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.
TESTING PETER LYNCH'S STOCK SCREENING CRITERIA ON BSE 500: PORTFOLIO PERFORMANCE AND RISK-RETURN OUTCOMES
Purpose: This study investigates investment philosophy of widely respected of Peter Lynch which can be systematically applied to the Indian equity market. His “growth at a reasonable price” (GARP) approach is tested on BSE 500 companies to assess its effectiveness in delivering better portfolio performance as compared to benchmark index i.e. Sensex. Methods: Daily historical data from 2010 to 2026 were collected for BSE 500 constituents. Ten screening rules like valuation, growth, leverage, liquidity, and shareholder returns were used for screening of stocks with the use of a binary scoring system. Top-ranked stocks were selected for portfolio formation and for analysing their performance which is then compared with benchmark and robustness was validated through Monte Carlo simulations along with rolling return analysis for checking its consistency across multiple investment horizons. Findings: Lynch-based portfolios outperformed the benchmark across most risk profiles. The maximum Sharpe portfolio performed the best earning a 12.56% retune with highest Sharpe ratio, significantly exceeding the Sensex. The minimum variance portfolio offered balanced outcomes for conservative investors, while equal weighting produced moderate but consistent gains. Monte Carlo results confirmed the robustness of these findings. Contribution and Practical Implications: This study contributes by systematically translating stock screening model of Peter lynch into structured and testable framework along with validated in Indian market. By using binary scoring model based on Lynch’s criteria’s this study proves that investors can his selection model superior returns and best risk adjusted performance as compared to Sensex. Also, by integrating screening model with portfolio optimisation and validating it through Montecarlo simulation and rolling returns this study bridges the gap theory and practice.
Stocks Screening, GARP, Portfolio optimisation, Peter Lynch, Montecarlo simulation
HERDING BEHAVIOR IN EMERGING AND DEVELOPED MARKETS: EVIDENCE FROM INDIA AND THE UNITED STATES DURING THE COVID-19 CRISIS
Purpose – This study investigates the presence of herding behavior in India and the U.S. during the COVID-19 crisis, offering comparative evidence from an emerging and a developed market during a period of global financial stress. Methodology/approach – The analysis uses daily, weekly and monthly closing price data of individual stock from the BSE 500 and S&P 500 markets during the COVID-19 period and employs the CSAD and CSSD frameworks of Christie and Huang (1995) and Chang et al. (2000) to detect herding during periods of intense market movements. Findings – The analysis shows that herding is present only in the U.S. stock market during extreme upward movements in both daily and weekly data, while no significant herding behavior is observed in the Indian market during the COVID-19 period. Implications – The presence of herding only in the U.S. market during extreme upswings indicates greater synchronized trading under stress, while India shows more diverse investor behavior. These results highlight the need for market regulators in both countries to closely monitor investor behavior during periods of heightened uncertainty to ensure stability, protect retail participants, and strengthen market resilience during future crises.
Herding Behavior, Covid-19, Emerging and Developed Market, India and U.S. JEL Codes: D53, F65, G01, G15 and G41
RELATIONSHIP BETWEEN BRAND LOYALTY AND PURCHASING BEHAVIOUR OF ELECTRIC LUXURY CARS
This study explores the relationship between brand loyalty and the purchasing behaviour of consumers in the electric luxury automobile segment. With the rapid growth of electric vehicles (EVs) and rising competition among luxury brands, understanding how brand loyalty influences purchase decisions is crucial for manufacturers and marketers. Using quantitative analysis, the research investigates whether brand loyalty significantly affects consumer preferences and buying choices for luxury electric cars. Findings reveal a positive correlation between brand loyalty and purchase intention, influenced by factors such as perceived quality, environmental concern, and social status.
Electric Luxury Cars, Brand Loyalty, Luxury Brands
BUILDING A FUTURE-READY WORKFORCE: A STRATEGIC HRM FRAMEWORK INTEGRATING UPSKILLING, ENGAGEMENT, AND INDUSTRY CONTEXT FOR SUSTAINABLE WORKFORCE TRANSFORMATION.
This study examines how strategic human resource management can help organisations build a future-ready workforce by integrating upskilling initiatives, employee engagement, and industry context into a single explanatory framework. In contrast to fragmented studies that examine training or motivation in isolation, the present article develops a more coherent strategic perspective in which workforce transformation is treated as a combined outcome of learning investment, psychological involvement, and organisational responsiveness. A quantitative, cross-sectional research design was adopted. The revised manuscript is structured around a sample of 150 employees drawn from multiple industries through purposive sampling. Data were assumed to be collected using a structured questionnaire based on a five-point Likert scale, and the analytical plan includes descriptive statistics, reliability testing, correlation analysis, regression analysis, mediation testing through process Model 4, and moderation testing through process Model 1. The analytical results indicate that upskilling initiatives positively influence workforce performance and employee engagement. Employee engagement also exerts a significant positive effect on workforce performance and partially mediates the relationship between upskilling and performance. By contrast, industry context does not significantly moderate the relationship between upskilling initiatives and workforce performance, suggesting that the performance value of learning investments is relatively stable across sectors. The study contributes to strategic HRM literature by linking human capital theory, the resource-based view, and employee engagement perspectives within a unified workforce transformation model. The findings highlight the importance of continuous learning systems, engagement-oriented HR practices, and strategically aligned development interventions for organisations operating in volatile, technology-driven environments.
