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.
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.
LIVING ENOUGH IN AN AGE OF MORE: COGNITIVE ACCELERATION AND SUSTAINABLE FUTURES IN LISA GENOVA’S MORE OR LESS MADDY
This article examines Lisa Genova’s novel, More or Less Maddy, as an accelerative cognitive narrative, questioning the sustainability of modern life of speed, excess, and productivity based on performance. The analysis is contextualized in terms of the critical discourse of sustainability in the digital age, referring to the theory of social acceleration by Hartmut Rosa (2013), the concept of achievement society by Byungul Han(2015), and the ethical concepts related to the slow culture and care ethics theories. Although the story does not touch directly on the aspect of artificial intelligence, it throws light on the allegory of bipolar disorder, specifically manic acceleration that is defined by cognitive overload, hyper-productivity, and affective excess, which reflects the accelerated tempos of late-capitalist and digitally mediated cultures. The purpose of the study is to analyze the way neuro-narrative by Genova reveals the boundaries of human thought in the circumstances of unstopping acceleration and to evaluate its ethical consequences in regard to a sustainable future. Through textual analysis, it validates More or Less Maddy as a counter-acceleration narrative that challenges the mainstream cultural imaginaries of infinite optimization, as well as the ones related to new AI-based productivity discourses. Furthermore, the article suggests that the definition of sustainability should also extend beyond the scope of environment and technology to encompass mental wellbeing, emotional harmony, moral discipline, and psychological resilience. In essence, the author asserts that “Genova’s fiction offers a philosophy of ‘living enough’ rather than always aiming for more” and makes contributions to the field of medical humanities and sustainability scholarship.
Cognitive Acceleration, Sustainable future, Neuro-Narratives, Slow Culture, Mental Health and Productivity.
Ms.M.C.Shree Akshatha, Dr. Kanchana C.M.
CHALLENGES IN LEARNING ENGLISH AS A SECOND LANGUAGE THROUGH ARTIFICIAL VOICES – A SURVEY
English as a Second Language (ESL) learners have gained greater access due to the extensive number of artificial voices in language acquisition resources. However, sorting through these artificial sounds presents significant challenges, as it helps the learners to understand English phonetic patterns, phonological structures, stress, rhythm, and pronunciation; the role of their mother tongue strongly influences the difficulties in understanding artificial sounds. This study explores the obstacles that ESL students struggle with in understanding the English language through artificial voices, emphasising aspects such as naturalness, intonation, and correct pronunciation. Based on the statistical studies, learners have a hard time understanding artificial voices because of things like uneven pitch and intonation, accent, inadequate intelligibility, strange accents, speech pace, and noisy background interruptions. It also concentrates on pronunciation, accent variability and the impact of understanding. This study presents insights into the difficulties of ESL listening through artificial voices through a comprehensive evaluation and provides solutions for ways to enhance language learning tools. This study of research also emphasises the necessity for voice processing, which is more linguistically refined and more flexible. By presenting experimental research into optimising artificial voice tools in an attempt to improve linguistic acquisition results, it brings value to contemporary discourse in applied linguistics and educational technologies.
Artificial voices, Language Learning, Accuracy, Intonation, Accent.
Ms M.B.Narmadha, Ms B. Dharanipriya, Ms M. Anushiya, Ms S. Rithiya, Mrs.R.Divya
B2B MARKETING STRATEGIES IN THE ETHANOL INDUSTRY: A STUDY OF SUPPLIER–BUYER RELATIONSHIPS
The ethanol market is in turn a complicated industrial structure marked by extended concert contracts, regulatory conformity, sustainability requirements, and technological assimilation. This paper looks at the B2B environment in ethanol business with particular emphasis on relationship development between suppliers and buyers, governance, and Information digitalization, as well as positioning in sustainability. Inspired by modern studies about transition to the circular economy [1], the adoption of supply chain technology [2], transaction cost governance in biomass markets [5], and resilient chemical supply chains [11], the paper creates an analytical framework to be applied in the industry markets. The mixed-method research design was selected, which implies integrating structured surveys and semi-structured interviews and analyzing a secondary industry to assess trust, coordination efficiency, transparency mechanisms, and long-term contract execution. The analysis of the existing findings suggests that joint planning, digital transparency via IoT-supported monitoring [14], and sustainability-focused differentiation have a positive impact on relation continuity and lessening the uncertainty of the negotiation process. Moreover, digital supply network transformation enhances enterprise responsiveness and performance [25]. The study comes out with the view that suppliers of ethanol who combine the technological capability, matching the sustainability and structured relationship governance, have better retention benefits and enhanced stability of supplies. The paper has made a contribution to the field of civil and industrial supply chain by giving a full coverage of the B2B marketing model in the ethanol industry.
