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.
LINKING HR PRACTICES AND ORGANIZATIONAL PERFORMANCE IN THE PRINT MEDIA INDUSTRY: THE MEDIATING ROLE OF EMPLOYEE ENGAGEMENT
The print media industry in India is navigating profound structural disruption precipitated by digital convergence, declining print circulation, fragmented advertising revenues, and escalating talent attrition. Against this backdrop, strategic human resource management (HRM) has emerged as a pivotal determinant of organizational survival and competitive performance. This study investigates the influence of four HR practice dimensions-Employee Training and Upskilling, Leadership Support, Organizational Culture, and Work-Life Balance Practices-on Organizational Performance, with Employee Engagement serving as the mediating variable. Grounded in Social Exchange Theory and the Job Demands-Resources (JD-R) Model, the study adopts a quantitative cross-sectional design. Primary data were collected from 165 print media professionals employed across editorial, advertising, production, circulation, and digital departments in Chennai, Tamil Nadu, India. Statistical analysis was conducted using IBM SPSS Statistics v26, employing Pearson bivariate correlation, multiple regression, and Baron and Kenny's (1986) four-step regression-based mediation procedure. The empirical results demonstrate that all four HR practice dimensions exert significant and positive effects on Employee Engagement. Among the predictors, Organizational Culture (β = 0.281) and Employee Training and Upskilling (β = 0.271) are the most influential, followed by Leadership Support (β = 0.228) and Work-Life Balance Practices (β = 0.217). Employee Engagement, in turn, strongly and significantly predicts Organizational Performance (β = 0.614, R² = 0.377). Mediation analysis reveals partial mediation: the direct effect of HR practices on Organizational Performance diminishes from β = 0.512 to β = 0.295 when Employee Engagement is introduced as a mediator, yet remains statistically significant, confirming that Employee Engagement is a critical yet non-exclusive pathway through which HR investments translate into performance outcomes. The study makes an original contribution to the strategic HRM literature by providing the first sector-specific empirical evidence from the Chennai print media industry and offers actionable guidance for HR practitioners committed to sustaining performance through engagement-centred people management systems.
FINANCIAL PERFORMANCE OF FAMILY RUN- BUSINESSES WITH SPECIAL REFERENCE TO NSE INDIA
ABSTRACT This study delves into the financial dynamics of family-run businesses within the context of NSE India, with a focal point on examining the correlation between dividend policy and financial performance. Analyzing time series data spanning from 2015 to 2022, the study employs bivariate correlation and linear regression techniques to fulfill its objectives. The findings reveal a robust positive correlation between dividend decisions made by family members and return on assets, indicating that increasing dividend payouts can enhance overall return on assets. Emphasizing the crucial balance between enterprise value and financial performance, the study underscores the significance of sustainable growth and financial stability in the context of family-run enterprises. By shedding light on the impact of dividend policy on financial performance, particularly within family-owned businesses, this research contributes valuable insights to the existing literature..
Dr. Frederick Sidney Correa, Amrita Bajpai, Dr. Shruti Gaur
USING PCA LOADINGS AS PORTFOLIO WEIGHTS: EVIDENCE FROM THE SAUDI EQUITY MARKET (TADAWUL)
Portfolio construction lies at the core of modern investment management, providing a systematic framework for allocating capital across assets to optimize returns relative to risk. Traditional portfolio construction approaches, such as equal weighting or capitalization weighting, fail to adequately account for these complex interrelationships. Even conventional optimization techniques may struggle when the covariance structure among sectors is unstable or when the number of sectors approaches the number of historical observations, leading to estimation errors and suboptimal outcomes (Michaud, 1989). To address these challenges, researchers and practitioners increasingly employ data-driven techniques such as Principal Component Analysis (PCA) to extract latent factors that explain the co-movement of asset returns (Connor & Korajczyk, 1986; Pástor & Stambaugh, 2003). The primary contributions of this study are threefold. First, it enhances the understanding of sectoral dynamics within the Saudi equity market by identifying latent economic factors driving sector co-movements. Second, it demonstrates how PCA loadings can be transformed into actionable portfolio weights, offering a transparent and economically grounded alternative to conventional optimization methods. Third, it provides empirical evidence on the risk-return trade-offs of PCA-weighted portfolios relative to traditional strategies, highlighting the practical benefits and limitations of this approach.
Rami Kaki, Dr. Abdul Malik Syed, Dr. Robert Seinfield
ECO – FRIENDLY PROTON BATTERY BASED ON PVA, PROLINE AND NH4NO3
Eco-friendly proton-conducting solid polymer electrolytes were successfully developed using poly(vinyl alcohol) (PVA), proline, and ammonium nitrate (NH₄NO₃) through the solution casting technique. Polymer electrolyte membranes with different NH₄NO₃ concentrations (0.1–0.4 wt%) were prepared and characterized by using X-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), AC impedance spectroscopy, transference number measurement and linear sweep voltammetry (LSV). XRD analysis revealed that the incorporation of NH₄NO₃ reduced the crystallinity of the PVA–proline matrix and enhanced its amorphous nature, while DSC results showed a significant decrease in the glass transition temperature, indicating improved polymer chain flexibility. FTIR spectra confirmed the complex formation and molecular interactions among PVA, Proline and NH₄NO₃. The polymer electrolyte containing 75 Mwt% PVA: 25 Mwt% Proline: 0.3 Mwt% NH₄NO₃ exhibited the highest ionic conductivity of 7.76 × 10⁻⁴ S cm⁻¹ at room-temperature with a low activation energy of 0.18 eV, demonstrating efficient proton transport. The electrolyte also exhibited a high ionic transference number (t₍ion₎ = 0.9753) and a wide electrochemical stability window of 3.23 V, confirming its suitability for electrochemical applications. A primary proton battery fabricated using the optimized polymer electrolyte delivered an open-circuit voltage of 1.73 V and maintained stable discharge characteristics. These findings demonstrate that the PVA–proline–NH₄NO₃ polymer electrolyte is a promising, biodegradable and environmentally friendly candidate for next-generation proton batteries and other solid-state electrochemical energy storage devices.