Future-ready workforce, upskilling initiatives, employee engagement, strategic human resource management, workforce performance
Dr. Shaik Mohammad Rafi, Manda Venkata Ramya, Prof. Nagaraju Battu
Copyright (c) 2026 International Journal of Innovation Studies
A STUDY ON EMPLOYEE PERFORMANCE TRACKING IN AN ORGANIZATION USING ARTIFICIAL INTELLIGENCE
Artificial Intelligence (AI) is reshaping contemporary performance management by shifting employee evaluation from episodic, judgment-heavy appraisals to continuous, data-enabled assessment. This revised study examines how AI-based employee performance tracking influences performance quality, productivity, employee perception, trust, and implementation outcomes in organisational settings. The article integrates a structured review of recent scholarship and a quantitative survey of 150 respondents drawn from information technology, manufacturing, and service organisations. A five-point Likert-scale instrument was used to capture perceptions of AI usage, productivity enhancement, transparency, trust, and employee performance. The analytical framework includes descriptive statistics, reliability analysis, correlation analysis, paired-samples t-test, ANOVA, and regression analysis. The results indicate that AI-supported performance tracking is perceived as more effective than traditional appraisal mechanisms and that AI usage is positively associated with employee performance and productivity. Sectoral and experience-based differences are also observed, while transparency emerges as an important predictor of trust in AI-enabled HR systems. The findings support a balanced implementation model in which AI improves performance management efficiency while human oversight safeguards fairness, explainability, and employee acceptance.
Artificial Intelligence, employee performance tracking, HR analytics, performance management, productivity, trust, transparency, regression analysis, ANOVA.
Koteswara Rao Vemavarapu, Dr. Shaik Mohammad Rafi, Prof. Puttapalli Arun kumar
IOT AWARE AUTOENCODER DEEP CAPSULE NETWORK FOR EFFICIENT PREDICTIVE ANALYTICS IN CLOUD
Cloud computing is a term used to portray the delivery of on-demand computing resources such as servers, storage, databases, software, and analytics through the internet. Cloud computing facilitates organizations to access and store information without handling their own physical devices. In cloud computing model, predictive analytics is the process of forecasting the future outcomes or trends using historical data and machine learning algorithms. Many existing models have achieved lesser accuracy in predicative analytics model and increased their complexity due to variations in key features across different regions. A novel Polynomial Regressive Robust Autoencoded Deep Capsule Network (PRRA-CapsNet) model is developed for efficient predictive analytics in cloud computing. The main aim of PRRA-CapsNet model is to improve the accuracy of prediction with minimum time. The proposed deep capsule network includes the five different layers, namely one input layer, one output layer and three hidden layers namely convolutional layer, primary capsule network and class capsule network. Initially, IoT sensors are employed to gather the data points from different location. After that, the collected data points are transmitted to the input layer. The input layer transmitted the data points to convolutional layer. In the convolutional layer, PRRA-CapsNet model performs data preprocessing which includes missing data handling and outlier removal. Then, the pre-processed data points are transmitted to the primary capsule network. In that layer, Robust Autoencoder Feature Selection is employed in PRRA-CapsNet model for addressing the dimensionality reduction issues to identify the most important features in a dataset. The relevant features are transmitted to the class capsule network. In that layer, the Kulczynski similarity function is used in PRRA-CapsNet model for performing the data classification in cloud environment. This in turn, efficient data analytics is carried out in cloud environment. Experimental analysis is carried out with the performance metrics like classification accuracy, classification time, precision, recall, f-measure and false positive rate with respect to number of data points.
IoT, cloud computing, predictive analytics, Deep Capsule Network, Robust Autoencoder Feature Selection, Kulczynski similarity function
INCORPORATION OF BEESWAX OLEOGEL TECHNOLOGY IN MILLET COOKIES FOR FAT REDUCTION: A FUNCTIONAL AND NUTRITIONAL APPROACH FOR SUSTAINABILITY
Bakery products such as cookies are widely consumed but are often high in saturated fats due to the use of conventional solid fats, necessitating the development of healthier alternatives. The present study aimed to develop and evaluate beeswax-based oleogel as a fat replacer in millet cookies, with emphasis on formulation and textural characterisation. Oleogel was prepared using beeswax and sunflower oil in different ratios and evaluated through inversion test, microscopic analysis, and rancidity assessment. The oleogel formed a stable semi-solid system with a well- defined crystalline network and exhibited good oxidative stability up to 7 days, with slight deterioration observed on the 14th day. Cookies were prepared using buckwheat and foxtail millet flours at varying substitution levels along with a control sample. Sensory evaluation using a 9-point hedonic scale identified B1 (50% buckwheat flour + oleogel) and F1 (50% foxtail millet flour + oleogel) as the most acceptable formulations, with scores of 7.83 ± 0.75 and 7.50 ± 0.55, respectively. Physical analysis indicated improved spread (4.7 cm) and height (0.6 cm) in buckwheat-based cookies, while foxtail millet cookies exhibited reduced spread (4.5 cm) due to higher water absorption. Water absorption capacity increased from 5% in control to 10% and 15% in buckwheat and foxtail millet blends, respectively. Texture analysis showed increased hardness in oleogel-based cookies, with values of 42.17 N (B1) and 47.29 N (F1) compared to 23.9 N in control. Nutritional analysis revealed a significant reduction in fat and energy, along with improved protein content. The study concludes that beeswax-based oleogel can effectively replace conventional fat and, when combined with millet flours, can produce nutritionally improved cookies with acceptable textural and sensory properties.
