy, high communication overhead, and limited adaptability to individual glucose patterns. Problem: Existing prediction models struggle to balance personalization, privacy, and computational efficiency. Centralized learning lacks scalability and exposes raw data, while traditional models like decision trees or statistical regressors fail to capture the temporal complexity inherent in CGM data. Methods: This study proposes a federated learning (FL) framework for real-time insulin prediction, utilizing Raspberry Pi devices as edge nodes for local data processing and model training. Each client trains a lightweight Long Short-Term Memory (LSTM) model on individual CGM sequences. The cloud server aggregates model weights using a secure federated averaging algorithm, avoiding raw data transfer and enabling decentralized model optimization. Results: The proposed federated LSTM approach was evaluated using simulated multi-client CGM datasets and compared against baseline models: centralized LSTM and decision tree. Results demonstrate superior performance by the federated model, achieving higher accuracy (91.4%), F1-score (90.0%), and lower mean squared error (3.75), while maintaining communication efficiency and strong personalization across clients. Conclusion: This work demonstrates the feasibility of deploying a scalable, privacy-preserving, and accurate insulin prediction system using federated deep learning (DL) and edge computing. The proposed architecture supports real-time glucose monitoring and personalized insulin management, offering a practical solution for decentralized digital healthcare.
Federated Learning; LSTM; Insulin Prediction; Edge Computing; Continuous Glucose Monitoring (CGM); Privacy-Preserving Healthcare
Prabaharan J, Dr.S.Kevin Andrews, Dr.P.S.Rajakumar
Copyright (c) 2025 International Journal of Innovation Studies
FROM CLICKS TO CLASSROOMS: THE ROLE OF INNOVATIVE SOCIAL MEDIA STRATEGIES IN SHAPING HIGHER EDUCATION CHOICES
In an era where students engage with universities through screens before ever visiting campus, social media has become more than a marketing tool and it is a space where perceptions, trust, and decisions are formed. This research investigates how innovative social media strategies influence both student engagement and the process of selecting higher education institutions. The study is based on a selected sample of 164 students from diverse programs, where Multiple Linear Regression (MLR) was applied to test whether interactive content, immersive storytelling, and stakeholder feedback predict cognitive engagement and knowledge-sharing behaviours. The results of this research revealed a significant positive relationship, with interactive content emerging as the strongest predictor of student engagement. Then, Structural Equation Modelling (SEM) was used to explore a more comprehensive framework, examining how trust, transparency, and content credibility mediate the connection between social media strategies and institutional choice. The SEM results showed good model fit (CFI = 0.95, RMSEA = 0.058), confirming that these mediating factors play a crucial role in transforming social media interactions into concrete enrolment decisions. The findings emphasize the need for higher education institutions to communicate authentically, provide credible information, and create opportunities for students to contribute and see real feedback reflected online. The study contributes to the existing literature by integrating engagement theory with decision-making models, offering a dual-pathway approach that can guide universities in designing social media strategies that are not just visible but meaningful in shaping student perceptions, strengthening institutional trust, and thereby supporting informed educational choices.
Social media strategies, student decision-making; student engagement, higher education choice
EXPLAINABLE MACHINE LEARNING FOR DYNAMIC PRICING IN FAST-CHANGING RETAIL ENVIRONMENTS
Dynamic pricing has become a critical strategy in fast-changing retail environments where demand patterns, customer behavior, and competitive conditions evolve rapidly. While machine learning models have shown strong performance in optimizing prices, their lack of transparency limits trust, adoption, and regulatory compliance in real-world retail applications. This paper proposes an explainable machine learning framework for dynamic pricing that balances predictive accuracy with interpretability. The approach integrates advanced pricing models with explainability techniques to reveal how key factors such as demand fluctuations, inventory levels, customer sensitivity, and competitor pricing influence price decisions. By providing human-understandable explanations alongside price recommendations, the framework enables retailers to make informed, accountable, and adaptive pricing decisions in real time. The model is designed to operate effectively in volatile retail settings, including online marketplaces and omnichannel platforms, where rapid changes require continuous learning and justification of pricing actions. Experimental evaluations demonstrate that the proposed explainable approach achieves competitive revenue performance while significantly improving transparency and decision confidence compared to black-box models. The findings highlight the importance of explainable machine learning in bridging the gap between algorithmic efficiency and managerial trust, supporting ethical, compliant, and sustainable dynamic pricing practices in modern retail ecosystems.
Explainable Machine Learning, Dynamic Pricing, Retail Analytics, Price Optimization, Decision Transparency, Fast-Changing Retail Environments
Mayank Atreya, Navin Chhibber, Harvendra Singh
Copyright (c) 2022 International Journal of Innovation Studies
DYNAMIC CUSTOMER PROFILING AT SCALE: CONTINUOUS SEGMENTATION FROM REAL-TIME BEHAVIORAL EVENT STREAMS
A dynamic customer profiling framework enabling continuous segmentation from real-time behavioral event streams was developed and evaluated. Traditional batch-based segmentation methods inadequately capture rapid behavioral shifts, resulting in outdated actionable intelligence. A real-time streaming architecture was implemented to ingest, process, and analyze continuous event data, enabling subsecond updates to customer profiles and segment memberships. The results demonstrated that the dynamic profiling system achieved low processing latency, high throughput efficiency, and significantly improved segmentation accuracy compared to batch models. The system effectively captured micro-behavioral variations, supported rapid transitions between segments, and provided more coherent and responsive clustering outcomes. Results confirmed continuous segmentation achieves 78% improved responsiveness and higher cluster coherence (silhouette score 0.64 vs. 0.41) compared to batch approaches, enabling real-time personalization.