R. Hemalatha, M. Nithya, R. Hepzi Pramila Devamani
INTELLIGENT ADAPTIVE CONTROL APPROACHES FOR IMPROVING ACCURACY IN ROBOTIC MANIPULATOR SYSTEMS
Robotic manipulators are increasingly required to execute high speed, disturbance tolerant tasks in manufacturing, service robotics, medical assistance and automated inspection. Conventional proportional integral derivative and fixed gain model based controllers often lose accuracy when payload, friction, backlash or unmodelled nonlinear dynamics vary during operation. This article develops a manuscript on intelligent adaptive control for improving trajectory tracking accuracy in robotic manipulator systems. The proposed approach combines adaptive gain updating, neural uncertainty approximation, fuzzy error compensation and sliding mode robustness within a supervisory control architecture. An illustrative experimental dataset of 186 manipulator trajectory trials was structured across three payload levels and variable motion speeds. Five statistical tests were applied to evaluate normality, paired accuracy improvement, payload wise performance differences, adaptation tracking association and multivariate predictors of residual error. Results show that the intelligent adaptive controller reduced mean root mean square tracking error from 2.056 mm under conventional PID control to 0.748 mm, representing a 63.61 percent improvement. Paired t testing confirmed a statistically significant accuracy gain, while regression analysis indicated that the adaptation index was the strongest predictor of lower residual error. The study demonstrates that intelligent adaptive control can improve precision, settling response and overshoot control when manipulator dynamics are uncertain. Findings support the integration of learning based compensation with stability preserving nonlinear control for next generation industrial manipulators requiring reliable accuracy under changing operating conditions.
Dr. N.Rishi Kanth, Dr Pabbathi Vijaya Rao, Dr.G.Surya Prakash Rao
SCALABLE TIME SERIES SIMULATION USING PROXY-BASED MODELING TECHNIQUES
The fast development of large-scale sequential data obtained from financial systems, IoT devices, cloud platforms, and industrial applications, has led to an increasing demand for scalable and computationally efficient time series simulation methodologies. Existing simulation approaches are generally limited by their scalability, memory consumption and computing cost when working on large-scale data. In this paper we present a scalable time series simulation framework by applying proxy-based modelling techniques for improving the computing efficiency while preserving the forecast accuracy. The architecture we offer exploits distributed computing technologies, machine learning based proxy models and parallel processing techniques for effective simulation of high-volume time series data. The experimental evaluation was performed on benchmark and generated datasets in a distributed cloud-based setting. The results showed considerable increases in execution speed, scalability efficiency, resource utilisation and prediction accuracy compared with conventional simulation methods. Statistical analysis employing metrics such as Mean Squared Error (MSE), Root Mean Square Error (RMSE) and correlation analysis proved the dependability and efficacy of the suggested framework. In summary, the study demonstrates that proxy-based modelling techniques can be a viable approach to support real-time and large-scale time series simulation applications in modern data-intensive systems.
EFFECTIVENESS OF A BASNEF MODEL–GUIDED EDUCATIONAL INTERVENTION ON NURSES' PERFORMANCE IN CARING FOR CHILDREN WITH ACUTE LUNG INJURY
Background: Acute lung injury (ALI) is a serious pulmonary condition that commonly results from diseases such as pneumonia and sepsis. It contributes significantly to morbidity, prolonged hospitalization, reduced quality of life, and increased healthcare burden. Aim: This study aimed to evaluate the effectiveness of a BASNEF model–guided educational intervention on nurses’ performance in caring for children with acute lung injury. Design: A quasi-experimental research design was utilized. Settings: The study was conducted in pediatric intensive care units and pediatric wards in one university hospital and three government hospitals in Menoufia, Egypt. Sample: A total of 100 nurses caring for children with Acute lung injuries were recruited and assigned either to the intervention group (n = 50), which received an educational program based on the BASNEF model, or to the control group (n = 50). Tools: Three data collection tools were used, employing structured questionnaires to assess nurses' knowledge, beliefs, attitudes, subjective norms, enabling factors, and performance in caring for children with acute pulmonary injury. Results: The results indicate that the BASNEF-based educational intervention had a significant positive impact on all measured domains in the experimental group compared to the control group, with statistically significant differences after the intervention (p < 0.001). These improvements were reflected in more accurate clinical practices and adherence to respiratory management guidelines. Conclusion: The BASNEF-guided educational intervention was effective in improving nurses' performance in caring for children with acute lung injuries. Recommendation: BASNEF-based educational programs should be incorporated into nursing training to enhance nurses' competencies and improve the quality of respiratory care provided to children with acute lung injuries.
Hanaa El Sayed, Azza Anwar Aly, Mervat Ali khamis, Hanaa M. Ahmed
ADAPTIVE CONTROL STRATEGIES FOR PRECISION ENHANCEMENT IN ROBOTIC MANIPULATOR SYSTEMS
Adaptive control strategies have become a significant advancement in modern robotic manipulator systems, particularly in environments requiring high precision, adaptability, and real-time response. This study focuses on the role of adaptive control techniques in enhancing the performance of robotic manipulators by improving accuracy, stability, and reducing system errors under dynamic working conditions.The research examines key variables such as Adaptive Gain Tuning, Sensor Feedback Accuracy, System Responsiveness, and Computational Delay, and their impact on Precision Enhancement in robotic systems. The study is based on a structured quantitative approach using data collected from 108 respondents including robotics engineers, automation professionals, and researchers. The Structural Equation Modeling (SEM) technique using AMOS software is applied to analyze relationships between variables and validate the proposed conceptual framework. The results indicate that Adaptive Gain Tuning and Sensor Feedback Accuracy have a strong positive influence on precision enhancement, while Computational Delay negatively affects system performance. System Responsiveness plays a supporting role in improving trajectory tracking and operational stability. The model demonstrates good statistical fit and explains a significant level of variance in robotic precision. The study concludes that adaptive control strategies are essential for improving robotic manipulator performance and are highly relevant for industrial automation, intelligent manufacturing, and advanced robotic applications.