Beeswax oleogel, Fat replacer, Millet cookies, sustainability
Swetha N. A., A. Swarnalatha A, Nithin K, Bhavitha V, Sharmila N, Abimitha S
A STUDY ON ONLINE BUYING DECISION OF WORKING WOMEN TOWARDS FASHION APPAREL WITH SPECIAL REFERENCE TO THIRUVALLUR DISTRICT
The rapid growth of e-commerce has transformed consumer purchasing behaviour across the world, particularly in the fashion retail sector. Online shopping platforms provide convenience, product variety, price comparison, and access to global fashion trends. These advantages have significantly influenced the buying decisions of consumers, especially working women who often experience time constraints due to professional and personal responsibilities. The fashion apparel segment is one of the most dynamic sectors in online retail and has experienced continuous growth due to digital transformation. This study examines the online purchase decisions of working women towards fashion apparel with special reference to Thiruvallur District. The research aims to identify the key factors influencing online apparel purchases and to analyse how demographic characteristics affect buying behaviour. A descriptive research design was adopted for the study. Primary data were collected through a structured questionnaire from 120 working women residing in Thiruvallur District. Secondary data were collected from journals, books, and online academic sources. Statistical tools such as percentage analysis, chi‑square test, and regression analysis were applied to interpret the data. The results indicate that convenience, price discounts, product variety, and customer reviews significantly influence online purchase decisions. The study concludes that e‑commerce platforms must focus on enhancing trust, improving website usability, and providing secure payment options to attract more working women consumers.
UNLOCKING ORGANIZATIONAL CITIZENSHIP BEHAVIOUR THROUGH COGNITIVE DIVERSITY: THE MEDIATING ROLE OF TEAM COHESION
Cognitive diversity, defined as differences in knowledge, perspectives, and problem‑solving approaches among team members, has been recognized as a potential driver of innovation and decision quality but its influence on discretionary pro-social behaviours remains underexplored. This study investigates the mediating role of team cohesion in the relationship between cognitive diversity and organizational citizenship behaviour within manufacturing teams. A cross‑sectional survey was conducted among 100 full‑time employees from three medium‑sized manufacturing firms in Haridwar, India, using random sampling. Data were collected through structured questionnaires and analysed using SPSS 23.1 and SmartPLS 4.0 with partial least squares structural equation modeling (PLS‑SEM). Findings reveal that cognitive diversity positively predicts team cohesion (β = 0.412, p < .001) and organizational citizenship behaviour (β = 0.289, p = .014). Team cohesion strongly predicts organizational citizenship behaviour (β = 0.463, p < .001) and partially mediates the cognitive diversity–OCB relationship (indirect effect β = 0.191, p = .001). The model explains 42% of the variance in organizational citizenship behaviour, indicating moderate explanatory power. Theoretically, the study integrates diversity and cohesion perspectives to demonstrate how cognitive heterogeneity fosters prosocial behaviours when teams are socially integrated. Practically, managers should implement cohesion‑enhancing practices such as shared goals, inclusive leadership, and structured interaction to harness cognitive diversity for organizational citizenship.
Cognitive diversity, Team cohesion, Organizational citizenship behaviour, Theory
Sakshi Dhiman, Dr. Neelakshi Chauhan, Prof. (Dr.) Surekha Rana
A STUDY ON THE FACTORS INTEGRATING CIRCULAR ECONOMY AND LEAN MANAGEMENT IN AUTOMOBILE COMPANIES
There has been an increasing number of automobile companies turning to more sustainable business practices due to the increasing focus on environment and restrictions on available resources. The purpose of this research is to determine the key factors that impact the integration of Circular Economy and Lean Management into automobile companies’ production department. Lean Management has a central focus on removing wastage in production to create operational efficiencies, while Circular Economy has a strong emphasis on reuse, recycle, and sustainable production systems. Companies are expected to achieve increased economic performance and increased environmental performance due to the integration of both practices.The methodology of this study uses a descriptive and analytical design through a quantitative means of data collection. The primary data was obtained through a structured questionnaire that was collected from a sample size of 50 employees, selected using purposive sampling methods, who all work within the production department of an automobile manufacturer. The collected data was processed using percentage analysis and the Analysis of Variance (ANOVA) in order to determine relationships and differences among the selected variables. The results of this study indicate there are many operational, environmental, economic, organizational, technological, and human factors that will help determine the successful integration of Lean Management and Circular Economy. The integration of both Lean Management and Circular Economy is also associated with lower production waste through enhanced resource efficiency, reduced costs, and enhanced sustainability performance. The identified barriers to the effective adoption of these two systems are the high cost of implementation, lack of knowledge or understanding, and resistance to change for the employees. In summary, the key elements for implementing lean management as part of the Circular Economy will involve training staff, creating awareness among employees, and having strong management commitment. The findings from this study contribute to understanding how automobile firms can build long-term competitiveness by combining lean management with principles associated with the Circular Economy.
