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.
DIGITAL LOYALTY, CUSTOMER RETENTION AND CUSTOMER LOYALTY IN CHENNAI’S INDIAN SPECIALITY RESTAURANTS
The rapid digital transformation of the hospitality industry has significantly reshaped customer engagement and relationship management practices in restaurant services. Indian speciality restaurants in metropolitan cities such as Chennai increasingly adopt digital technologies to enhance customer satisfaction, strengthen retention, and build sustainable customer loyalty. This study examines the influence of digital loyalty mechanisms on customer retention and customer loyalty in Chennai’s Indian speciality restaurants. Customer satisfaction is examined as a mediating variable between digital loyalty and loyalty outcomes. A quantitative research design was adopted, and primary data were collected from 150 customers using structured questionnaires. Structural Equation Modeling (SEM) using Partial Least Squares (PLS) was employed to analyze the data. The findings reveal that digital loyalty mechanisms significantly influence customer satisfaction, which subsequently enhances retention and customer loyalty. Customer retention further strengthens customer loyalty. The study contributes empirical evidence to hospitality literature and provides managerial insights for restaurant operators seeking competitive advantage through digital strategies.
Digital Loyalty, Customer Satisfaction, Customer Retention, Customer Loyalty, Indian Speciality Restaurants, Chennai
Copyright (c) 2026 International Journal of Innovation Studies
SKIN CANCER RECOGNITION USING DEEP LEARNING ON ORIGINAL AND AUGMENTED DERMOSCOPIC DATASETS
Skin cancer has been listed among the most common and deadly diseases in the world and the diagnosis of the cancer in the earliest and accurate manner is quite crucial in increasing the probability of patient survival. Recent advances in deep learning have demonstrated that there is much that can be done to extract skin cancer diagnosis by automatically analyzing dermoscopic images. The article is an elaborate implementation and comparative study of deep learning-based skin cancer classification on original and augmented dermoscopic images datasets. The three types of models, which were investigated, were a classical Convolutional Neural Network (CNN), a deep residual network (ResNet-101), and a proposed attention-based CNN. To deal with the problem of class imbalance and contribute to the overall model generalization, data augmentation tools, including rotation, flipping, zooming, and brightness adjustment were used. Based on the preliminary experimental findings on the original data, it came out that the baseline CNN and the ResNet -101 had an accuracy of 77 and 75 respectively. In comparison, the CNN based on attention had a significantly higher accuracy of 98 that indicates greater feature discrimination. After their training on the augmented datasets, the accuracy increased significantly in all models with CNN and ResNet-101 achieving 99% and 98 accuracy, respectively. Attention-based CNN repeatedly provided the best performance of the highest accuracy of up to 99 percent with the high precision, recall and F1-score on all types of lesions, including the minority clinically vital subsets. These results confirm that data augmentation and attention will be very effective in enhancing robustness, reliability thus the proposed methodology would be most relevant in automated and clinically useful management of skin cancer diagnosis.
Skin cancer recognition, dermoscopic images, deep learning, data augmentation, convolutional neural network, ResNet-101, attention-based CNN.
Mandar Shrikant Upasani, Dr.Ashish Chaurasia
OBSTACLE DETECTION FOR VISUALLY IMPAIRED PEOPLE USING IOT
Obstacle detection is one of the most critical challenges faced by visually impaired people, especially in unfamiliar environments where it becomes difficult and dangerous to navigate. This research proposes an IoT-based smart assistance system that aids mobility, safety, and independence in blind users through real-time obstacle detection and alerting. The proposed system integrates ultrasonic sensors with a microcontroller, such as Arduino or NodeMCU, and wireless communication modules to scan a user's surroundings continuously. If an obstacle is detected in a specified range, the system would send feedback via vibration or audio alerts directly to ensure the swift response of the user against the chances of collision. It also connects the module to an IoT-enabled cloud platform for remote monitoring, data logging, and future scalability for advanced features like GPS-based navigation, emergency alerts, and caregiver communication. The compact, wearble design aims to ensure comfort And convenience while maintaining accurate detection performance. Experimental results confirm that the system efficiently identifies obstacles of various sizes and distances in different environmental conditions, whether dynamic or poor light, which essentially enhances the safety and situational awareness of users. This research points to the potential that IoT technologies have in delivering cost-effective, reliable, and user-friendly assistive devices for visually impaired individuals. The proposed solution, with sensor-based detection integrated into intelligent communication and real-time feedback, will contribute to enhancing independent mobility and improving the quality of life among the blind.