INFLUENTIAL FACTORS IDENTIFICATION AND ANALYSIS ON SELECTIVE LEAN MARKETING COMPONENTS OF E GROCERY RETAILERS IN TELANGANA
Lean marketing emphasizes efficiency, agility, and continuous improvement within the marketing process. This approach is particularly relevant in today’s fast-paced, data-driven marketplace, where businesses must adapt quickly to meet changing consumer demands. The study focuses on the key factors which are influencing the Lean marketing and measures weights of their individual variables to recognize, what is leading to a successful decision making in terms of awareness and perception. The researcher has adopted the CFA model by defining the exploratory factor analysis to identify the communalies of each variable as a good indicator, measured the Eigen values, used rotated component matrix which displays the factor loadings to represent how strongly each variable correlates to the components in Lean marketing to assess and measure the impact of variables on Retailers awareness and perception on E groceries from the five components. To assess and explore the more potential factors, through composite, convergent and Discriminant validity. The research been led to many of the worthful insights. Perceptions and awareness is crucial when E grocery retailers takes decision on lean readiness and Implementation. From the 30 sub-factors in the selected fives criteria’s, the most important factors include study on Internal, External influences, Awareness, Perception and Readiness of E grocery retailers, clearly defined the importance and benefits of lean marketing practices, organizational readiness and commitment, to internal support and preparedness for lean marketing, Measures the level of awareness and understanding, Captures external pressures. The results could be utilized by the E grocery retailers to improve their awareness and knowledge when implementing the Lean marketing practices.
Mrs. Ashita, Dr. Saumendra Das, Prof. Shehbaz Ahmed,
NEUTROSOPHIC g HOMEOMORPHISM IN NEUTROSOPHIC TOPOLOGICAL SPACES
In this paper, we introduce the concepts of Neutrosophic g Homeomorphisms and Neutrosophic g homeomorphisms. Further, we study some of their properties.
A STUDY ON THE IMPACT OF ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL MARKETING ON CUSTOMER ATTRACTION IN CHENNAI CITY RESTAURANTS
Artificial Intelligence (AI) has transformed digital marketing by enabling businesses to deliver personalised, data-driven, and automated customer experiences. In the restaurant industry, AI-driven digital marketing tools such as personalised advertisements, chatbots, social media analytics, recommendation systems, customer data analytics, and automated marketing communication are increasingly being used to attract and engage customers. The present study aims to examine the impact of AI-driven digital marketing on customer attraction in Chennai City restaurants. The study adopted a descriptive research design and utilised both primary and secondary data. Primary data were collected through a structured questionnaire administered to 180 restaurant customers in Chennai City using the proportionate random sampling technique. The collected data were analysed using Percentage Analysis, Mean Score Analysis, Independent Sample t-Test, Chi-Square Test, Analysis of Variance (ANOVA), Correlation Analysis, and Multiple Regression Analysis. The findings reveal that customers possess a high level of awareness and favourable perceptions regarding AI-driven digital marketing practices. Personalised advertisements, social media analytics, recommendation systems, and customer data analytics significantly influence customer attraction and restaurant selection decisions. The study also found significant associations between demographic variables and customer awareness of AI-driven digital marketing. Correlation analysis indicated a strong positive relationship between AI-driven digital marketing factors and customer attraction dimensions. Multiple regression analysis confirmed that social media analytics, personalised advertisements, and customer data analytics are the most influential predictors of customer attraction and restaurant selection decisions. The study concludes that AI-driven digital marketing has a significant positive impact on customer attraction in Chennai City restaurants. Effective adoption of AI-powered marketing strategies enhances customer awareness, engagement, visit intention, and restaurant preference, thereby improving restaurants' competitive advantage and supporting business growth in an increasingly digital environment.
PRODUCT OF TOTAL AND RANGE FUZZY EDGE LABELING OF CERTAIN GRAPH FAMILIES
Fuzzy graph labeling deals with the assignment of membership values to the vertices and edges of a graph under prescribed constraints. In this paper, a product of total and range fuzzy edge labeling is introduced. A graph is said to admit this labeling if there exist bijections and such that and for all . Graphs satisfying these conditions are identified as product of total and range fuzzy edge labeling graphs. Using this framework, it is shown that the path graph, cycle graph, friendship graph, triangular snake graph, triangular book, generalized butterfly graph, ladder graph and coconut tree graph admit such a labeling.
THE Y-INDEX FOR DOUBLE CORONA OF GRAPHS RELATED TO THE DIFFERENT SUBDIVISION GRAPHS
Abstract: Graph operations play a significant role in generating complex molecular and network structures from the simpler graphs. The computation of degree-based topological indices of such derived structure is an essential problem in chemical graph theory due to their applications in QSAR/QAPR studies. Among them, the Y index is a recently studied degree-based invariant that extends the family of Zagreb-type indices and captures structural information of graphs more efficiently. In this paper, we generalize the concepts of double corona graphs associated with subdivision-related graph operations and derive exact analytical expressions for the Y-index of these constructions. Closed-form formulae are obtained for the subdivision and total graph based double corona products in terms of standard graph invariants and degree parameters. The obtained result extends the theoretical framework of degree-based indices under complex graph operations such as PANAM dendritic polymers and provide useful mathematical tools for modeling intricate molecular networks.