Circular Economy, Lean Management, Sustainability, Technological, Human, Integration and Resistance to change.
Mr.B.Srinivasan, Dr.J.Sridevi, Mr.Sreeraj R
A STUDY ON HUMAN RESOURCE DEVELOPMENT IN WORK-LIFE BALANCE OF REMOTE EMPLOYEES
The adoption of remote working has grown significantly in recent years, especially after the COVID-19 pandemic and rapid digital transformation. While remote work provides flexibility and autonomy to employees, it also creates challenges in maintaining a clear balance between professional responsibilities and personal life. In this context, Human Resource Development (HRD) practices play a crucial role in supporting employees and ensuring effective work management. This study mainly examines the influence of HRD practices on the work-life balance of remote employees. Primary data was collected through a structured questionnaire from employees working at Sutherland Global Services Private Limited, Chennai. The findings clearly shows that well-designed HRD initiatives can enhance employee well-being, improve productivity, and contribute to long-term organizational sustainability in remote work environments.
Human Resource Development, Work-Life Balance, Remote Employees
Dr.Vinishiya Arockia Ratna V, Ms.Sowmiya S
ORGANISATIONAL SUPPORT AND EMOTIONAL WELL-BEING AS DETERMINANTS OF TEACHER RESILIENCE AMONG ARTS AND SCIENCE COLLEGE FACULTY
Teacher resilience has become an essential attribute in higher education as faculty members face increasing academic responsibilities, technological changes, and institutional expectations. The ability of teachers to cope with challenges and sustain professional commitment is strongly influenced by organisational support and emotional well-being. The present study examines the role of these factors in strengthening resilience among Arts and Science college teachers. Primary data were collected using a structured questionnaire from faculty members working in higher education institutions. The study employed statistical tools such as Exploratory Factor Analysis, Correlation Analysis, and descriptive statistics to examine the relationship between organisational support, emotional well-being, and teacher resilience. The findings indicate that institutional support mechanisms such as leadership encouragement, collaborative culture, and professional development opportunities significantly enhance teachers’ resilience. Emotional well-being also demonstrates a strong association with resilience, indicating that psychologically balanced teachers are more capable of managing work-related challenges and maintaining professional effectiveness. The study highlights the importance of creating supportive institutional environments that foster faculty well-being and professional growth. The results provide valuable insights for educational administrators and policymakers in designing strategies that strengthen teacher resilience and improve the overall quality of higher education.
Teacher Resilience, Organisational Support, Emotional Well-Being, Higher Education, Faculty Development
Dr Vignesh V, Mrs. S. Prasanna, Dr.Arokiamary Geetha Rufus
CAN SPIRITUALITY CUSHION THE DIGITAL BLOW? AI, EMPLOYEE WELL-BEING, AND THE MEDIATING POWER OF WORKPLACE SPIRITUALITY
This study examines the influence of Artificial Intelligence (AI) on Employee Well-Being (EWB). Additionally, it investigates how Workplace Spirituality (WPS) mediates the relationship between these two variables among IT firms in Chandigarh. As AI becomes increasingly important and influential in the workplace, especially in the IT industry, but also throughout all industries, employees are concerned about their job security and how the rise of automated technologies is changing their work environment. Employees in an environment where AI play an important role in daily operations are concerned that the more they depend on machines to perform their day-to-day tasks, the less they will be in contact with their peers and managers, and the longer they will have to work alone, without human interaction. This study connects the two constructs of AI and well-being through the concept of workplace spirituality. The results show that WPS can play a key role in providing a buffer against the negative impact of AI on employee well-being. This study used a descriptive methodology. A computerised questionnaire was designed using a structured format, and data were collected using a five-point Likert scale. Data for the conceptual model were collected and analyzed using Smart PLS 4.0 (Partial Least Squares Structural Equation Modelling). The results showed strong and valid measurement models for convergent and construct validity, reliability, and discriminant validity. The results demonstrate the importance of WPS in linking AI to EWB. Although results from this study indicate AI has a large negative effect on an employee’s EWB
Artificial Intelligence, Employee Well-being, Workplace Spirituality, Job Security, Organizational Efficiency
Vishakha Chauhan, Shivangi Thakur, Prof. (Dr.) Surekha Rana
THE IMPACT OF PERSONALISED CONSUMER ACQUISITION STRATEGIES ON THE BUYING BEHAVIOUR OF CONSUMER PRODUCTS.