Dynamic customer profiling, real-time behavioral data, continuous segmentation, streaming analytics, online machine learning, customer behavior modeling, event-driven architecture.
BRIDGING SOCIAL INTELLIGENCE AND ORGANIZATIONAL PERFORMANCE: EXAMINING THE MEDIATING ROLE OF EMPLOYEE ENGAGEMENT
In modern healthcare systems, interpersonal competence has become increasingly critical for maintaining service quality, teamwork effectiveness, and organizational sustainability. This study investigates how Social Intelligence (SI) influences Organizational Performance (OP) and evaluates the extent to which Employee Engagement (EE) mediates this relationship. Using a descriptive, cross-sectional design, data were collected from 384 healthcare professionals—including physicians, nurses, administrative staff, allied health workers, and support employees—selected through stratified random sampling. Validated scales measuring SI, EE, and OP were administered, and the responses were analyzed using SPSS and Hayes’ PROCESS Macro (Model 4). Results confirmed that SI has a positive but relatively weak direct effect on OP. EE emerged as a strong predictor of OP, demonstrating that engaged employees contribute more significantly to communication effectiveness, teamwork, service quality, and organizational growth. Mediation testing revealed that EE partially mediates the SI–OP relationship, indicating that social competencies influence performance more effectively when channelled through engagement behaviors. These findings emphasize the need for healthcare organizations to cultivate both social intelligence and engagement-supportive practices—such as meaningful job design, recognition systems, effective communication, and supportive leadership—to enhance productivity and patient-centered outcomes. The study contributes to organizational behavior literature by integrating SI, EE, and OP within a unified model relevant to healthcare environments.
Social Intelligence; Employee Engagement; Organizational Performance; Healthcare Professionals; Mediation Analysis; Workplace Behaviour; Patient-Centered Care
EXPLORING OCCUPATIONAL STRESS ACROSS INCOME CATEGORIES: A COMPARATIVE STUDY OF EMPLOYEES IN GUWAHATI CITY.
Stress is an inevitable part of modern life which affects individuals belonging to all walks of life. Stress exists in day-to-day small tasks and carries to workplace with varying complexity. Urbanization, industrial development, increasing competition and modernization in the society are contributing to increasing stress. People perceive stress when they fail to meet up to the environmental expectations and find the situation threatening or challenging. Unwaveringly, an alarming rise in psychosomatic and psychological disorders of individuals and feeling of frustration and dissatisfaction with life in general reflect the high stress being experienced by the people in the present-day world. There can be multiple reasons or causes of stress like past traumatic events, future uncertainty, increasing competition in different walks of life, role conflict etc. Employees experience moderate to high level of stress in workplace due to work deadlines, feeling of powerlessness, excessive workload, conservative company policies, composite lifestyle of people, rigorous government policies etc. Employees in both public and private sector experience stress and respond in different ways. Stress manifests in various forms of emotional disorders. (Singh and Singh 1992) found that stress gets its reflection in the form of anxiety, depression, helplessness, hopelessness and anger. The stressful people with the slightest provocation are easily induced to anger and anxiety and become unable to relax. Employees’ responses to work demands and pressures are largely influenced by their personality characteristics, and psychological and behavioural patterns, such as beliefs and values, aspiration and expectancy, need structure, attributions, locus of control, personality traits, coping skills, cognitive patterns, etc. An important personal characteristic which influences the experience of stress is “sense of control.” Control refers to the perception by the individual worker that his or her action results in expected outcomes, particularly those which are important for the workers. Sense of control has been found to have significant effect on people’s response to stress. Personality traits also play an instrumental role in building stress among employees. Traits such as perfectionism, competitiveness and agreeableness lead to more stress among teachers (Anwar S. et al. 2021)
OPTIMIZING CUSTOMER EXPERIENCE THROUGH PERFORMANCE ENGINEERING IN CALL CENTER APPLICATIONS
This study examines the function of performance engineering in enhancing customer experience in call center application settings. As customers anticipate quick, dependable, and smooth service encounters, many call centers have problems with system latency, IVR delays, CRM slowdowns, and unpredictable handling times. This study used a mixed-methods experimental design to look at the baseline performance of the system, make targeted performance engineering changes, and see how those changes affected both technical and experiential outcomes. The results showed that key performance measures had big increases, such as shorter response times, fewer delays in queues, and higher system throughput. Similarly, customer experience metrics like CSAT, call abandonment rate, and first-call resolution showed big improvements. The results show that using formal performance engineering frameworks may considerably improve call center operations, agent productivity, and overall customer happiness. This study presents a scalable framework for the ongoing enhancement of service delivery via data-driven engineering methodologies.
Performance Engineering, Call Center Applications, Customer Experience, Response Time, IVR Optimization, CRM Performance, CSAT, First Call Resolution, System Throughput, Queue Management
Copyright (c) 2019 International Journal of Innovation Studies
CALORIE ESTIMATION AND FOOD CLASSIFICATION USING DEEP LEARNING: A CULTURAL AND ARCHITECTURAL PERSPECTIVE
Traditional food tracking method remain time consuming and culturally biased for different cuisines. However, the need for an appropriate dietary monitoring system is essential for managing chronic diseases. Currently, different deep learning models integrated automated solutions, but many of those models are mainly specific towards Western-centric datasets which constrains the generalizability across diverse cuisines. This holistic review examines twelve empirical studies between 2020-2025 leveraging different models like YOLOv8, Mask R-CNN, ViT, and MobileNet which are applied for deep learning for food recognition and calorie estimation. Efficiency was evaluated on basis of the mAP, accuracy level, MAE, and real-time feasibility metrics, with diverse datasets across multi-regions of Indian, Koream, Malaysian, and Chinese cuisines. Results shows that lightweight models maybe deployment ready (e.g. MobileNetV2, YOLOv8m) but they often lack segmentation accuracy. Vision Transformers exhibits higher accuracy but constrained by high computation cost. A balanced performance is provided by volume-based, API-driven, and regression-based calorie methods. This study reveals the importance of culturally inclusive datasets, better multi-label classification of food items, and explainable AI to develop robust and globally scalable food recognition systems for health applications by considering multi-cuisines across Asian regions.