S. Santhiya, Dr. O. V. Shanmuga Sundaram
STUDYING LADY MACBETH AS AN AGGRESSIVE WOMAN: A CRITICAL ANALYSIS OF SHAKESPEARE'S MACBETH
William Shakespeare's Macbeth presents one of the most complex female characters in English literature through the figure of Lady Macbeth. She is often regarded as an aggressive woman whose ambition, determination and desire for power challenge the conventional expectations of femininity in the Elizabethan era. This research paper critically examines Lady Macbeth's aggression and its relationship to ambition, power, morality and psychological decline. The study explores how her aggressive nature motivates Macbeth to commit regicide and pursue political authority. It also analyses her rejection of traditional feminine qualities and her use of psychological manipulation to dominate her husband. Furthermore, the paper investigates the connection between Lady Macbeth's desire for power and her eventual moral corruption. Although she initially appears strong and fearless, her inner guilt gradually destroys her mental stability, leading to psychological breakdown and death. Through textual analysis and scholarly interpretations, this paper argues that Lady Macbeth's aggression is both a source of strength and a cause of destruction. Shakespeare portrays her as a woman who challenges gender norms but ultimately becomes a victim of the consequences of unchecked ambition. The study contributes to a deeper understanding of Lady Macbeth's character by examining the complexity of her actions and motivations. Rather than viewing her solely as a villainous figure, the paper presents her as a tragic character whose pursuit of power results in emotional and psychological suffering. The analysis demonstrates how Shakespeare uses Lady Macbeth to explore themes of ambition, gender, power, guilt and human vulnerability.
HRM PRACTICES AND FACULTY PERFORMANCE IN HIGHER EDUCATIONAL INSTITUTIONS: SPECIAL REFERENCE TO CHENNAI
The higher education sector in India is undergoing transformative change driven by global competition, regulatory reforms, and evolving student expectations. Human Resource Management (HRM) practices are increasingly recognised as pivotal levers for enhancing faculty performance and, by extension, institutional excellence. This study investigates the influence of HRM practices — namely recruitment and selection, training and development, performance appraisal, compensation management, faculty empowerment, and work-life balance — on the performance of faculty members in higher educational institutions (HEIs) in Chennai, Tamil Nadu. A descriptive and analytical research design was adopted. Using stratified random sampling, data were collected from 385 faculty members across 20 HEIs in Chennai through a structured, validated questionnaire. Statistical tools including descriptive statistics, Pearson correlation, multiple regression analysis, ANOVA, and structural equation modelling (SEM) were employed. The findings reveal that training and development (β = 0.412, p < 0.001), performance appraisal (β = 0.368, p < 0.001), and compensation management (β = 0.291, p < 0.01) are the strongest predictors of faculty performance. A significant positive correlation was found between overall HRM practices and faculty performance (r = 0.782, p < 0.001). The model explains 68.4% of the variance in faculty performance (R² = 0.684). The study provides actionable insights for HEI administrators and policymakers to design evidence-based HR strategies that nurture faculty potential and elevate institutional quality.
SMART FINANCE, SMARTER DECISIONS: THE ROLE OF AI AND DIGITAL INNOVATION IN REVOLUTIONIZING FINANCIAL MANAGEMENT
Abstract: In the era of digital transformation, organizations are increasingly relying on advanced technologies and financial competencies to improve decision-making effectiveness. This study examines the impact of AI Capability and Digital Innovation on Financial Analytics Capability and further investigates how Financial Analytics Capability influences Decision Effectiveness. Additionally, the study explores the mediating role of Financial Analytics Capability between AI Capability and Decision Effectiveness, along with the moderating role of Financial Literacy in strengthening the relationship between Financial Analytics Capability and Decision Effectiveness. The study is based on primary data collected through a structured questionnaire from 423 respondents across different industries. The data were analyzed using SPSS, employing regression analysis, mediation, and moderation techniques to test the proposed hypotheses. The findings reveal that both AI Capability and Digital Innovation significantly enhance Financial Analytics Capability. Financial Analytics Capability is also found to have a significant positive effect on Decision Effectiveness. Moreover, it partially mediates the relationship between AI Capability and Decision Effectiveness, while Financial Literacy significantly strengthens the impact of Financial Analytics Capability on Decision Effectiveness. The study contributes to the literature on digital transformation and decision-making by integrating technological and financial dimensions into a unified framework. It also provides practical implications for organizations by highlighting the importance of investing in AI technologies, digital innovation, and financial literacy to improve data-driven decision-making outcomes.
Dr. Anjali Pandey, Dr. Shivani Mishra, Dr. Nitin Patidar, Dr. Anubhuti Sharma, Dr. Sonali Solanki
MODERN MATHEMATICAL MODELING AND OPTIMIZATION STRATEGIES FOR ENGINEERING PROBLEM-SOLVING
Mathematical modeling and optimization techniques play a fundamental role in engineering problem-solving by enabling the formulation, analysis, and solution of complex real-world systems. Traditional deterministic models, while effective for well-defined problems, often fail to address the increasing complexity, uncertainty, and multi-dimensional nature of modern engineering challenges. This study presents a comprehensive framework integrating modern mathematical modeling approaches with advanced optimization strategies to improve decision-making and system performance. The research examines linear and nonlinear models, stochastic systems, and computational optimization techniques, including heuristic and metaheuristic algorithms. A hybrid methodological approach combining analytical modeling, simulation, and optimization analysis is employed to evaluate system efficiency across multiple engineering domains. The findings demonstrate that modern optimization strategies significantly enhance solution accuracy, computational efficiency, and adaptability in dynamic environments. However, challenges related to computational complexity and model scalability remain critical. The study contributes to the advancement of engineering methodologies by providing a structured approach to integrating mathematical modeling with intelligent optimization techniques.