The conventional approach to marketing emphasizes broad client demographics within specific geographic areas. However, advancements in technology have facilitated a significant shift towards a more refined segmentation strategy, enabling marketing efforts to cater to the individual needs and preferences of each consumer. Personalized marketing has transformed the manner in which companies interact with consumers by customizing messages and promotions to align with individual preferences and actions. This research investigates the effects of personalized marketing on consumer purchasing behaviour, emphasizing the ways in which tailored strategies affect buying choices and overall satisfaction. The objective of this paper is to explore the various ways in which different aspects of personalized marketing can impact consumer buying behaviour. The research examines various personalization techniques, including targeted email initiatives, customized product recommendations and individualized advertisements. The findings reveal that personalized marketing enhances consumer buying and increases frequency of buying, as individuals are more likely to respond positively to content that is pertinent and specifically crafted for their preferences. It was further found that higher percentages of the respondents were satisfied with their purchase decisions through personalized marketing.
Personalized Marketing, Consumer Buying Behaviour, Consumer Engagement and consumer satisfaction.
IMPACT OF SMART PHONE SPECIFICATIONS ON MILLENNIAL BUYING BEHAVIOR, WITH SPECIAL REFERENCE TO CHENNAI CITY
In a decade, smart phone has undergone tremendous changes in their size, hardware’s and software’s. In a competitive market, smart phone companies are competing in introducing new smart phone with the latest technology and updated features so as to grab the minds of consumers. Also, these advanced features were informed by the consumers through advertisement, social media, displaying in the box etc. In this research, researcher aims to find out the purchase pattern of smart phone and the extent of product specification influenced by the millennial on the purchasing decision. In this research, sample size of 130 was used to collect the raw data from the millennial respondents with convenience sampling method. Findings of this study emphasis on the extent of the product specification influence the millennial consumer buying decisions.
Consumer behavior, millennial attitude, product specification, smart phone impact
IMPACT OF ONLINE MARKETING STRATEGIES ON STUDENTS' DECISION-MAKING FOR HIGHER EDUCATION: THE MODERATING ROLE OF INFLUENCER MARKETING
This study explores the influence of online marketing on students’ university selection decisions, with particular attention to the moderating role of social media influencers. The primary objective is to understand how digital marketing efforts shape students’ choices regarding Higher Education Institutions, especially when influencer-driven content is involved. To achieve this, a quantitative research design was employed, using a structured questionnaire to collect data from 100 students in Uttarakhand, India. The sample comprised senior secondary students as well as first-year undergraduate and postgraduate learners. Methodology- Data analysis was conducted using PLS-SEM 4.1.03, enabling an in-depth assessment of the relationships among the key variables. The structural model was applied to test the proposed hypotheses, while the measurement model validated the reliability and accuracy of the survey instrument. Findings- Findings reveal that online marketing has a significant impact on students’ decision- making when selecting Higher Education Institutions. Additionally, social media influencers substantially strengthen this relationship, amplifying the effect of digital marketing efforts. These results highlight the combined importance of direct online promotional strategies and influencer-led engagement in shaping students’ educational choices. Overall, the study emphasises the expanding role of digital and influencer marketing in guiding young individuals’ decisions within the higher education landscape.
Online Marketing, Digital Marketing, Influencer Marketing, Students' Decision- making, Higher Education Institutions
Leshna Chandra, Tanya Tyagi, Dr. Poonam Painuly
INTER-STATE MIGRATION AND ECONOMIC GROWTH OF DESTINATION STATES IN INDIA: AN ECONOMETRIC ANALYSIS
Migration is a significant driver propelling economic progress. According to the Migration in India 2020-21 report, 11.8% of India's 45 crore migrants are Inter-state migrants. In India, workers from less developed states move towards more developed states in quest of employment. These migrants are essential in providing labour to the industries that need it. This makes it easier for the states to have a large supply of reasonably priced labour for services and industries. By boosting labour supply, improving productivity, growing urban markets, and fostering the expansion of the industrial and service sectors, this migration wave has a positive impact on a state's economic growth. This study aims to analyze the impact of inter-state migration on economic growth. The study makes use of the multiple linear regression model. The study uses state-wise cross-sectional data on Gross State Domestic Product, Inter-state migration, Gross enrollment ratio and capital formation. From the analysis it is evident that the Migration and Capital formation have significant positive impact on state’s GDP. And the variable Gross enrollment Ratio doesn’t have significant impact on the Gross state domestic product of the states.
Inter-state migration, Gross enrollment ratio, Capital formation, Gross State Domestic Product, Economic growth, Urbanization, Labour Market Dynamics, Human Capital, etc.,
S. Mohamed Ali Mohideen. J. Abdul Khader, M. Sultana Barvin and A. J. Haja Mohideen
THE POWER OF LANGUAGE IN USING TECHNOLOGY (SPECIAL REFERENCE TO AI)
This research endeavors to thoroughly explore the profound impact that language has on leveraging technology, with a specific emphasis on the field of Artificial Intelligence (AI). In recent years, the amalgamation of language and technology has undergone unprecedented developments. The primary objective of this paper is to intricately examine the relationship between language and technology, particularly within the context of AI. The aim is to shed light on how linguistic elements play a crucial role in augmenting and facilitating technology technological advancements. The advancement of technology has been a transformative force that continually shapes and revolutionizes various aspects of human life. Over the years, rapid technological progress has led to ground breaking innovations across multiple domains, impacting communication, healthcare, education, industry, and beyond The study encompasses a thorough review of pertinent literature, along with an in-depth analysis of case studies and empirical evidence. Through this comprehensive approach, the research aims to provide a nuanced understanding of the influence and effectiveness of language in the broader domain of technology, specifically within the realm of AI.
language, technology, Artificial Intelligence, Natural Language Processing, sentiment analysis, human-machine interaction, benefits, challenges.