A NOVEL HYBRID APPROACH FOR PADDY LEAF DISEASE SEVERITY CLASSIFICATION USING SPATIAL BIDIRECTIONAL CONVOLUTIONAL NEURAL NETWORK CLASSIFIER
Bacterial, viral, and fungal pathogens that cause Paddy Leaf Diseases (PLD) result in significant yield losses in rice crops. Early detection and effective management are crucial for ensuring optimal growth and productivity. For severity analysis, they used the publicly accessible Mendeley Rice Leaf Disease Dataset, which contains images of the illnesses Brown Spot, Blight, Blast, and Tungro. Gaussian Blur is a multi-step pre-processing technique that smoothens noisy images by identifying regions where intensity changes rapidly.Further image segmentation using Otsu's thresholding, which automatically determines the optimal threshold to separate the foreground and background based on image histogram analysis and feature extraction using an autoencoder, can be used to learn high-level features, and Scale-Invariant Feature Transform (SIFT) can capture local spatial information, enhancing the overall feature set for downstream tasks. Select KBest for the feature selection method that selects the top k features, evaluates each feature's relevance to the target variable, and retains the most informative ones. The proposed Spatial Bidirectional Convo Neural Feedforward Net (SBCNFN) integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Feedforward Neural Networks (FNN) for nonlinear feature mapping, and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for capturing bidirectional temporal dependencies in spatial sequences. Grid Search optimizes the model by fine-tuning hyperparameters to achieve the best performance. Python tools and libraries were used for model development. The proposed model used to classify the severity levels of paddy leaf disease, like Mild 98.62%, Average 96.7%, Severe 98.12%, and Profound Accuracy of 98.66%. The findings demonstrate that the suggested approach works better than the current approaches by offering more precise and trustworthy estimates of disease severity.
Paddy Leaf Disease, Gaussian Blur, Otsu’s Thresholding, Autoencoder, Spatial Bidirectional Convo Neuro Feedforward Net Classifier, Grid Search, Disease Severity Prediction, Deep Learning (DL), Python.
IRIS-DRL FOR SECRECY IN RIS-ASSISTED UAV NETWORKS
This paper introduces a novel deep reinforcement learning (DRL) framework Intelligent Reconfigurable Integrated Security (IRIS) designed to enhance the secrecy and efficiency of communication in Reconfigurable Intelligent Surface (RIS) assisted Unmanned Aerial Vehicle (UAV) networks. IRIS jointly optimizes UAV flight trajectories, RIS phase configurations, and power allocation strategies to strengthen physical-layer security in environments susceptible to eavesdropping and signal interference. Unlike traditional (DRL) methods such as Proximal Policy Optimization (PPO), which often struggle with high-dimensional optimization tasks in dynamic wireless systems, IRIS employs an adaptive exploration-exploitation mechanism tailored for secure UAV operations. The framework dynamically responds to environmental changes, maximizing the secrecy rate while minimizing energy consumption and latency. Simulation results, conducted using MATLAB, demonstrate that IRIS significantly outperforms conventional approaches across multiple performance indicators, including secrecy rate, convergence speed, and energy efficiency. A comprehensive sensitivity analysis of key hyperparameters further validates the model's robustness across various deployment scenarios. The results highlight IRIS as a promising algorithm for secure communication in UAV-enabled applications such as disaster relief, critical infrastructure monitoring, and next-generation IoT deployments.
Intelligent Reconfigurable Integrated Security (IRIS), Reconfigurable Intelligent Surface (RIS), Unmanned Aerial Vehicle (UAV), Deep Reinforcement Learning (DRL).
Candida.Y, Dr.K.Sudhaman, Dr J.Ann Roseela, Dr C.Sharanya
EMOTION-AWARE REINFORCEMENT LEARNING FOR ADAPTIVE VIDEO GAMING THERAPY
Reinforcement Learning (RL) in Video Gaming Therapy, combined with emotion detection, dynamically adapts game environments to optimize therapeutic outcomes by personalizing experiences based on real-time emotional responses.A key challenge is integrating accurate emotion detection with reinforcement learning to personalize video game therapy effectively while ensuring real-time responsiveness and user engagement.This model presents a novel framework for an Inverted Residual Adaptive Convo-Depth NeuroNet that integrates Emotion Detection and Reinforcement Learning (RL) to create a therapy system for an adaptive video gaming therapy system. The Emotion Detection Module uses a pre-trained MobileNetV2 to analyzethe data and classify the emotional state, such as Happy, Fear, Angry, Disgust, Neutral, Sad, and Surprise. The Reinforcement Learning Module leverages Deep Q-Network (DQN)algorithms, where the state combines the player's emotional state and game performance. The RL model adjusts the game difficulty and provides personalized rewards. This methodpurposes to create a more immersive and effective video game therapy environment, where the game evolves in response to both emotional and performance rewards, ensuring a personalized and engaging experience.The Python-based simulation achieved performance metrics ranging from 83% to 85%, including F1-score, precision, recall, and accuracy, validating the effectiveness of the emotion-aware adaptive therapy system.