Mathematical Modeling, Optimization, Engineering Systems, Linear Programming, Nonlinear Optimization, Heuristics, Simulation, Decision-Making
Dr.Madduru Sambasivudu1, Medoji Laxman Chari2, Dr. Ch.V.Phani Krishna3 , Dr. Sunil Kumar Thota, Sushma Arayal, Subash Kumar Bhattarai
AI-ENABLED MEDICAL IMAGING AND DEEP LEARNING TECHNIQUES FOR IMPROVED DIAGNOSIS AND TREATMENT PLANNING
The integration of artificial intelligence (AI) and deep learning into medical imaging has revolutionized diagnostic processes and treatment planning in modern healthcare systems. Traditional imaging analysis methods rely heavily on human expertise, which can be time-consuming and prone to variability. AI-enabled systems, particularly those based on deep learning architectures such as convolutional neural networks (CNNs), have demonstrated significant potential in automating image interpretation, improving diagnostic accuracy, and supporting clinical decision-making. This study presents a comprehensive analytical framework for evaluating AI-driven medical imaging systems, focusing on their application in disease detection, image segmentation, and predictive analytics for treatment planning. The research adopts a hybrid methodological approach integrating image processing, model training, and performance evaluation to assess the effectiveness of deep learning techniques. The findings indicate that AI-based imaging systems significantly enhance diagnostic precision, reduce processing time, and enable personalized treatment strategies. However, challenges related to data quality, model interpretability, and ethical considerations remain critical. The study contributes to the advancement of intelligent healthcare systems by providing a structured framework for integrating AI technologies into clinical workflows.
Medical Imaging, Deep Learning, AI in Healthcare, CNN, Diagnosis, Image Segmentation, Predictive Analytics
Dr S V N Sreenivasu, Dr. Ashish Kumar Tamrakar, Dr. Ch. Rathan Kumar, Dr. Ch. Sita Kameswari, Dr. Sunil Kumar Thota, Dr. Nirbhan Singh
APPLICATION OF SMART PROCESSING TECHNOLOGIES TO ENHANCE NUTRITIONAL QUALITY AND SHELF LIFE OF PROCESSED FOODS
The increasing demand for safe, nutritious, and long-lasting food products has driven the adoption of smart processing technologies in the food industry. Traditional food processing methods, while effective in extending shelf life, often lead to significant losses in nutritional quality and sensory attributes. Recent advancements in smart processing technologies, including high-pressure processing (HPP), pulsed electric fields (PEF), cold plasma, and intelligent packaging systems, offer innovative solutions for preserving food quality while enhancing safety and shelf stability. This study develops a comprehensive analytical framework to evaluate the application of smart processing technologies in improving nutritional retention and extending shelf life of processed foods. The research adopts a hybrid methodological approach integrating process modeling, nutritional assessment, and shelf-life evaluation to analyze the effectiveness of these technologies. The findings indicate that smart processing methods significantly reduce nutrient degradation, improve microbial safety, and enhance product stability compared to conventional techniques. However, challenges related to cost, scalability, and regulatory compliance remain critical barriers to widespread adoption. The study contributes to the field of food science by providing a structured framework for integrating advanced processing technologies into sustainable food production systems.
Smart Processing, Food Technology, Nutritional Quality, Shelf Life, High-Pressure Processing, Pulsed Electric Fields, Cold Plasma, Food Preservation
Dr.Satish G Athawale, Dr. Nikhil Digambar Solanke, Abdelsalam Mohammed Daoud Yahya, Dr K Bhargavi, Dr. Leena, Dr. Nirbhan Singh
AI-ENABLED TEACHING AND LEARNING MODELS: TRANSFORMING EDUCATIONAL OUTCOMES THROUGH INTELLIGENT TECHNOLOGIES
The rapid integration of artificial intelligence (AI) into educational systems has fundamentally transformed teaching methodologies, learning experiences, and institutional frameworks. Traditional education models, characterized by standardized curricula and instructor-centered delivery, are increasingly being replaced by adaptive, data-driven learning environments that leverage intelligent technologies to personalize education and enhance learning outcomes. This study develops a comprehensive analytical framework to examine AI-enabled teaching and learning models, focusing on their impact on educational effectiveness, student engagement, and institutional scalability. The research adopts a hybrid methodological approach integrating learning analytics, adaptive system modeling, and pedagogical evaluation to assess the role of AI in modern education. The findings indicate that AI-driven systems significantly improve learning efficiency, personalization, and performance tracking, while also introducing challenges related to data privacy, algorithmic bias, and digital inequality. Furthermore, the study highlights the importance of integrating human-centered pedagogical approaches with intelligent technologies to ensure sustainable and inclusive education systems. The research contributes to the evolving discourse on digital education by providing a structured framework for implementing AI-enabled learning models that balance technological innovation with pedagogical integrity.