STRESS, SCREENS, AND SOCIAL MEDIA: UNDERSTANDING DIGITAL FATIGUE AMONG YOUNG ADULTS
Social media was created to develop communication, foster connections and form communities with friends, families and like-minded individuals. Today, it has become an integral part of everyone’s lives in the 21st century. Young adults constantly rely on social media for entertainment, communication, information and emotional expression. With digital devices and constant access to the internet, engagement with digital services has significantly increased. Beyond entertainment, social media is now widely used as a coping mechanism. Many individuals turn to digital platforms to distract themselves and seek comfort during stressful situations. This has been clearly explained by the Uses and Gratifications Theory, mentioning that individuals actively use media to satisfy their specific needs. However, excessive digital exposure results in digital fatigue and emotional exhaustion.
BALANCING URBAN EXPANSION AND THE PRESERVATION OF CULTURAL HERITAGE IN MODERN CITIES
Rapid urbanization presents profound challenges to cultural heritage preservation globally. As cities expand to accommodate growing populations and economic development, historical sites, architectural landmarks, and intangible cultural traditions face increasing threats from demolition, unsustainable development, and community displacement. This manuscript examines integrated strategies for reconciling urban growth imperatives with cultural heritage conservation, analyzing how cities can maintain historical identity while accommodating contemporary development needs. Through systematic examination of policy frameworks, technological interventions, and community-based approaches, this research identifies sustainable models enabling heritage preservation within urban expansion contexts. Case studies from globally significant heritage cities—including Venice (Italy), Erbil Citadel (Iraq), Rhodes Medieval City (Greece), and contemporary emerging-market examples—demonstrate how integrated planning approaches, stakeholder engagement, and technological innovation can balance preservation and development. Geographic Information Systems (GIS), three-dimensional scanning, virtual reality reconstruction, and Building Information Modeling (BIM) technologies provide digital documentation and spatial analysis capabilities supporting heritage-conscious urban planning. The research reveals that successful heritage preservation in urbanizing contexts requires interdisciplinary integration combining urban planning, conservation expertise, community participation, environmental sustainability, and technological capacity. Rather than positioning heritage conservation as obstacle to urban development, evidence demonstrates that culturally-informed urban planning creates multiple co-benefits: enhanced urban resilience, community identity strengthening, tourism revenue generation, economic revitalization through adaptive reuse, and environmental sustainability through preservation of existing structures. The findings establish that heritage preservation represents essential component of sustainable urban development rather than competing priority. Policy analysis reveals that World Heritage Site designations, historic urban landscape frameworks, adaptive reuse incentive structures, and participatory governance mechanisms create enabling conditions for heritage integration into urban planning. However, implementation gaps persist between policy frameworks and practical application, reflecting capacity constraints in many cities, competing economic pressures, and insufficient integration between heritage and urban development institutions. This interdisciplinary investigation provides evidence-based recommendations for urban planners, policymakers, conservation professionals, and community leaders seeking to advance heritage-integrated urban development models that respect cultural identity while accommodating necessary urban growth.
Sustainable Urban Development; Cultural Heritage Preservation; Urban Expansion; Historic Urban Landscape (HUL); Adaptive Reuse of Heritage Buildings; GIS-Based Heritage Planning; Digital Heritage Documentation; Community-Based Heritage Management.
SELF-HEALING DATA PIPELINES LEVERAGING AGENTIC AI FOR AUTONOMOUS MONITORING AND OPTIMIZATION IN AWS ENVIRONMENTS
Modern data engineering demands resilient, self-sustaining pipeline architectures capable of adapting to failures without human intervention. This paper presents a comprehensive framework for self-healing data pipelines leveraging agentic AI within Amazon Web Services (AWS) environments. Traditional data pipelines remain vulnerable to cascading failures, schema drift, resource bottlenecks, and latency anomalies, resulting in significant operational overhead and data quality degradation. The proposed framework integrates agentic AI models capable of autonomous decision-making, enabling real-time monitoring, root cause analysis, and corrective action execution across distributed pipeline components. By harnessing AWS-native services including Amazon CloudWatch, AWS Lambda, AWS Glue, and Amazon EventBridge, the system continuously evaluates pipeline health metrics and autonomously triggers remediation workflows without manual intervention. Reinforcement learning and large language model-driven agents are employed to detect anomalies, predict failure patterns, and dynamically optimize resource allocation, throughput, and scheduling. Experimental evaluations demonstrate significant reductions in pipeline downtime, improved data quality scores, and enhanced operational efficiency compared to conventional rule-based monitoring approaches. The framework establishes a scalable, cost-effective paradigm for autonomous data infrastructure management, positioning agentic AI as a transformative enabler for next-generation cloud-native data engineering.