B2B marketing, ethanol industry, supplier–buyer relationships, sustainability strategy, digital supply chains
Pratap Desai, Dr. Sudhir A. Atwadkar, Dr. Pushpraj Wagh
THE INFLUENCE OF SOCIAL NETWORKING SITES AS A SOCIAL PHENOMENON ON THE SELF-EFFICACY OF SECONDARY SCHOOL STUDENTS IN ARAB SCHOOLS WITHIN THE GREEN LINE: SCHOOL PRINCIPALS' PERSPECTIVES
This study examined the influence of social networking sites (SNS), conceptualized as a pervasive social phenomenon, on the self-efficacy of secondary school students enrolled in Arab schools within the Green Line, as perceived by school principals. Employing a descriptive-survey research design, data were collected via a structured questionnaire administered to a convenience sample of 62 male and female school principals, drawn from a target population of 95 administrators serving Arab secondary schools in the Northern District within the Green Line. Findings indicated that mean scores across all self-efficacy domains were uniformly rated at a low level. Domain rankings, in descending order, were as follows: the behavioral domain (M = 2.22), the social domain (M = 2.20), the cognitive domain (M = 2.14), and the self-confidence domain (M = 2.12). Furthermore, no statistically significant differences were detected in principals' perceptions as a function of years of administrative experience; however, statistically significant differences were identified with respect to gender and academic qualification, favoring male principals and bachelor's degree holders, respectively.
social networking sites; self-efficacy; school principals; secondary education; Arab schools.
AN EMPIRICAL STUDY ON LEAD GENERATION AND CONVERSION STRATEGIES AND THEIR IMPACT ON PRODUCTIVITY IN THE EDTECH INDUSTRY: A STUDY WITH REFERENCE TO JARO EDUCATION
The EdTech industry has experienced significant growth due to digital transformation and increasing demand for online learning. In this competitive environment, effective lead generation and conversion strategies are essential for enhancing organizational productivity. This study examines the impact of lead generation and conversion strategies on productivity in the EdTech industry, with specific reference to Jaro Education. The study adopts a descriptive research design using both primary and secondary data. Primary data was collected from 100 respondents through a structured questionnaire, and statistical tools such as percentage analysis, correlation, and regression were applied. The findings reveal that while respondents perceive these strategies as highly important, the statistical analysis indicates no significant individual impact on productivity. This suggests that productivity is influenced by multiple organizational and market-related factors. The study provides insights for improving marketing and sales integration and highlights the need for a holistic approach to productivity enhancement in the EdTech sector.
Lead Generation, Conversion Strategies, EdTech, Productivity, Customer Relationship Management (CRM), Digital Marketing
DATA-DRIVEN ANALYSIS OF LAST-MILE DELIVERY EFFICIENCY AND ITS IMPACT ON SERVICE QUALITY AND ORGANIZATIONAL PRODUCTIVITY: EVIDENCE FROM FOOD DELIVERY PLATFORMS IN CHENNAI
The emergence of online food delivery apps like Swiggy, Zomato and Dunzo has revolutionized the food service industry in India's urban cities. In Chennai - a densely populated urban agglomeration with a population of over ten million and high tech-savviness amongst consumers - last-mile delivery is the most critical and high cost aspect of the food delivery supply chain. This research conducts a quantitative analysis of the efficiency of last-mile delivery and the resulting effect on service quality and organisational productivity for the food delivery platforms in Chennai. A structured questionnaire was distributed to 103 delivery partners of food delivery platforms such as Swiggy, Zomato, Zepto and Eatsure. The study used five statistical techniques: Percentage Analysis, Descriptive Statistics, Pearson Correlation, Regression Analysis and One-Way ANOVA. The study finds that workload, technology reliability and service design account for 67 percent of the variance in delivery performance and job satisfaction (R² = 0.670, R = 0.818). High workload is the strongest predictor of complaints (r = .461**), and reliability of GPS is the second most positive predictor of delivery performance. The research offers insights for food delivery platforms, logistics managers, and policymakers.
Last-Mile Delivery, Food Delivery Platforms, Service Quality, Organizational Productivity, Chennai, Route Optimization, Delivery Efficiency, Data-Driven Logistics, Gig Economy.