Obstacle detection, visually impaired, ultrasonic sensors, real-time alerting, wearable device, assistive technology.
Dr. Sandrilla R, Gugan R, Md Irfan H, Kumaresan S
ILLNESS COGNITION AND PERCEIVED STRESS AMONG ESRD PATIENTS UNDERGOING HEMODIALYSIS
Chronic disease of the kidney is an important cause of global mortality and morbidity. It has become a fast-expanding global problem related to health in all nations. End-stage renal disease is a complex, progressive, and debilitating illness that affects a patient’s quality of life, physical and mental health, well-being, social functioning, and emotional health. Chronic kidney failure causes many limitations for patients. Despite the effectiveness of hemodialysis, this method causes mental problems and frustration in patients. In the present study, the investigator aimed to assess the level of Illness cognition and Perceived stress among patients undergoing Hemodialysis. The researcher adopted a Descriptive Research design, and the data was collected through a questionnaire. Simple random sampling was used. A hundred samples were collected from the Hospital and the dialysis center, Illness cognition and Perceived stress scale were used to collect the Data. There will be a positive relationship between Illness cognition and Perceived stress among patients undergoing Hemodialysis.
CKD, ESRD, Illness cognition and Perceived stress.
IMPACT OF CORPORATE GOVERNANCE ON FINANCIAL PERFORMANCE OF THE COMPANY
Corporate governance has become a cornerstone of modern corporate practice, ensuring accountability, transparency, and fairness in organizational decision-making. In India, the evolution of governance frameworks—from voluntary codes in the 1990s to mandatory provisions such as Clause 49 of the SEBI Listing Agreement—reflects the growing recognition of governance as a driver of financial stability and investor confidence. This paper examines the historical development of corporate governance norms in India, recent regulatory reforms, and their impact on corporate financial performance. The study highlights how mechanisms such as independent and diverse boards, robust audit systems, transparent disclosures, and aligned executive incentives reduce agency problems, strengthen oversight, and foster sustainable growth. Empirical insights suggest that well-governed companies not only achieve higher profitability and easier access to funding but also demonstrate resilience against financial and regulatory risks. Furthermore, the integration of governance with green financing initiatives underscores the strategic role of sustainability in enhancing firm value. By analyzing governance structures and financial outcomes, the paper concludes that corporate governance is not merely a compliance requirement but a strategic tool for long-term value creation, financial stability, and sustainable competitiveness in the global economy.
Corporate Governance, Green Financing, Financial Performance, SEBI, Clause 49, Sustainability
Copyright (c) 2025 International Journal of Innovation Studies
ANALYSIS OF FACTORS INFLUENCING THE CAREER CHOICES OF UNDERGRADUATES IN OPTING FOR BUSINESS EDUCATION IN MP
Career choice is a critical decision in a student’s academic journey, shaping both professional trajectories and long-term socio-economic outcomes. Among undergraduate students, the decision to pursue business education is increasingly influenced by a multitude of personal, social, economic, and institutional factors. Understanding these determinants is essential, particularly in regions like Madhya Pradesh (MP), where higher education is expanding rapidly and students encounter diverse academic and career pathways. Research across disciplines consistently highlights that career decisions are shaped by intrinsic motivations, perceived competence, social expectations, and the broader learning environment. For instance, Tannous et al. (2024) found that students’ professional identity, study experiences, and perceived career prospects significantly guide their career preferences in health sciences. Similarly, Zhou and Shirazi (2025), in a systematic review, emphasized that career aspirations among youth—especially in STEM fields—are shaped by personal interest, self-efficacy, and external support systems. In the context of higher education choices, several studies offer important insights into how students select academic programs and disciplines. Chhor et al. (2024) identified institutional reputation, academic environment, and career opportunities as major reasons behind students’ enrollment decisions in Cambodian universities. Within the Indian setting, Thomas, John, and Thomas (2024) demonstrated that socio-economic background, parental influence, and perceived job security play a substantial role in