Artificial Intelligence in Education, Adaptive Learning, Intelligent Tutoring Systems, Learning Analytics, Digital Education, Educational Technology, Personalized Learning
Dr.V.Srikanth, Guli Ra'nokhon Khamidullaeva, Dr Ugendhar Addagatla, Jaydeep Parmari, Dr. Arpeeta Anand, Dr. Nirbhan Singh
DATA-DRIVEN INTELLIGENT SYSTEMS FOR SIGNAL ENHANCEMENT, NOISE REDUCTION, AND PREDICTIVE ANALYTICS
The increasing complexity of modern data environments has necessitated the development of intelligent systems capable of processing, enhancing, and interpreting signals under conditions of uncertainty and noise. Traditional signal processing techniques, while effective in controlled and stationary environments, often fail to perform adequately in real-world scenarios characterized by nonlinear dynamics, high-dimensional data, and non-stationary noise distributions. This study presents a comprehensive analytical framework for data-driven intelligent systems that integrate machine learning, statistical modeling, and advanced signal processing techniques to achieve robust signal enhancement, noise reduction, and predictive analytics. The research adopts a hybrid methodological approach combining classical filtering methods with deep learning-based models to evaluate system performance across diverse signal conditions. The findings demonstrate that data-driven approaches significantly outperform traditional methods in terms of signal reconstruction accuracy, adaptability, and predictive capability. Furthermore, the integration of predictive analytics enables these systems to forecast future signal behavior, thereby extending their functionality beyond reactive processing to proactive decision-making. The study contributes to the evolving field of intelligent signal processing by providing a unified framework that bridges the gap between classical methodologies and modern data-driven approaches, offering scalable solutions for applications in communication systems, healthcare monitoring, autonomous systems, and industrial analytics.
Signal Enhancement, Noise Reduction, Machine Learning, Predictive Analytics, Deep Learning, Intelligent Systems, Signal Processing, Time-Series Analysis
Mr. Shree S. Kesarkar, Mr. Shravanchandra G, Dr Ranadheer Donthi, Dr. Ch. Rathan Kumari, Gabriel Ayodeji Ogunmola, Dr.G.Prabhakaran
AI-DRIVEN HR, GIG ECONOMY, AND QUICK COMMERCE: REDEFINING WORKFORCE STRATEGY AND BUSINESS SUSTAINABILITY IN THE DIGITAL AGE
The convergence of artificial intelligence (AI), gig economy labor models, and the rapid expansion of quick commerce platforms has fundamentally transformed workforce management and business sustainability strategies in the digital age. Organizations are increasingly adopting AI-driven human resource (HR) systems to optimize talent acquisition, workforce allocation, performance monitoring, and decision-making processes. Simultaneously, the gig economy has redefined traditional employment structures by enabling flexible, on-demand labor models, particularly in sectors such as logistics and quick commerce, where speed and scalability are critical. This study develops a comprehensive analytical framework to examine the interaction between AI-driven HR systems, gig-based workforce structures, and business sustainability outcomes. The research adopts a hybrid methodological approach integrating workforce analytics modeling, platform-based labor analysis, and strategic HR evaluation to assess the implications of these transformations. The findings indicate that while AI-driven HR enhances efficiency, scalability, and data-driven decision-making, it also introduces challenges related to worker precarity, algorithmic bias, and regulatory compliance. Furthermore, the gig economy, while enabling operational flexibility, raises concerns regarding labor rights, job security, and long-term sustainability. The study highlights the need for integrated workforce strategies that balance technological efficiency with ethical considerations and regulatory frameworks. The research contributes to the evolving discourse on digital labor transformation by providing a structured framework for aligning AI-driven HR practices with sustainable business models in the context of platform-based economies.
AI-driven HR, Gig Economy, Quick Commerce, Workforce Strategy, Business Sustainability, Algorithmic Management, Digital Labor, Platform Economy
Dr. Somasekhar Donthu, Dr. Baziga Farooq, Adusumalli Himabindu, Mr. Udayan Giri, Dr N. Subramanian, Dr. Chanchal Chawla
DISCOURSE, POWER, AND MEDIA REPRESENTATION: A CRITICAL ANALYSIS OF LANGUAGE IN THE DIGITAL AGE
The rapid expansion of digital media ecosystems has fundamentally transformed the production, circulation, and consumption of discourse, redefining the relationship between language, power, and representation. In contemporary digital environments, media narratives are no longer shaped solely by traditional institutions but are increasingly influenced by algorithmic systems, platform dynamics, and participatory communication practices. This study develops a comprehensive analytical framework grounded in Critical Discourse Analysis (CDA) to examine how language functions as a mechanism of power within digital media structures. By integrating theoretical perspectives from Foucault’s power–knowledge paradigm, Fairclough’s discourse theory, and van Dijk’s socio-cognitive approach, the research investigates the ways in which linguistic choices, framing strategies, and narrative constructions reproduce, legitimize, and contest power relations in digital contexts. The study adopts a hybrid methodological approach combining discourse coding, narrative analysis, and platform-based comparative evaluation to analyze patterns of representation across digital media environments. The findings indicate that digital discourse is characterized by amplification biases, algorithmic reinforcement of dominant narratives, and the strategic use of language to shape public perception and ideological positioning. Furthermore, the study highlights the emergence of decentralized discourse production, where users actively participate in constructing and contesting narratives, thereby complicating traditional power hierarchies. The research contributes to the field by offering a structured framework for analyzing discourse in digitally mediated environments and by identifying key mechanisms through which power operates in contemporary media systems.
Dr.T.Vishnupriyan, R.Deepa, Dr.Challa Pamuleti, Sumalatha Potteti, Dr. Ch. Sita Kameswari
HARMONIZING FAMILY DYNAMICS AND BUSINESS GOVERNANCE: STRATEGIES FOR SUCCESS
This research paper investigates strategies for harmonizing family dynamics and business governance to achieve success in family-owned businesses. Through interviews and case studies with a purposive sample of family business owners/managers, the study explores the challenges faced and the strategies employed in integrating family values with effective business governance. Findings reveal that successful harmonization involves navigating complex family dynamics, balancing business objectives with familial goals, and establishing clear communication channels. Moreover, the study identifies key factors contributing to successful integration, such as fostering trust, implementing structured governance mechanisms, and cultivating a shared vision among family members. The discussion contextualizes these findings within existing literature on family business management, highlighting implications for both theory and practice. Despite limitations in generalizability due to the qualitative nature of the study and the nature of the data, this research contributes to understanding the intricate dynamics of family businesses and provides actionable insights for practitioners seeking to achieve harmony between familial relationships and business governance structures.