Reinforcement Learning (RL),Video Gaming Therapy, Emotion Detection, Convolutional Neural Network (CNN), Python, Deep Q-Network (DQN).
FOSTERING ENTREPRENEURSHIP INTENTIONS: THE ROLE OF WOMEN EDUCATIONAL INSTITUTION
India's economic development is extremely rapid compared to other countries. India has the world's second-largest economic development. Entrepreneurship contributes significantly to economic growth and development. Today, young people, particularly women from Generation Z, are aspiring entrepreneurs. The educational institution has an important role in fostering entrepreneurial intentions. This research examines the effect of entrepreneurial education on entrepreneurial intentions among the students. For this, a Stratified sampling method was used to collect the data. A total of 370 samples were collected from students who are studying in various women's colleges within Chennai city. The statistical toolssuch as descriptive statistics and Structured Equation Model (SEM), where the results revealed that personal attitude, subjective norms, self-efficacy, and educational institutions significantly influence entrepreneurial intentions.
Educational Institution, Entrepreneurship, Gen Z Women, Psychological, Entrepreneurial intentions.
MARKETING IN THE AGE OF AI: “OPPORTUNITIES, INTERVENTIONS, AND IMPACTS ON SOCIETY"
Marketing is changing due to the role of artificial intelligence (AI) on both the strategic and operational structures of organizations. As the literature shows, AI is significantly enhancing marketing efficiency, through improved customer segmentation, strategic personalization, and more effective decision-making, while simultaneously raising significant challenges in areas of ethics, transparency, and social influence. Synthesized from an in-depth review of both conceptual and empirical research (2020–2025), this research article identifies broad themes such as AI-led digital transformation, integrating sustainability, human and AI collaborative intelligence, and the impact of generative AI on creative marketing processes. These results indicate that, in addition to offering operating efficiencies, AI potential must be guided by ethical principles to avoid bias, job loss, and privacy breaches. The research winds up by advocating for subsequent research agendas that focus on the co-evolution of AI technology and human governance to achieve optimal innovation, trust, and societal benefit in marketing practice.
Artificial intelligence, digital transformation, integrating sustainability, human and AI collaborative intelligence, generative AI.
Mrs. Sivaranjani. S, Ms. R. Rubini Mahamaya, Dr. Y. Kalaivani,
TRANSFORMING ENTERPRISE IT OPERATIONS THROUGH SERVICENOW SAAS IMPLEMENTATIONS: A CASE-BASED FRAMEWORK FOR AGILE DIGITAL TRANSFORMATION
The expanding operational agility and digitalization demands had compelled businesses to move to the cloud-based platform like ServiceNow to transform IT services. This paper had explored the changes in the IT operations in organizations by implementing the ServiceNow SaaS and how this has formed an evidence-based framework to promote agile digital transformation practices. A semi-structured interview, document analysis, and observations of the limited number of processes had been used to collect data by applying a comparative case-study approach to technology, healthcare, and financial service organizations. The results had shown great improvements on incident-resolution time, automation of workflow, compliance with SLA and user satisfaction after implementation. The research had also found leadership support, adoption of agile delivery, structured governance, and systematic training as important success factors, and employee resistance and change-management gaps were found to be major challenges. In general, the study showed that the ServiceNow facilitated the process of modernizing IT service delivery and improving process efficiency in enterprises assuming a strategic, human-oriented, and iterative process of transformation.
ServiceNow, SaaS implementation, Digital transformation, IT service management (ITSM), Agile methodology, Workflow automation, Enterprise IT operations
Copyright (c) 2020 International Journal of Innovation Studies
INNOVATIVE S`TRATEGIES FOR ENHANCING SUSTAINABILITY AND CORPORATE SOCIAL RESPONSIBILITY IN E-COMMERCE: A DATA-DRIVEN APPROACH (WITH REFERENCE TO CHENNAI CITY)
The study aims to contribute to both academic literature and practical business strategies by identifying and analyzing innovative, data-backed CSR and sustainability practices in the e-commerce sector. The acumens will benefit policymakers, environmental advocates, tech developers, and e-commerce firms aiming balanced growth with social responsibility and environmental stewardship. Data were collected from a sample of 580 respondents through personal interview, using simple random sampling method. Statistical modeling using Structural Equation Modeling (SEM), regression analysis, and data-driven insights from consumer surveys and environmental impact assessments, chi-square test, one way ANOVA, SEM analysis, sentiment analysis method used to determine the relationship between demographic characteristics and sustainable development and CSR. It provides a foundational understanding of the need for data-driven sustainability and CSR in e-commerce, particularly in the context of Chennai city. This research will bridge the gap between sustainability theory and industry practice, providing insightful strategies for a greener, more responsible e-commerce sector.
Corporate Social Responsibility, Sustainable Practices, E-Commerce Sector, , Environmental Impact Assessments, Structural Equation Modeling,)
Dr. M. Kavitha, Mrs. Priya V S, Mr. Rithik Bosco J
A STUDY ON CUSTOMER PERCEPTION TOWARDS SMART GADGETS IN CHENNAI CITY
The aim of this paper is to analyze the consumer perception towards Smart Gadgets in Chennai city and second, to determine Factors to be considered while buying decision Smart gadgets among college student in South Chennai. This study revealed that maximum Smart gadgets users are female and the majority of the respondents have their family monthly income level above Rs.40000. By applying Factor Analysis, it is determined that the majority of the respondents are influenced by Social influence factor to buy smart gadgets. Survey was limited to Chennai City only, so it cannot be generalizes to all the centers.