shaping both undergraduate and postgraduate career decisions. Focusing specifically on commerce and management, Panakaje et al. (2024) highlighted that students’ choices are significantly driven by employability prospects, personal interest in business domains, and evolving industry demands. Similarly, studies on teaching career motivations (Rana et al., 2024), design careers (Swargiary & Padun, 2024), and medical specializations (Godbole et al., 2025) all reinforce the idea that career choices are multidimensional and shaped by field-specific aspirations and contextual determinants. A considerable body of literature also points to the role of family, peer networks, and socio-economic constraints in shaping career decisions. Research from India and neighbouring countries shows that these socio-cultural influences remain dominant. For instance, Islam et al. (2021) found that Bangladeshi students’ career choices are driven by financial expectations, societal norms, and parental guidance. In the Indian context, Srivastava and Dhamija (2022) revealed that institutional image, social prestige, and peer groups strongly influence students’ choices in higher education. Ray et al. (2020) further emphasized that Indian students consider job prospects, family expectations, and self-interest as core determinants of career decision-making. Among business students specifically, Lamichhane et al. (2022) identified personal motivation, perceived career growth, and entrepreneurial aspirations as major influencing factors.
Alkesh Sharma¹, Manoj Bhatia², Khushboo Chhabra³
CUSTOMER PERCEPTION TOWARDS ADOPTING PREDICTIVE AI IN MARKETING FOR AN E-COMMERCE ROBOTIC DELIVERY STARTUP: BREAKING DOWN THE HUMANIZING- DEHUMANIZING SPECTRUM
In the modern age, Marketing is not just about bringing awareness. It's about satisfying a need, even before it arises, and Predictive AI has revolutionized the process by turning the data into foresight, to predict the customer behavior. In the age of reels, where the span of attention has come down, Predictive AI provides personalized recommendations, saving the time, to catch up with the race for lead. But for the business, a single tactic isn't just enough to get the competitive advantage. It's important to have a Plan B and integrate it with Plan A to get a blast of energy. The Plan B is to bring in Novelty in the form of Robotic Delivery System, where Robots & Drones make the delivery process faster, efficient and sustainable. While coming up with a synergy which is humanized in the form of Predictive AI, providing Personalized experience & Dehumanizing in the form of contactless Robotic Delivery System, it is important to know to the customer perception. So, the main aim of this study is to understand Customer perception towards adopting Predictive AI in marketing for an E-commerce Robotic Delivery Startup, Breaking down the Humanizing - Dehumanizing spectrum. A sample of 153 Respondents was considered for the study. A structured Questionnaire was used to collect the Primary Data. Tools such as Frequency, Weighted Mean, T-Test, ANOVA, Correlation and Multiple Regression were used in the study. The study revealed that there exists an Impact of Predictive AI & Robotic Delivery System on Customer Perception mediated by the integrated effect.
Predictive AI, Marketing, Robotic Delivery System, Drones, Delivery Logistics, Integrated Effect, Customer Perception
AN SEM-BASED ANALYSIS OF CUSTOMER TRUST AND ACCEPTANCE OF BANKING CHATBOTS FOR SENSITIVE TRANSACTIONS IN CHENNAI CITY
The rapid adoption of artificial intelligence in the banking sector has led to the increased use of chatbots for customer service delivery. While banking chatbots offer convenience and efficiency, customer trust and acceptance remain critical concerns, particularly when chatbots are used for sensitive financial transactions. This study examines the factors influencing customer trust and acceptance of banking chatbots for sensitive transactions in Chennai City. A descriptive research design was adopted, and primary data were collected from 180 banking customers using a structured questionnaire. Structural Equation Modelling (SEM) using AMOS was employed to test the proposed conceptual framework. The results reveal that perceived security, privacy concerns, perceived usefulness, and ease of use significantly influence customer trust, which in turn has a strong positive effect on customer acceptance of banking chatbots. The findings confirm the mediating role of customer trust in the relationship between technology-related factors and chatbot acceptance. The study offers valuable theoretical and managerial implications for banks aiming to enhance trust and encourage customer adoption of AI-enabled chatbot services for sensitive transactions.