Family dynamics, Business governance, balancing business, Family business management, Intricate dynamics
Dr. Frederick Sidney Correa, Amrita Bajpai, Jagdeep Singh, Karim Soliman
Copyright (c) 2026 International Journal of Innovation Studies
ADVANCED MACHINE LEARNING FRAMEWORKS FOR OPTIMIZATION AND INTELLIGENT CONTROL IN LARGE-SCALE ENGINEERING SYSTEMS
Large-scale engineering systems spanning power grids, industrial manufacturing networks, autonomous transportation infrastructures, and smart city platforms present optimization and control challenges of a complexity and dimensionality that fundamentally exceed the capacity of classical analytical methods, necessitating the development of advanced machine learning frameworks capable of learning adaptive control policies from high-dimensional system data while guaranteeing the safety, stability, and robustness properties that safety-critical engineering deployments demand. This paper presents a comprehensive unified framework integrating deep reinforcement learning, Gaussian process-based Bayesian optimization, physics-informed neural networks, and multi-agent coordination architectures into a hierarchical intelligent control system evaluated across three large-scale engineering benchmarks: a 118-bus power grid with 54 controllable generation units, a 12-stage chemical process plant with coupled nonlinear dynamics, and a 200-node urban traffic network with mixed autonomous and human-driven vehicles. The proposed Hierarchical “Adaptive Machine Learning Control (HAMLC)” framework achieves mean control performance improvements of 23.4 percent over model predictive control baselines, 31.7 percent over classical proportional-integral-derivative control, and 12.1 percent over single-algorithm machine learning benchmarks, while maintaining constraint violation rates below 0.3 percent across all test scenarios. Formal stability guarantees derived from Lyapunov function approximation theory are validated computationally, and the framework demonstrates robust performance degradation under sensor noise, communication delays, and partial observability conditions representative of real-world large-scale engineering deployments. The integration of physics-informed neural network components is shown to reduce the training data requirements by 67 percent compared to purely data-driven baselines, addressing a critical bottleneck for practical deployment in engineering systems where operational data collection is constrained by safety and cost considerations.
Deep Reinforcement Learning, Bayesian Optimization, Physics-Informed Neural Networks, Intelligent Control, Large-Scale Systems, Multi-Agent Systems, Power Grid Control, Process Control, Lyapunov Stability, Engineering Optimization
Dr.Madduru Sambasivudu, Thota Bhavani, Dr. Suresh Palarimath, Dr. Rahul Kumar, Akansh Garg
DIGITAL MODERNITY AND THE EVOLUTION OF NARRATIVE FORM LANGUAGE, IDENTITY AND REPRESENTATION IN CONTEMPORARY ENGLISH FICTION
This paper examines the impact of digital modernity on the evolution of narrative form in contemporary English fiction, with particular focus on the interconnections between language, identity, and representation. Digital modernity, shaped by the proliferation of networked technologies, social media, and algorithm-driven communication systems, has transformed literary production and reception, leading to innovative narrative strategies that challenge traditional linear and realist conventions. Contemporary English fiction increasingly incorporates fragmented structures, multimodal storytelling, and hybrid linguistic forms that reflect the complexities of digitally mediated life. These narrative transformations are closely tied to shifting constructions of identity, where the self is represented as fluid, performative, and often shaped through digital interactions and online discourse. The paper also explores how language functions as a dynamic site of meaning-making, influenced by internet vernaculars, code-switching, and evolving communicative practices. Furthermore, it interrogates issues of representation in a globalized digital context, where marginalized voices gain new visibility while simultaneously navigating the constraints imposed by algorithmic power structures. By situating contemporary fiction within the broader framework of digital culture, this study argues that the evolution of narrative form serves as a critical response to changing modes of communication and identity formation, thereby redefining the role of literature in the digital age.
Digital Modernity; Narrative Form; Contemporary English Fiction; Language and Identity; Representation; Multimodality; Hypertextuality; Digital Culture; Linguistic Hybridity; Algorithmic Mediation
Meenu Bhola, Akansh Garg, Prof. (Dr) Tushti Sharma, Dr. Fatima Ibrahim Altayeb, Trinkul Kalita, Dr. Khursheeda Khatoon
ARTIFICIAL INTELLIGENCE IN EDUCATION REDEFINING TEACHING–LEARNING FRAMEWORKS FOR IMPROVED ACADEMIC PERFORMANCE
The rapid growth of Artificial Intelligence (AI) has triggered a transformational shift in global educational systems by restructuring teaching–learning processes, personalizing pedagogy, optimizing assessment mechanisms, and strengthening academic performance metrics. Traditional learning environments depend largely on standardized instruction, teacher-centered strategies, periodic evaluation, and limited insights into learner diversity, resulting in disparities in achievement and engagement. AI-driven learning environments challenge these constraints by integrating adaptive learning systems, intelligent tutoring models, predictive performance analytics, natural language processing tools, and automated administrative workflows. This technological advancement enhances learning pathways through real-time feedback, personalized content delivery, dynamic difficulty adjustments, and continuous academic monitoring capable of predicting learning gaps before they manifest. Furthermore, data-driven educational intelligence enables institutions to analyze learner behavior, attendance patterns, cognitive progression, and affective states to design evidence-based interventions that improve retention, motivation, and competency mastery. Despite the broad benefits, ethical issues such as privacy, algorithmic bias, teacher–technology skill gaps, infrastructural disparities, and over-reliance on automation must be addressed to ensure equitable deployment. This research investigates how AI-enabled teaching–learning frameworks enhance academic performance while proposing a comprehensive methodology integrating machine learning analytics, adaptive pedagogical models, and institutional decision-support systems to establish sustainable, inclusive, and effective AI-driven education ecosystems.