Smart gadgets- Customer Perception- Buying Decision –Suitability- Social influence.
ASSESSING MANAGERIAL TRAINING IN MAHARATNA COMPANIES IN INDIA USING DONALD KIRKPATRICK’S MODEL
Public Sector Undertakings (PSUs), particularly Maharatna companies such as ONGC, IOCL, NTPC, SAIL, CIL, BHEL, and GAIL, play a pivotal role in India’s industrial growth and global competitiveness. To maintain their leadership position, these organizations invest significantly in managerial training and leadership development. This article applies Donald Kirkpatrick’s Four-Level Training Evaluation Model to assess the effectiveness of managerial training programs in Maharatna companies, with a special emphasis on Level 4 (Results), which measures organizational impact. The analysis highlights how training influences key performance outcomes, including productivity, financial performance, safety, innovation, employee engagement, customer satisfaction, and sustainability. Case examples from NTPC, SAIL, and IOCL demonstrate tangible benefits such as reduced downtime, improved industrial relations, and cost savings in logistics. The study also discusses challenges in measuring Level 4 outcomes, such as attribution and data availability, and suggests best practices, including ROI calculations, balanced scorecards, and longitudinal studies. Findings affirm that aligning training with strategic objectives ensures not only enhanced managerial competence but also measurable organizational gains, thereby reinforcing the role of Maharatna companies as drivers of India’s economic progress
Ajay Agarwal; Dr. Manoj Bhatia; Dr. V. K. Jain
KERNEL DISCRIMINANT OPTIMIZED DEEP CONVOLUTIONAL NEURAL LEARNING CLASSIFIER FOR PREDICTIVE ANALYTICS
Data mining (DM) is an emerging field that focuses on applying big datasets of mining techniques to enhance the results and learning process. It involves find out the patterns and correlations within huge sets of data with the aim of forecasting future outcomes. Predicting future outcomes for large datasets is a critical application of data mining. By analyzing historical admission data and other relevant factors, existing predictive models have been developed to forecast the likelihood of various outcomes. However, these models faced challenges when applied to large datasets, affecting their predictive effectiveness. To improve prediction accuracy, a novel model called Kernel Discriminant Optimized Deep Convolutional Neural Learning Classifier (KDODCNLC) is introduced. The primary aim of the KDODCNLC model is to achieve higher prediction accuracy and reduced time consumption. In this model, data is first collected during the data acquisition phase for classification purposes. The proposed DCNN comprises the different layers. At first, data is fed into input layer from Student Performance Prediction dataset. The input data then moves to hidden layer 1, where preprocessing is performed to remove unwanted data. The preprocessed data is passed to hidden layer 2. In this max-pooling layer, feature selection is conducted to identify significant features while discarding others. Then, it is sent to a fully connected layer for classification to reduce the features. In this layer, data is determined by a kernelized discriminant function to provide prediction outcomes. To minimize classification error, hyperparameter optimization in Convolutional Neural Networks (CNNs) is essential, involving fine-tuning of network parameters. The KDODCNLC model employs the Rain-fall optimization algorithm for hyperparameter optimization, aiming to reduce error and enhance prediction accuracy. To conclude, the accurate prediction outcomes are attained at output layer, enabling predictions through less time consumption. Experimental evaluation obtains the different metrics. Outcome quantitatively demonstrates KDODCNLC model enhances the prediction accuracy by 5%, precision by 5%, and recall by 3% and F1-Scoreby 4%, then the conventional methods. Additionally, KDODCNLC achieves a 27% reduction in prediction time than the existing methods.
Data mining, prediction, deep convolutional neural network, kernelized discriminant function, hyperparameter optimization, rain-fall optimization algorithm
P. Christy Jeyakumar, Dr. S. Ismail Kalilulah, Dr. V. N. Rajavarman
IMPACT OF FINANCIAL INCLUSION SCHEMES ON LIVES AMONG TRIBAL POPULATION IN KODAGU DISTRICT IN KARNATAKA
The study focuses on various aspects of financial literacy among the tribal population of Kodagu district in Karnataka. The primary data is collected through survey methods, employing an interview schedule, supplemented by an extensive review of existing literature. The key parameters of the study are ; socio-economic distribution, awareness of income- generating schemes initiated by the government, utilization of financial schemes and services, and the overall impact of these initiatives on the lives of tribal communities in Kodagu district. The research is based on a combination of primary data and secondary data sources. The study reveals that the majority of the respondents from the tribal population are male, with labour being their predominant occupation. A significant proportion of the community remains unaware of government-supported income-generating schemes, and the utilization of available financial schemes and services is relatively low. The analysis further indicates that awareness levels have a direct influence on the effectiveness and impact of these schemes on the socio-economic conditions of the tribal households.