Banking Chatbots, Customer Trust, Acceptance, Sensitive Transactions, Artificial Intelligence, SEM, AMOS
AUTOMATED KNEE OSTEOARTHRITIS SEVERITY DETECTION FROM X-RAY IMAGES: A DATA-CENTRIC AI APPROACH
Knee osteoarthritis remains one of the main sources of pain and disabling conditions that affect the knees of millions of people. Consequently, the early detection of the disease can greatly impact patient care. Nevertheless, the diagnosis of KOA through X-ray is not always a simple solution. These vast datasets of medical images are typically quite inconsistent, noisy, and a variety of image qualities. We aimed to enable AI to detect KOA more accurately by connecting data preparation as the most crucial step of the process in our study. To render the images more AI-friendly, we performed a thorough cleaning of the X-ray images to remove noise and ensure consistent quality. We separated the images into different levels of severity of the disease after cleaning, which allowed our algorithm to recognize the smallest changes as the disease progressed. This well-prepared dataset allowed the AI model to obtain a high level of accuracy in detecting KOA, even being able to match the performance of human experts. We designed an AI tool that can offer stable and reliable assistance to doctors, thus, the diagnostic procedure can be potentially expedited and patient outcomes improved without the need for data of inferior quality from the very beginning. We plan to perfect these methods and also study the possibility of using them for diagnosing other joint disorders.
Artificial Intelligence, Radiographic Analysis, Knee Osteoarthritis, Medical Imaging, Automated detection.
Swathi kumari R, S.Geetha, P. S.Rajakumar
AN SEM-BASED ANALYSIS OF PURCHASING BEHAVIOUR AND BRAND LOYALTY OF WOMEN CONSUMERS TOWARDS PERSONAL CARE PRODUCTS IN CHENNAI CITY
The rapid expansion of the personal care products market has intensified competition, making consumer satisfaction and brand loyalty critical for long-term success. This study examines the structural relationships between purchasing behaviour factors and brand loyalty among women consumers of personal care products in Chennai City, with consumer satisfaction acting as a mediating variable. A descriptive research design was employed, and primary data were collected from 150 women consumers using a structured questionnaire. Structural Equation Modelling (SEM) using AMOS was applied to test the proposed conceptual framework. The results indicate that purchasing behaviour factors—product quality, price, brand image, availability, and promotion—significantly influence consumer satisfaction, which in turn exerts a strong positive effect on brand loyalty. The findings confirm the mediating role of consumer satisfaction and highlight its importance in transforming favourable purchasing behaviour into sustained brand loyalty. The study offers both theoretical and managerial insights for personal care product marketers operating in competitive urban markets.
Purchasing Behaviour, Brand Loyalty, Consumer Satisfaction, Women Consumers, Personal Care Products, Structural Equation Modelling, AMOS
ASSESSING THE INFLUENCE OF DECISION-MAKING FACTORS ON INVESTMENT PORTFOLIO BEHAVIOUR OF NON-RESIDENT INDIANS: AN EMPIRICAL STUDY
This study investigates the impact of decision-making factors on the investment portfolios of Non-Resident Indians (NRIs) from Kerala, a region with one of the highest remittance inflows in India. Drawing from existing literature, ten independent variables—such as risk tolerance, return expectations, financial literacy, and behavioural biases—were identified and assessed using a structured questionnaire. A sample of 385 NRI respondents was selected based on Cochran’s formula, and data were analysed using Structural Equation Modelling (SEM) in AMOS. The results reveal that most constructs significantly influence portfolio decisions, except for liquidity preference and information sources, which did not show a strong effect. The study contributes to a better understanding of NRI investment behaviour by integrating financial and psychological variables in a single model. It offers valuable insights for financial planners, policy-makers, and investment advisors working with the diaspora population. The findings also highlight the need for targeted financial education and customised investment products to enhance portfolio diversification. Limitations include reliance on self-reported data and a geographically specific sample. Future research can expand on these findings by exploring other NRI communities and employing longitudinal or mixed-method approaches to better capture changes in behaviour over time.