SUSTAINABILITY THROUGH VEDIC PRINCIPLES: INTEGRATING DIGITAL LEARNING IN GURUKUL EDUCATION FOR AN ECO-CONSCIOUS GREENER ENVIRONMENT
Purpose: A study related to integrating Vedic knowledge into the curriculum through a digital medium will promote environmental awareness by blending ancient wisdom with modern technology to enable students to adopt sustainable practices. Methodology: A comprehensive literature analysis was done to find effective modes of education that include the use of Vedic texts through digital media. Case studies, as is the current one, are used to analyze practical approaches. The expert opinion was also sought to know better ways of implementing Vedic through the medium. Results: It was observed that the incorporation of the Vedic approach through the digital medium results in a marked increase in environmental awareness. Effective techniques for engagement, including virtual experience, game-based learning, and Gurukuls online, were observed as successful. For instance, Vedic Heritage Portal is an online platform providing Vedic knowledge. Conclusion: Integration of Vedic philosophy in education using digital mediums proves to be a highly effective method for increasing environmental awareness. This blend of the old and new would ensure that learners become aware of their environment-related responsibilities.
Vedic philosophy, environmental awareness, digital education, sustainable practices, traditional knowledge, modern technology integration.
Aparna Vajpayee, K K Ramachandran, K K Karthik, Kaushik Mishra, Ashish Saxena
A NOVEL ARCHITECTURE FOR AUTONOMOUS IT SERVICE MANAGEMENT: INTEGRATING GENERATIVE AI WITH WORKFLOW AUTOMATION PLATFORMS
The current IT service management (ITSM) frameworks are under pressure to deliver quicker responses to incidents, lower the costs of operation, and get accustomed to more complicated hybrid infrastructure settings. The paper presents an innovative architecture that would allow the seamless integration of Generative AI (GenAI) functions and enterprise workflow automation platforms to make the IT services management fully autonomous. The presented system uses the large language models (LLM) to classify tickets based on their smartness, analyze their root causes, and remediate them using natural language, and organize automated processes via ServiceNow, Ansible, and Apache Airflow. The architecture can be used to do context-aware decision-making without human oversight by adding retrieval-augmented generation (RAG) and real-time telemetry ingestion to handle a large category of recurring incidents. Simulated enterprise settings have shown experimental evaluation results of mean time to resolution (MTTR) reduced by 67 percent and escalation rates reduced by 54 percent, and massive cost savings over traditional rule-based automation strategies. The architecture also uses explainability modules and human-in-the-loop override mechanisms to provide governance, compliance and auditability. The work provides a vendor-neutral blueprint of next-generation autonomous ITSM, including its theoretical background and a practical implementation roadmap which enterprises can adopt. The architecture is further extensible to Change Management, Problem Management, and Service Catalog Management, providing a comprehensive GenAI-driven framework across the full ServiceNow ITSM module suite.
Generative AI, IT Service Management, Workflow Automation, Large Language Models, Autonomous Remediation, Change Management, Problem Management, Service Catalog Management, ServiceNow.
REIMAGINING DECISION MAKING AND WORKFORCE GROWTH: IBM’S CONTRIBUTIONS TO AI SKILLING AND COGNITIVE DEVELOPMENT
AI is disrupting industries in ways that create opportunities and challenges for skilling the workforce. As industries increasingly adopt AI, the skills for an AI-future workforce require an increasingly educated and trained workforce for end-to-end operations. IBM has been a leader in AI education through various programs aimed at bridging the AI skills gap with an emphasis on cognition, learner adaptability and inclusivity. With a goal to train two million people by 2026, IBM seeks to democratize AI skills acquisition to ensure underrepresented populations can also thrive in this emerging digital economy. This paper explores IBM's AI skilling and upskilling efforts through a mixed-methods analysis that combines the qualitative elements of learning platforms within IBM, collaborations in the industry, and partnerships in academia, with quantitative data regarding enrolments and program efficacy. This research assesses, through paper review, enrolment numbers, and participant feedback, how IBM's AI labs enhance cognitive abilities (problem-solving, critical thinking, decision-making), as well as how IBM's impact on AI curriculum in higher education institutions enhances workforce readiness. Results indicate that IBM's AI-training initiatives are part of a larger, organizational, developmental approach to learner strengths in industry-appropriate competencies and encourage cognitive transferability. Digital credentialing, mentorship and application to real-world scenarios increase motivation and retention. In addition, AI and diversity are addressed through allowing underrepresented groups to engage with such initiatives to ensure equity in the technological future. Ultimately, IBM's connection to skills development and cognitive growth through AI-acquisition and implementation, therefore, closes the skills gap more than enough. Initiatives such as these will pave the way for other organizations to foster a workforce ready for the future.