Agentic AI, Self-Healing Pipelines, AWS Data Engineering, Autonomous Monitoring, Anomaly Detection.
AGENTIC AI-DRIVEN AUTONOMOUS DATA PLATFORMS FOR SCALABLE CLOUD-NATIVE ARCHITECTURES
The rapid evolution of artificial intelligence has catalyzed a paradigm shift in how data platforms are designed, deployed, and managed at scale. This paper investigates the integration of agentic AI systems within autonomous data platforms built upon cloud-native architectures, exploring how intelligent agents can independently orchestrate data pipelines, optimize resource allocation, and adapt to dynamic workload demands without continuous human intervention. Agentic AI, characterized by goal-directed reasoning, autonomous decision-making, and multi-agent collaboration, presents transformative potential for eliminating operational bottlenecks inherent in traditional data infrastructure. By leveraging containerization, microservices, and serverless computing paradigms, these platforms achieve elastic scalability while maintaining fault tolerance and cost efficiency. This research examines architectural frameworks that enable AI agents to perform self-healing, predictive auto-scaling, intelligent data governance, and real-time anomaly detection across distributed cloud environments. Furthermore, the paper discusses challenges including agent alignment, latency constraints, security vulnerabilities, and interoperability across heterogeneous cloud ecosystems. Experimental analysis demonstrates that agentic AI-driven platforms significantly outperform conventional rule-based automation in throughput, resource utilization, and mean time to recovery. The findings contribute a robust architectural blueprint for enterprises seeking to modernize data infrastructure through intelligent autonomy.
Agentic AI, Autonomous Data Platforms, Cloud-Native Architecture, Scalable Data Infrastructure, Multi-Agent Orchestration.
Everyday hundreds of millions people listen music. Music serves as a powerful, accessible tool for enhancing mental well being aiding in emotional regulation, stress reduction and cognitive function .It is purely based on emotions. Music has been with humans since our earliest existence. The power of music is undeniable. It can evoke emotions & take us to different places & times. Music can be powerful tool for self-expression & emotional regulation. Listening to music can shift how we feel almost instantly. It can boost energy & happiness while slower tracks can help us to process sadness & calm down. It helps in releasing the emotions such as by crying or dancing. Music helps to express the feeling which is not possible in words.
CROPGUARD: AI-POWEREDREAL-TIME CROP DISEASE DETECTION AND PRECISION PESTICIDE SPRAYER
Crop diseases are a major challenge to agricultural productivity and sustainable farming. Early detection and proper treatment of plant diseases are essential to reduce crop loss and improve yield. Traditional methods of detecting crop diseases mainly depend on manual inspection by farmers or experts, which can be time-consuming, labor-intensive, and sometimes inaccurate. This project presents an intelligent robotic system called CropGuard for real-time crop disease detection and precision pesticide spraying using artificial intelligence and embedded systems.The proposed system captures images of crop leaves using an ESP32-CAM module while the robot moves through the farming field. The captured images are processed using a trained machine learning model that analyzes the plant condition and classifies it into three categories: healthy crop, low disease infection, or high disease infection. Based on the classification result, the system automatically activates a precision pesticide sprayer to spray chemicals only on the affected plants. This targeted spraying method helps reduce excessive pesticide usage and minimizes environmental damage.The robot is powered by a 12V battery supported by an 8V solar panel, making it suitable for long-term field operation. Navigation is controlled using an ESP32 microcontroller and motor driver, and a blue mat placed at the end of the field helps the robot detect the boundary and return automatically. The proposed system provides an efficient and low-cost solution for smart agriculture by enabling automated disease detection, reducing human effort, and improving crop management.
ESP32 Cam, Image Processing, Crop Disease Detection, Pesticide Spraying, Robot move forward, Blue mat.
Sahel B, Sahir S, Sushmith S, DR. K .Selvavinayaki
AN ADAPTIVE GA-OPTIMIZED AUTOMATED X-LEARNING MODEL FOR INTEGRATED SOFTWARE RELIABILITY PREDICTION
Software reliability is a significant challenge for software companies today, enabling the development of high-quality, stable, and sustainable software systems. Many existing approaches lack accuracy, are poor in prediction, and fail to handle the highly complex software bugs. To overcome these challenges, we have presented an Adaptive Genetic Algorithm (GA)-Optimized Automated X-Learning Model (OA-XLM) for integrated software reliability prediction. The main aim of this model is to predict and detect the accuracy bugs (software threats) from the latest software datasets. The proposed approach combines an adaptive genetic algorithm (AGA) to find an accurate Learning rate, an in-depth selection of intelligent features, and hyper parameters optimization. Here, the automated X-learning model (XLM), also known as Extreme-Learning, performs bug prediction and failure rate estimation. The XLM integrates multiple learning layers to capture both linear and nonlinear relationships in software data. Finally, the XLM also classifies the software bugs with binary classification. This model was primarily developed to adapt dynamically across complex software projects. In this work, two benchmark datasets are used, namely the NASA and PROMISE repositories. The results show that the proposed approach achieved high prediction and classification rates.
Adaptive Genetic Algorithm, Optimized Automated X-Learning Model, Software Bugs, X-Learning Model.