Non-Resident Indians, Investment Behaviour, Decision-Making Factors, Portfolio Choice, Behavioural Finance
HYBRID INTENT RECOGNITION FOR PERSONALIZED LEARNING: INTEGRATING DEEP LEARNING AND RULE-BASED MODELS IN MOODLE CHATBOTS
Conversational chatbots have revolutionized educational platforms by providing personalized support and interactive learning experiences. However, existing intent recognition models in Learning Management Systems (LMS) like Moodle struggle with ambiguous, context-dependent queries, leading to inaccuracies in understanding student interactions. This research proposes a hybrid intent recognition algorithm that combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning models with rule-based verification to enhance the chatbot's ability to classify intents with higher precision. The primary objective of this study is to address the limitations of traditional Natural Language Understanding (NLU) techniques by leveraging deep learning for context retention and rule-based mechanisms for domain- specific accuracy. The proposed hybrid model was trained using a dataset of student queries collected from Moodle logs, categorized into 20 intent classes, and processed using tokenization, text cleaning, and word embeddings (e.g., GloVe, BERT). Comparative performance analysis against standalone models—Rule-Based, Naïve Bayes, LSTM, and GRU—demonstrated that the hybrid model achieved 92.5% accuracy, significantly outperforming conventional approaches. Statistical validation using paired t-tests and ANOVA confirmed the statistical significance of the improvements, with the hybrid model achieving the highest precision, recall, and F1- score while reducing response time by 20% compared to LSTM. These findings validate the proposed approach as a robust solution for real-time intent recognition in educational chatbots, fostering personalized learning, improving student engagement, and optimizing Moodle’s teaching-learning evaluation processes. Future research will explore reinforcement learning techniques to dynamically adapt chatbot responses and expand multilingual support for diverse educational environments.
Intent Recognition, Chatbot, Hybrid Model, LSTM, GRU, Rule-Based System, Natural Language Understanding, Educational Technology, Moodle, Personalized Learning.
Johnbenetic Gnanaprakasam , Ravi Lourdusamy
THE ROLE OF SCHOOL PRINCIPALS IN ACHIEVING A BALANCE BETWEEN TIME MANAGEMENT AND MOTIVATING EDUCATIONAL STAFF
The purpose of the article is to investigate how school principals could balance inspiring educational staff with time management. The study seeks to pinpoint the techniques principals use, the difficulties they encounter, and how time management techniques affect staff motivation. Combining quantitative data gathered by questionnaires with qualitative insights gleaned from semi-structured interviews, a mixed-method approach was used. School principals and instructional personnel chosen by stratified random sampling to guarantee varied representation made up the study sample. Effective planning, task distribution, and technology utilization turned shown to be three main tactics for juggling staff motivation and time management. Schools using well-defined time management strategies showed better performance, lower stress, and more staff motivation. Still, obstacles including conflicting priorities, opposition to change, and limited resources were noted as ones standing in between. The study revealed a strong, favorable relationship between effective time management by principals and staff motivation. Suggestions include for strengthening channels of communication, training on time management and motivation, and group decision-making nurturing. The results highlight the vital part school principals play in using good leadership to create a motivating and efficient learning environment.
Dr. Ihab Massarwa, Dr. Taghrid Qadiria, Dr. Nadia Mussa, Dr.Haneen Majdalawi
THE IMPACT OF DIGITAL MARKETING ON CREATING BRAND AWARENESS
Digital marketing advancement changed how brands build awareness and target consumers through their marketing strategies. The research investigates brand recognition changes through a study of digital marketing principles including social media marketing with content marketing and influencer marketing strategies. The research performed thematic literature review methods alongside empirical data analysis to understand current digital marketing patterns along with their obstacles and possibilities. Digital marketing tools substantially boost brand visibility and customer interest yet potential long-term issues stem from excessive advertising and technical limitations and abundant media content. The research shows that companies need to use complete marketing strategies which mix digital methods with traditional techniques to achieve ongoing brand visibility.