Artificial Intelligence (AI), AI Skilling, AI Upskilling, IBM SkillsBuild, AI Workforce Development, Digital Learning, AI Talent Pipeline, Diversity in AI, AI Education
Aparna Vajpayee, K K Karthik, K K Ramachandran, Kaushik Mishra, Neerav Varma, Parag Sanghani
CYBERSECURITY, DATA PRIVACY, AND HUMAN PSYCHOLOGY IN THE DIGITAL ECONOMY: PUBLIC PERCEPTIONS AND PATHWAYS TO SUSTAINABLE DEVELOPMENT
This research explores the attitudes of the general population towards the concept of the digital economy to evaluate the impact that this phenomenon has on economic processes, consumer ehaviour, and legislation. In the current research, a mixed-methods approach was applied, meaning that qualitative data were collected via interviews and focus groups, while quantitative data were obtained via a Likert-scale survey. There were 100 participants in the study, allowing the attitudes and behaviour concerning the digital economy to be concluded. As it appears from the results, people have diverse perceptions of the digital economy phenomenon, and they may be negative or positive. While a considerable number of people know about the advantages of digital technologies regarding greater convenience and efficiency, others expressed some worries about data security, cybersecurity, and digital literacy. The main limitation of this study is that there is only a limited number of participants in the study, which limits the application of the results. Moreover, the use of a Likert scale to gather quantitative data could not have allowed for an analysis of all possible nuances. Although the present study has certain limitations, it is of great importance since it reveals the public perception regarding the digital economy, which could serve as a basis for further research. By developing a deeper knowledge about the public perception, policymakers and companies will be able to develop policies related to the sustainable development of the digital economy.
Digital economy, perceptions, attitudes, survey, qualitative analysis, quantitative analysis, convenience, privacy, cybersecurity, inclusivity.
ARTIFICIAL INTELLIGENCE IN START-UP GROWTH: MECHANICAL INNOVATIONS, OPPORTUNITIES, AND PSYCHOLOGICAL CHALLENGES FOR ENTREPRENEURS
The purpose of this research is to investigate the effects of AI on organisational processes, particularly focusing on customer satisfaction, innovations, decision-making, and efficiency. Furthermore, this research also aimed at identifying the major obstacles faced by organisations when implementing AI in their business operations, including financial considerations, ethical issues, and resistance to change. By exploring the advantages and disadvantages of AI technology, the current research will present possible approaches to introducing the organisational use of AI-based solutions.
ENHANCING SECURITY AND SCALABILITY IN BLOCKCHAIN NETWORKS THROUGH ARTIFICIAL INTELLIGENCE
Blockchain now stands as one of the most revolutionary leading technologies across industries, promising the result that is easier, more transparent, and secure. However, as the network size of blockchains continues to grow, so does the need to address issues of security and scalability of the networks. This research focuses on the possibility to enhance the security and the capacity of the blockchain models using AI methods. In fact, regarding the security threats which are linked to blockchain such as fraud, data leak, and cyber assaults, AI offers new execution styles through machine intelligence learning, deep learning, and anomaly acknowledgment. When it comes to combatting new cyber threats, blockchain environments can add operational value and complexity by staging real-time AI models for threat detection and automated updates to protocols. Secondly, the issue of scalability is significant given that the greater demand for the numbers of the transactions on the Web impedes performance and sometimes leads to a network overload. It also reviews solutions supported by AI technology for instance predictive analysis and optimization programs that can enhance the ability of block chain networks with high through put and the ability to do away with delay hence fast processing of transaction. Further, the interaction between AI and blockchain is explored including how AI can enhance consensus algorithms, control resources, and enable bright contract execution towards scaling the network. The research comprises the case studies of AI implementation, evaluates the present state of developments in AI integration, and considers future advancements in AI integration to fulfill the new needs of blockchain. As shown below, the necessary integration of AI into the blockchain platforms can indeed greatly improve security measures and increase scalability of the current global blockchain platforms which will therefore lead to broader recommendation and sustained usage.
Artificial Intelligence, Blockchain Security, Scalability, Smart Contracts
ROBUST MLOPS FRAMEWORKS FOR AUTOMATING THE AI/ML LIFECYCLE IN CLOUD ENVIRONMENTS
It has become clear that MLOps is an important way to manage the whole lifecycle of AI/ML models, from creation to deployment. This article talks about an all-inclusive MLOps system made to make training, validating, deploying, and monitoring models in the cloud automatic. To make the AI/ML process easier, the framework has scalable cloud-native tools, continuous integration/continuous deployment (CI/CD) pipelines, and automated hyperparameter optimization. Case studies show that models are more reliable, they can be deployed faster, and they have less operating overhead. The importance of these developments for the widespread use of AI in businesses cannot be overstated.