Financial inclusion, Financial Literacy, Awareness, Income-generating schemes, Socio-economic determinants, Impact on lives
LEGAL AND ETHICAL IMPLICATIONS OF AI-INDUCED JOB DISPLACEMENT IN MULTINATIONAL CORPORATIONS: A COMPARATIVE ANALYSIS BETWEEN THE UNITED STATES AND INDIA
The rapid advancement of Artificial Intelligence (AI) has significantly transformed global industries, offering unprecedented efficiency and innovation while simultaneously raising concerns over workforce disruption. This research investigates the legal and ethical implications of AI-induced job displacement within multinational corporations (MNCs), providing a comparative analysis between the United States and India over the last five years (2021–2025). This study evaluates sector-wise job displacement trends, growth rates, and their socio-economic impacts. Utilizing cumulative frequency analysis, year-on-year percentage growth, and sectoral impact assessment, the paper highlights how early AI adoption in the U.S. has led to steady job displacement, whereas delayed but rapid AI integration in India has resulted in sudden employment shocks. The research further explores employment discrimination laws, data privacy frameworks, intellectual property (IP) concerns, and algorithmic bias mitigation, identifying critical gaps in both countries’ regulatory landscapes. Ethical considerations such as fairness in automated decision-making, employee dignity, and inclusive growth are discussed in depth. The findings suggest that without robust policy interventions, reskilling programs, and global ethical AI governance, AI-driven displacement could intensify labor inequalities. The paper concludes with targeted recommendations for governments, MNCs, and international bodies to foster responsible AI adoption while ensuring workforce resilience.
Artificial Intelligence, AI-driven Automation, Job Displacement, Multinational Corporations, Employment Laws, Data Privacy, Algorithmic Bias, Sectoral Analysis, Year-on-Year Growth, Cumulative Frequency, Ethical AI Governance, Reskilling, Socio-Economic Impact, Comparative Study, United States, India
EMPOWERING WORKING WOMEN THROUGH FINANCIAL LITERACY: UNVEILING THE IMPACT ON INVESTMENT DECISIONS IN HIGHER EDUCATION
This study investigates the role of financial literacy—including financial knowledge, experience, and management skills—in shaping the investment decisions of working women in higher educational institutions. Using a sample of 600 respondents, this research applies statistical analysis (SPSS V.26) to examine the relationship between financial literacy and investment decisions. Findings indicate that financial management skills are the strongest predictor of investment decisions, followed by financial knowledge and experience. This paper contributes to the literature by addressing the gendered financial behavior gap, offering actionable insights for policymakers and educators to improve financial education for women, thereby empowering them to make informed investment choices. The findings have practical implications for promoting gender equity in financial decision-making.
Financial Literacy, Investment Decisions, Working Women, Gendered Financial Behavior, Financial Education, Financial Empowerment
Sudha Pandey, Sr. Prof. (Dr.) Manjula Jain
EFFECT OF VIDEO-ASSISTED HEALTH TEACHING ON KNOWLEDGE REGARDING TAI-CHI EXERCISE FOR STRESS MANAGEMENT.
Stress has become a major issue in today’s society, with many individuals facing rising levels of stress due to various obligations, such as work responsibilities, academic pressures, and family duties. College students, in particular, are highly susceptible to stress and frequently experience its negative effects. Research by Brougham et al. highlights that factors like daily challenges, financial concerns, and academic expectations significantly contribute to elevated stress levels among this group. The simultaneous presence of multiple stressors can increase the likelihood of stress-related symptoms, ultimately affecting students' well-being and academic success. Tai Chi is a form of exercise that involves gentle, flowing movements, controlled breathing, and mindfulness. Its slow and graceful motions make it suitable for people of all ages and fitness levels. This accessible practice promotes relaxation, reduces stress, and supports overall physical and mental well-being. Objectives of the study: 1) To identify the knowledge regarding TAI CHI exercises for stress management among junior college students. 2) To measure the effect of health teaching on knowledge regarding TAI CHI exercises for stress management. 3) To find the association of the effect of knowledge regarding TAI CHI exercises for stress management with selected demographic variables. The method of study was an evaluative study. 60 samples were selected for the study by a non-probability purposive sampling technique. Data collection was done by using questionnaires. Pre-test was conducted, after the pretest, intervention was given to the samples, and post-test was conducted after the health education. The data was analyzed by descriptive and inferential statistics. The reliability was assessed using the test and retest method. Pearson's correlation coefficient was found to be 0.86. Hence, the tool of the study was found to be reliable. Result shows that descriptive and inferential statistics are used in analysis. The researchers applied a paired t-test to assess the effect of health teaching on knowledge regarding TAI CHI exercises for stress management. The average knowledge score in the pretest was 6.5, which increased to 15.1 in the posttest. T-value for this test was 20.6 with 59 degrees of freedom. The corresponding p-value was small (less than 0.05), and the null hypothesis was rejected. It is evident that the knowledge regarding TAI CHI exercises for stress management among junior college students improved significantly after health teaching. Conclusion was to assess the effect of health teaching on knowledge regarding TAI CHI exercises for stress management was shown to be effective. Hence, the corresponding p-value was small (less than 0.05), and the null hypothesis is rejected. It is evident that the average knowledge score in the post-test is significantly higher than the pre-test.
TAI CHI exercises, Stress management, knowledge, junior college students, Video-assisted health teaching
Mrs. Jayashree Ohol, Shani Shaji, Dr. Mrs. Khurshid Jamadar, Dr. Mrs. Sadhana Adhyapak
TRAVEL AS A BRIDGE: EXPLORING THE POWER OF CULTURAL TOURISM
Cultural tourism has emerged as a powerful vehicle for fostering intercultural understanding, preserving heritage, and promoting economic development. The study “Travel as a Bridge: Exploring the Power of Cultural Tourism” investigates how tourism functions not only as a leisure activity but also as a conduit for cultural exchange and community empowerment. By examining global trends, case studies, and scholarly literature, this research highlights the ways in which travelers engage with local traditions, arts, languages, and heritage sites, while communities benefit through income generation, employment, and cultural preservation. The study also addresses challenges such as cultural commodification, over-tourism, and the sustainability of tourism- driven economies. The findings underscore that when managed responsibly, cultural tourism serves as a bridge between societies, enhancing mutual understanding, fostering social cohesion, and contributing to both cultural vitality and economic prosperity.