Digital marketing, brand awareness, social media marketing, content marketing, influencer marketing, consumer engagement, digital transformation, branding strategies.
“FOSTERING WORKPLACE VITALITY: EXPLORING THE UNDERLYING DRIVERS OF EMPLOYEE SUCCESS IN CONTEMPORARY ORGANISATIONS”
Motivation plays a vital role in shaping employee’s attitudes, enhancing productivity and driving organisational success. This study focuses on the influence of motivational strategies on employee job satisfaction and workplace performance among Information Technology (IT) professionals. The convenience sampling method was used for this study to collect data from 150 respondents working in the IT sector in Chennai city. A structured questionnaire with three sub sections along with the socioeconomic profile was disseminated to each respondent through google form link. The study used statistical instruments such as Simple Percentage Analysis, Descriptive Statistics, Regression, Correlation and Chi-Square to analyze the data. The results indicate that there is significant positive influence of Motivational Strategies on Job Satisfaction and Workplace Performance. The study highlights the importance for organisations to continually assess and refine their motivational approaches to sustain their employees for making the organisation reach higher profits and achieve its goal. The study also provides pathway for future research and suggestions for the management for making improvements in adopting various motivational strategies.
Motivation, Vitality, Strategies, Performance, Rewards, Success
Dr. R. Rangarajan, Dr. A.S Kalyana Kumar, Smita Singh .V
ASSESSING THE IMPACT OF EMPLOYEE SATISFACTION ON LOYALTY IN THE GARMENT SECTOR.A STUDY OF BANGALORE - BASED INDUSTRIES.
This study investigates the factors influencing employee satisfaction and its effect on loyalty within the garment industries in Bangalore. Primary data was collected using a structured questionnaire with a 5-point rating scale from employees in five key areas—Bagalur, Yelahanka, Yeswanthapura, Electronic City, and Peenya—yielding 530 responses, with 430 valid samples used for analysis. Descriptive statistics indicated moderate to high levels of satisfaction and loyalty among respondents. Correlation analysis revealed strong positive associations among employee satisfaction, organizational commitment, and retention intent, with coefficients ranging from 0.62 to 0.68. Further, regression analysis showed that employee satisfaction significantly predicts employee loyalty (B = 0.45, p < 0.001), along with organizational commitment (B = 0.35, p < 0.001) and retention intent (B = 0.30, p < 0.001), with the model explaining 62% of the variance in loyalty. A review of relevant literature on working conditions in Bangalore’s garment factories provided additional secondary insights to contextualize the findings. Overall, the results underscore the critical role of employee satisfaction in driving loyalty and highlight the potential benefits of targeted human resource interventions for enhancing organizational performance in the garment sector in Bangalore.
Employee satisfaction, garment industries, organizational commitment, loyalty.
AI-ENHANCED VOCABULARY TOOLS AND THEIR IMPACT ON ACADEMIC WRITING DEVELOPMENT AMONG EFL COLLEGE STUDENTS
This research paper examines how AI vocabulary tools affect the academic writing skills of EFL college students. The research design combines quantitative and qualitative methods to evaluate writing assessment results, vocabulary quiz outcomes, and student feedback before and after the intervention. The intervention employs adaptive AI systems which provide immediate lexical suggestions and domain-specific customization combined with metalinguistic explanations for authentic coursework and self-directed learning activities. The research shows significant progress in three main areas, which cover both lexical variety and exact word choice, cohesive structure, and student engagement through customized tools and teamwork capabilities. Research indicates that students need AI tools together with human instructor guidance to develop critical thinking skills while avoiding excessive dependence on technology. The study examines three main obstacles to digital transformation, which consist of digital literacy deficits, unbalanced technology and digital resource availability, and ethical problems that need ongoing support and proper regulatory frameworks. The study reveals implications for curriculum development, which supports the use of AI-enhanced language learning tools throughout language skills and subject areas, while identifying research directions for studying AI tool retention, scalability, and cultural adaptability.
Artificial Intelligence (AI), AI-powered vocabulary tools, EFL academic writing, Lexical diversity, Automated writing feedback, Language learning technology
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
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,