MLOps, Automated AI Pipelines, Hyperparameter Optimization, Cloud AI Lifecycle, CI/CD for AI.
CLOSED-LOOP ENGINEERING INTEGRATING REVERSE ENGINEERING AND DIMENSIONAL VARIATION ANALYSIS (DVA) TO MITIGATE LIFECYCLE RISK IN HEAVY EQUIPMENT
This paper explores a closed-loop engineering methodology where reverse engineering (RE) and dimensional variation analysis (DVA) is coupled to reduce lifecycle risk for heavy equipment manufacturing and maintenance. The research is based on 120 key components of the working system of the 15 heavy machinery-run heavy loaded performance. The reverse engineering technique was employed to re-model the objects as digital computer models, achieved an average geometry fidelity of 18.6% over the legacy CAD data. Statistical tolerance models were developed for dimension variability analysis in 3 manufacturing and 4 refurbishment cycles. The reduction rate of the screw sleeve's dimensional deviation is 27.4%, and that of the assembling fit degree consistency is 21.8%. Through the proposed closed-loop feedback, a real-time updating of design was feasible which resulted in an overall decrease by 32.1% rework and 24.7% unplanned downtime. Life cycle risk scores, based on failure probability and cost-impact indices, reflect a reduction of the overall risk with 29.3% along the life time. Furthermore, the range of maintenance cost was decreased by 19.5%, and the use efficiency of parts was improved from 62% to 81%. These quantitative results provide reliable evidence to support dimensionally correct and cost-effective closed-loop engineering systems combined with RE and DVA for industrial product lifecycles, informing data-driven decisions in an effort to improve sustainability and resilience of the heavy machinery.
Closed-loop engineering, Reverse engineering, Dimensional variation analysis, Heavy equipment lifecycle, Risk mitigation.
Vijayakumar Reddy Yeddula, Durga Bhaskara Raghava Rama Kishor Adurthy, Vegendra Thammisetty
MULTISTAGE ADAPTIVE 3D-CNN MODEL FOR DETECTING THE DROWSINESS AMONG THE DRIVERS
Increased driver drowsiness-related accidents underscore the need for better detection technologies to address this global issue. Researchers have tried numerous machine learning and deep learning methods, including CNN variations, but computational complexity, low evaluation accuracy, and doubtful reliability make real-time deployment difficult. A multistage adaptive 3D-CNN model for Driver Drowsiness Detection that addresses system complexity and performance. Our models classify driver fatigue symptoms. Face attributes for sub-models are collected using different 3D CNN architectures. The head condition model finds head dozing, eyebrow elating, and glancing towards the side. The eye alignmentprototype finds eye closings, soreness, and eyes that are only partially closed. Mouth condition model identifies open and covered yawning. The specs and usual circumstances model classify states as day, sunglasses, night, and normal. All models are essential to the system, detecting distinct driver sleepiness factors with specialized algorithms. Extensively examined using KEC-DDD and NTHU-DDD datasets, representing their toughness and adaptability. Dataset validation findings confirm the sub-models' capacity to accurately capture driver behaviours in various contexts.
Drowsiness; Deep Learning; Convolutional Neural Network; Accuracy; Detection.
TRIPLE STAGE CNN MODEL TO DETECT THE DROWSINESS AMONG THE DRIVER’S BASED ON FACE POSITIONING TECHNIQUE
Generally, we need strong detection tools to combat driver drowsiness, which is an increasing problem around the world. Deep Learning (DL) and Machine Learning (ML) methods, such as different types of Convolutional Neural Networks (CNNs), have been tried by researchers, but they haven't been able to be used in real-time situations because they are too hard to implement because they are so complicated to compute and don't work very well when tested. Regarding these restrictions, a multistage adaptive multi-level -CNN model has been developed specifically for Driver Drowsiness Detection (DDD), emphasizing the resolution of system complexity and performance challenges. The suggested system employs a multiple tier methodology for driver sleepiness detection, incorporating breakthroughs from multiple separate research stages. The findings illustrate the efficacy of the suggested model across various datasets. The proposed model outperforms previous models, such as YOLOv7-Face, MTCNN, AAMS, and Hyperface-Resnet, in terms of bounding box regression and facial point localization accuracy. The results demonstrate the proposed model's efficacy in facial positioning and the detection of initial driver drowsiness, rendering it helpful for improving road safety.
Convolutional Neural Networks; Accuracy; Detection; Deep Learning; Drowsiness.