Cultural Tourism, Intercultural Exchange, Heritage Preservation, Economic Impact, Sustainable Tourism, Community Engagement, Global Travel.
ASSESSING CITIZEN SATISFACTION AND SERVICE EFFICIENCY IN E-GOVERNANCE: A STUDY OF COMMON SERVICE CENTRES IN KERALA
The rapid advancement of digital technologies has transformed public administration, enabling governments to deliver services more efficiently and transparently. This study assesses citizen satisfaction and service efficiency in the functioning of Common Service Centres (CSCs) under the e-Governance framework in Kerala. The primary objective is to evaluate how effectively CSCs facilitate citizen-centric services, reduce bureaucratic barriers, and promote digital inclusivity across rural and urban populations. A structured questionnaire was administered to a sample of 385 respondents representing diverse demographic and socio-economic backgrounds across various districts of Kerala. The study employed both descriptive and inferential statistical analyses using SPSS to examine the relationship between service quality, accessibility, transparency, responsiveness, and overall satisfaction levels. Findings reveal that citizens perceive CSCs as convenient and accessible platforms for availing government services, especially in rural areas where digital infrastructure remains limited. However, variations in service efficiency were observed based on technical readiness, operator competency, and network reliability. The study further highlights that transparency, timely service delivery, and staff professionalism significantly influence citizen satisfaction. Despite notable progress, challenges persist in terms of system downtime, limited awareness among beneficiaries, and inconsistent service quality. The research concludes that enhancing technological infrastructure, continuous capacity-building of service providers, and citizen feedback integration are vital for strengthening e-Governance performance. The study contributes to policy formulation aimed at improving the effectiveness and inclusiveness of digital governance initiatives in Kerala and offers a replicable model for similar contexts across India.
E-Governance, Common Service Centres (CSCs), Citizen Satisfaction, Service Efficiency, Digital Inclusion, Kerala.
A STUDY ON THE AWARENESS ABOUT SELF-HELP GROUP AMONG IT’S MEMBERS WITH SPECIAL REFERENCE TO HDFC BANK, AMBATTUR.
The present study, titled "A Study on the Awareness About Self-Help Group Among its Members With Special Reference to HDFC Bank, Ambattur” explores the level of awareness among Self-Help Group (SHG) members regarding banking procedures and government schemes. The research specifically investigates whether SHG members understand the process of obtaining loans from banks and the intended purpose of such loans. A key objective is to assess the extent of their awareness about microfinance and banking terms and conditions. Additionally, the study aims to identify the effectiveness and reach of current government schemes available to SHGs. Primary data was collected through a structured questionnaire from a sample of 52 SHG members in Ambattur, supported by information from local panchayat offices, newspapers, and magazines. The analysis was conducted using a percentage model. The findings indicate that whether SHG members in Ambattur show a strong understanding of banking terms and conditions, their awareness of government schemes remains limited.
Awareness in bank loan, Government Schemes and Policies.
OPTIMIZATION OF IMAGE SEGMENTATION THROUGH THE TRANSPORTATION PROBLEM FRAMEWORK
Image segmentation is one of the key activities in image processing in which an image is divided into areas of significance to be analysed. Conventional approaches are based on clustering, thresholding or edge detecting which tend to be ineffective when noise is present, the illumination varies, or the object has complicated features. In this work, we suggest applying the model of optimal segmentation based on the use of the transportation problem which is a classical model of operations research. Segmentation is formulated as a cost minimization by considering pixel distributions as supply point and expected segment models as demand point. The model of transportation makes certain groups of pixels are optimally allocated to areas with insignificant spatial or intensity distortion. It uses the ideas of optimal transport theory and offers a strong and mathematically based theory of segmentation used in medical imaging, satellite image and pattern recognition. Image segmentation is a critical part of image processing that focuses on the division of an image into meaningful components in order to analyse the same. The traditional techniques which aim at clustering, thresholding, or edge detection tend to fail in case of noise, or contrast in the lighting across the image, or complex topology. The new approach proposed by the current study is that of developing segmentation as a problem in transportation, which is a traditional model of operation research. In this model pixel intensities are distributed and supply nodes are modelled and the prototypes of the desired segment are desired demand nodes. The intention of this is to lessen the total transportation cost, distortion of space or intensity and therefore optimal distribution of pixels in regions is realized. The model is well entrenched in the optimal transportation theory; therefore, providing a mathematically sound and robust solution of the segmentation issues. Other restrictions that can be easily considered in the approach include spatial continuity or texture similarity to give it more flexibility. The experiments indicate that it is capable of doing well in many areas including medical imaging, satellite images analysis and pattern recognition with better segmentation accuracy and interpretability than the traditional methods.
Dr.Jyothi.P, Dr.Uttamkumar YV, Prof.Champa T, Prof. Shivaraj Kumar, Dr.V. Silambarasan
Meta-Analysis of Public–Private Partnership Models and Their Effectiveness in Funding Affordable Housing Projects Across the U.S.