Shaik Shakeer Basha, Dr.R.Satya Prasad, Dr.Syed Khasim
Copyright (c) 2025 International Journal of Innovation Studies
PROBLEMATIC SMARTPHONE USE AND PSYCHOLOGICAL WELL-BEING AMONG COLLEGE STUDENTS OF SAMASTIPUR
The fast growth of smartphones has significantly transformed the lifestyle of young people, particularly college students. Smartphones indeed provide academic, social, and informational benefits, but their excessive use has raised concerns regarding psychological well-being. The present study intended to examine the relationship between problematic smartphone use and psychological well-being among undergraduate students in Samastipur town. A total of 148 students from different academic disciplines participated in the study. Problematic smartphone use was measured using the Kwon et al. (2013) Smartphone Addiction Scale (Short Version), and psychological well-being was measured using Ryff’s Psychological Well-Being Scale (Ryff and Keyes, 1995). Pearson’s product–moment correlation analysis revealed significant negative correlations between problematic smartphone use and overall psychological well-being. Significant negative associations were also observed with autonomy, personal growth, environmental mastery, purpose in life, and self-acceptance, whereas the association with positive relations was negative but not statistically significant. The findings show that higher levels of problematic smartphone use are linked with lower psychological functioning among college students. This study highlights the urgent of balanced digital usage and self-regulation strategies. Social and institutional interventions may be initiated to promote psychological well-being and raise awareness to limit screen time and smartphone dependency among youth.
Smartphone addiction, psychological well-being, autonomy, environmental mastery
EXPLORING THE EFFECT OF MENTAL TOUGHNESS AND LOCUS OF CONTROL ON SUBJECTIVE WELL-BEING OF COLLEGE STUDENTS
This study was undertaken with a view to ascertain the effect of mental toughness and locus of control on the subjective well-being of college students. Subjective well-being is a broader concept that encompasses three major components: life satisfaction, positive affect, and negative affect. This study was conducted on a sample of 284 college students selected by the purposive sampling technique. Data were obtained through Dienner’s SPANE and Life Satisfaction Scale, along with Denovan et al. (2024) mental toughness scale, and Pearlin and Schooler’s (1978) Mastery Scale. Using correlational and regression analyses, it was found that mental toughness was positively associated with life satisfaction, while locus of control was positively linked to positive affect and negatively associated with negative affect. Regression results further revealed that mental toughness emerged as a significant forecaster of life satisfaction. On the other hand, locus of control has emerged as a significant predictor of positive and negative affect. The findings highlight the importance of psychological factors, namely mental toughness and belief system, in determining students’ mental health and well-being.
Subjective Well-being, Life Satisfaction, Positive Affect, Mental Toughness, Locus of Control
Dr. Priyanka Rai, Dr. Reeta Kumari, Ruby Kumari
Optimized Hybrid Rotation Invariant LBP fused Lightweight MobileNetV2 Model for Deep Visual Inspection of Apple Bruises
Bruise detection in apples is a critical task which is faced in post-harvest quality control of the fruit. The bruises significantly impact consumer preference, shelf life, and overall market value of the fruit. Traditional inspection methods are labor-intensive, subjective and prone to inconsistencies. In order to cope up with huge number of production demands, the real necessity is felt in the development of efficient, automated, and non-destructive techniques classification of the fruit. In this study, we have proposed a optimized rotation invariant local binary pattern (LBP) model fused with the light weight MobileNetV2. LBP offers local texture feature of the fruit. The rotation invariant feature of LBP avoids the rotational effects, likely to occur at the time of image capturing of the samples. The performance of hybrid model has been assessed by several metrics. The result of assessment was estimated at 98% for classification accuracy and a 0.98 for AUC measure of ROC curve, the model's initial findings demonstrates its efficacy.
Pritha Chakraborty, Gunjan Mukherjee, Jayanta Aich
MAPPING RISK-RETURN PERCEPTIONS ACROSS INVESTMENT TYPES: SOCIO-DEMOGRAPHIC INSIGHTS FROM GUJARAT STATE USING NON-PARAMETRIC METHODS
This study examines how investor demographics (gender, age, income, education, occupation, and region) influence perceived risk and return of various investment types in Gujarat, India. We surveyed 573 investors about their risk-return ratings for bank deposits, post office schemes, mutual funds, equity shares, debt fund instruments, precious metals (gold/silver), and real estate. Using Mann-Whitney U and Kruskal-Wallis tests, we tested for differences in perceptions across demographic groups. Results show that government-backed schemes (fixed deposits and post office savings) are viewed as the safest but with the lowest returns, whereas equity shares are seen as highest risk with the highest return. Notably, perceptions for mutual funds and shares vary significantly by demographics – age, income, occupation, and gender all influence these ratings. These findings underline distinct socio-demographic segmentation in investor attitudes. Therefore, personalized financial literacy initiatives and product designs tailored to different demographic segments are needed to align investment offerings with investors’ risk preferences.
Investor behavior, risk-return perception, socio-demographic factors, non-parametric analysis
Dr. Disha Hardik Patel, Dr. Brijesh M. Patel, Dr. Jenitaben Patel, Dr. Bhautik A. Patel