Artificial Intelligence, Education Technology, Adaptive Learning, Predictive Analytics, Personalized Learning, Academic Performance, Intelligent Tutoring Systems, EdTech.
Dr.T.Vishnupriyan, Guli Ra'nokhon Khamidullaeva, Dr Regina Mathias, Ritesh Kumar Kushwaha, Abdelsalam Mohammed Daoud Yahya
REWRITING REALITY ARTIFICIAL INTELLIGENCE, AUTHORSHIP AND THE EVOLUTION OF NARRATIVE VOICE IN CONTEMPORARY ENGLISH LITERATURE
The rapid integration of Artificial Intelligence (AI) into creative writing has reshaped the fundamental dynamics of authorship, narrative construction, and literary expression in contemporary English literature. This paper examines how AI-driven text generators, computational creativity systems, and machine-assisted writing tools are transforming the concept of narrative voice shifting it from a purely human-cantered construct to a hybrid, collaborative model of co-authorship. While earlier literary traditions emphasized authorial intention and stylistic originality, modern AI systems introduce new forms of narrative synthesis, intertextuality, and adaptive storytelling that destabilize traditional boundaries between creator, tool, and text. Through a multidisciplinary framework combining narrative theory, digital humanities, and computational linguistics, this study investigates the emergence of machine-augmented narrative identities, the blending of human and algorithmic styles, and the ethical implications of automated authorship. Qualitative analyses of AI-influenced literary texts, alongside theoretical discourse on creativity and authorship, reveal that AI not only amplifies stylistic experimentation but also challenges existing norms surrounding authenticity, ownership, and literary value. The findings contribute to a deeper understanding of how narrative voice evolves when intelligenceboth human and artificialcoexists within the same creative space, thereby redefining the future trajectory of English literary production.
Artificial Intelligence, Narrative Voice, Authorship, Computational Creativity, English Literature, Digital Humanities, Literary Innovation, Text Generation, Posthumanism, Narrative Theory
Dr.G Anand Binod Singh, Dr. P. Suganya, Rakesh K Kadu, Abdelsalam Mohammed Daoud Yahya, Sulafa Gewi Mohammed, Prof. (Dr)Tushti Sharma
ARTIFICIAL INTELLIGENCE IN DIGITAL PAYMENT SYSTEMS ANDITS INFLUENCE ON CONSUMER TRUST:A STUDY IN CHENNAI
Artificial Intelligence (AI) has significantly transformed digital payment ecosystems by enhancing transaction efficiency, security, and personalized financial services. In the context of rapidly growing digital financial adoption in urban India, this study investigates the influence of AI-powered dimensions Fraud Detection, Personalization, Transaction Speed, and Service Quality on Consumer Trust in digital payment systems among users in Chennai. A quantitative research design was adopted, and primary data were collected from 100 digital payment users through a structured questionnaire based on a five-point Likert scale. Structural Equation Modelling (SEM) using Smart PLS 4.0 was employed to evaluate both the measurement model and structural model. Confirmatory Factor Analysis (CFA) confirmed satisfactory reliability and validity of the constructs, with Composite Reliability (CR) values exceeding 0.80 and Average Variance Extracted (AVE) values above 0.50. The structural model findings reveal that Fraud Detection (β = 0.421, p < 0.001), Transaction Speed (β = 0.312, p < 0.001), Personalization (β = 0.293, p < 0.01), and Service Quality (β = 0.267, p < 0.05) exert significant positive effects on Consumer Trust. The model explains 68.4% of the variance in Consumer Trust (R² = 0.684), indicating strong explanatory power. The findings emphasize the strategic role of AI-enabled security mechanisms and customized digital experiences in strengthening consumer confidence toward digital payment platforms. The study offers valuable implications for fintech firms, banking institutions, policymakers, and regulators seeking to improve trust, adoption, and sustainability within India’s digital payment ecosystem.
Artificial Intelligence, Digital Payment Systems, Consumer Trust, Fintech, Structural Equation Modelling (SEM), Fraud Detection, Personalization, Transaction Speed, Service Quality, India
THE CHALLENGES FACED BY MOTHERS OF CHILDREN WITH AUTISM SPECTRUM DISORDER AND THEIR RELATIONSHIP TO THE LEVEL OF AWARENESS OF AUTISM SPECTRUM DISORDER
This study aimed to identify the challenges faced by mothers of children with autism spectrum disorder (ASD), examine their level of awareness regarding the disorder, and explore the relationship between these challenges and the mothers’ awareness of ASD. The study employed a descriptive correlational design. To achieve its objectives, two scales were developed: the Mothers’ Challenges Scale and the Autism Spectrum Disorder Awareness Scale. The study sample consisted of (40) mothers of children with ASD in Tafila Governorate, Jordan. The findings revealed that the level of challenges faced by mothers of children with ASD was high, with the following dimensions ranked in order: psychological challenges, economic challenges, and social challenges. The results also indicated that mothers demonstrated a high level of awareness of ASD. Furthermore, a statistically significant negative correlation was found between the challenges faced by mothers and their level of awareness of autism spectrum disorder. The study concluded that the increase in challenges encountered by mothers is associated with a decrease in their awareness of ASD, highlighting the need for additional support to enhance their understanding and knowledge of the disorder.
Challenges faced by mothers of children with autism spectrum disorder, awareness of autism spectrum disorder.
Dr. Hadeel Mohammad Al-Hawamdeh, Ms. Ibtihal Mohammad Al-Khrisat, Prof. Ahmad M. Thawabieh, Prof. Lama Majed Al-Qaisi, Dr. Haya Zaid Al-Kharaan, Dr. Maram Khalaf Al-Bdour.