This study examines the effectiveness of Public–Private Partnerships (PPPs) in financing affordable housing projects across the United States through a meta-analysis of 29 cohorts published between 2015 and 2025. The findings reveal that PPPs generally improve housing outcomes, with a pooled hazard ratio of 1.10, indicating a significant positive effect compared with conventional public funding models. However, substantial heterogeneity across studies highlights that outcomes vary widely depending on governance quality, financing mechanisms, and policy environments. Strong positive effects were observed in contexts where fiscal accountability, risk-sharing, and policy support were well established, while weaker or null effects appeared where governance or financial frameworks were less robust. The analysis identifies tax-incentive and equity partnership models as the most effective in urban areas, while lease and concession models were more common in rural or small-scale projects. Results also point to significant challenges, including potential equity concerns and the risk of affordability erosion when subsidies expire. Overall, PPPs represent a promising mechanism for mobilizing private capital, enhancing efficiency, and diversifying housing finance, but their success is contingent on careful design, strong regulatory oversight, and context-specific implementation. These findings provide practical implications for U.S. housing policy, particularly in relation to the Housing Supply Action Plan and the Infrastructure Investment and Jobs Act, and underscore the need for long-term monitoring and equity-focused strategies.
INTEGRATING ARTIFICIAL INTELLIGENCE INTO MODERN ACCOUNTING AND AUDITING: OPPORTUNITIES AND CHALLENGES
The drastic development of digital technologies in a major way has changed the accounting area to move away from the conventional bookkeeping practices to intelligent, automated, and data, driven systems. The paper discusses how accounting and auditing incorporate AI, efficiency, accuracy, transparency, and strategic decision, making being some of the aspects affected by the change. The AI, driven tools make possible financial analysis in real, time, minimise manual errors, and facilitate regulatory compliance in continuously changing environments. The smart machines that perform routine work among the staff of accounting professionals free the latter for financial forecasting, interpreting analytical insights, and strategic planning, which in turn strengthen the quality, trust, and timeliness of business decisions. The research also highlights the use of progressive technological devices in the form of machine learning, predictive analytics, and natural language processing becoming the major factors contributing to the improvement of financial reporting, fraud detection, auditing, and performance assessment. These tools give accountants and auditors the power to process, interpret and visualize bigger datasets in less time with higher accuracy. However, the introduction of these technologies has its drawbacks in the form of challenges related to data protection, ethical issues, implementation costs, and the requirement for continuous professional training to be able to manage and evaluate AI, driven systems properly. The overall results point to the fact that AI is not substituting the accounting and auditing staff but rather upgrading their skillsets, thus enabling them to transition into more analytical, advisory, and value, focused roles that are in line with the needs of modern businesses in the digital environment
Artificial Intelligence, Auditing, Real-time Monitoring, Data Privacy, Digitalization
EFFICIENCY MEASUREMENT OF SAIL’S INTEGRATED STEEL PLANTS USING THE DATA ENVELOPMENT ANALYSIS MODEL
This study evaluates the operational efficiency of five integrated steel plants under Steel Authority of India Limited (SAIL) using Data Envelopment Analysis (DEA). The output-oriented DEA model under Variable Returns to Scale (VRS) assesses the relative efficiency of Bhilai, Bokaro, Rourkela, Durgapur, and Burnpur steel plants from 2018-19 to 2022-23. Input variables include iron ore, coke rate, slag rate, and total metallic input, while output variables comprise sintering machine yield, productivity, blast furnace productivity, crude steel output, and mill yield. This study aims to evaluate the relative efficiency of five SAIL steel plants using DEA and identify best practices to improve their performance. The study focused on using secondary data from annual reports, sustainability reports, and operational records of each plant. The input and output variables are carefully selected to reflect the steel production process. The ANOVA test confirms significant differences in input variables across the plants. DEA results reveal significant variations in efficiency scores among the plants. ISP Burnpur and Rourkela Steel Plant emerge as the most efficient plants, while Bhilai Steel Plant and Bokaro Steel Limited show scope for improvement. The study demonstrates the effectiveness of DEA in evaluating the operational efficiency of SAIL's steel plants. The findings provide valuable insights for improving resource utilization, reducing input costs, and boosting productivity across SAIL's steel plants.
Data Envelopment Analysis, SAIL, Steel Plant Efficiency, Raw Material Utilization, Technical Efficiency, Operational Benchmarking, Indian Steel Industry.
SECURING LOW-POWER EDGE AI: A VULNERABILITY ANALYSIS AND CYBERSECURITY FRAMEWORK FOR RESOURCE-CONSTRAINED DEVICES
The rapid deployment of Edge AI systems powered by low-power technology has created new operational challenges in healthcare facilities, autonomous vehicles, and industrial IoT devices, leading to increased security vulnerabilities. Security mechanisms require strong computational power, which these efficiency and real-time-oriented systems do not possess by design. Predictable threats against Edge AI systems are now more prevalent due to their limited computing power. Security frameworks prove inefficient when used in Edge AI environments, creating a vital protection weakness. This research paper focuses on a new lightweight cybersecurity system designed for Edge AI systems that lack sufficient resources. The proposed security construct enables the deployment of effective cryptographic systems and AI-based anomaly identification along with defence mechanisms suitable for real-time edge devices operating with restricted power consumption. A realistic dataset, known as the Cyber Threat Detection Dataset, was utilised to test the framework, which incorporated multiple normal and attack behaviours, as well as several different attack varieties. The high-speed intrusion detection system relies on Random Forest (RF), while a Lightweight Convolutional Neural Network (CNN) handles anomaly detection activities within the framework, and Federated Learning (FL) decentralises learning across different edge nodes with privacy protection. According to test results, the developed framework delivers advanced threat detection accuracy, better energy efficiency, and faster inference speeds. Each security model partakes specific capabilities that enhance a layered defence, resulting in a protected, adaptable and scalable protection for Edge AI systems.