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                <text>Siddiqui A.A.I., G.Y. Pathrikar College of Computer Science and Information Technology, MGM University, Aurangabad, India; Deshmukh P.D., G.Y. Pathrikar College of Computer Science and Information Technology, MGM University, Aurangabad, India; Upreti K., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Delhi NCR, India; Kumari P., Gautam Buddha University, Greater Noida, Uttar Pradesh, India; Nijhawan P., CHRIST (Deemed to be University), Delhi NCR, India</text>
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                <text>Raokatta S., Singapore Institute of Technology, Singapore; Akshaya A.V.R., School of Business and Management, CHRIST University, Bengaluru, India; Rajeswari M.S., Department of Computer Science and Engineering, Jeppiaar Institute of Technology, Chennai, India; Sathiyaseelan V., Department of Autotronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil N?du, 602105, India</text>
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                <text>This chapter examines the transformative role of generative artificial intelligence (AI) in enterprise analytics, with emphasis on the Generative Pre-trained Transformer (GPT) family and related attention-based architectures. In contrast to conventional machine-learning pipelines whose performance is constrained by task-specific supervision and rigid feature engineering generative models exploit large-scale self-supervised pre-training, enabling emergent reasoning and effective transfer across heterogeneous downstream tasks. We demonstrate these advantages through a pragmatic integration of GPT-class large language models (LLMs) within an SAP HANA environment. By fine-tuning the LLM on domain-specific SQL corpora and curated schema metadata, the system learns to synthesise syntactically correct, execution-ready SQL statements that align with the underlying business logic. This design obviates costly data-centralisation efforts: users can pose natural-language questions and obtain HANA-compliant queries over distributed enterprise data without deep knowledge of relational algebra or SAP-specific functions. Moreover, explicit injection of domain ontologies during fine-tuning improves semantic grounding and materially increases query-generation accuracy. A sales-reporting case study substantiates these claims, showing that the approach streamlines complex analytic workflows, reduces time-to-insight, and enhances report reliability. Collectively, the findings position generative AI as a catalytic technology for modernising enterprise data management and accelerating data-driven innovation.  2026 Ram Kumar Chenthur Pandian, Shanmuga Raju Sekar, Subrata Chowdhury, Muhammad Rukunuddin Ghalib, and Kassian T.T. Amesho.</text>
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                <text>Artificial Intelligence in Detecting Autism;pp.124-142</text>
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                <text>Akshaya A.V.R., School of Business and Management, CHRIST University, Bengaluru, India; Vijayalakshmi B., Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India; Nisha P., Department of CSE, Dr. NGP Institute of Technology, Coimbatore, India; Ramachandran A., Department of Software Systems, Sri Krishna Arts and Science, Kuniyamuthur, Coimbatore, India; Sathiyaseelan V., Department of Autotronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India</text>
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                <text>Autism Spectrum Disorder (ASD) is a condition that involves many aspects and falls into the category of neurodevelopmental disorders. This is shown by problems in socializing, talking and repetitive actions. Despite the fact that early intervention is beneficial such initiatives may be postponed due to the lengthy process of assessing the disease by qualified doctors. More people are interested in AI and ML technologies which may help detect ASD earlier and more accurately. The goal of this paper is to describe the machine learning techniques used to spot ASD in individuals of any age, using information from behaviour, genes and brain images. It applies supervised learning, unsupervised learning and deep learning, using Support Vector Machines, Random Forests and Convolutional Neural Networks to find autistic patterns in complex data. We also discuss the use of facial recognition, speech recognition, motion analysis and wearable devices in helping with early detection and creating personal intervention programs. At the same time, these technologies are concerned with data accuracy, biased algorithms, lack of openness and ethical and social issues such as data safety and consent. It explains the benefits and the issues that come with using AI and ML in healthcare to find cases of autism spectrum disorder (ASD). By working together, policymakers, researchers and clinicians can help these technologies advance the diagnosis and treatment of ASD which will improve the lives of those with ASD and their families.  2026 Ram Kumar Chenthur Pandian, Shanmuga Raju Sekar, Subrata Chowdhury, Muhammad Rukunuddin Ghalib, and Kassian T.T. Amesho.</text>
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                <text>Intelligent Systems: Emerging Trends and Challenges in Deep Neuro-Fuzzy Models and Explainable Artificial Intelligence;pp.85-100</text>
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                <text>Shetty L., Symbiosis Centre for Management Studies, Symbiosis International, Deemed University, Bengaluru, India; Srivastava S., Christ Deemed to be University, Bengaluru, India</text>
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                <text>The fast-paced growth of q-commerce platforms has radically changed the shopping arena to deliver consumers unparalleled convenience. However, this speedy delivery poses significant challenges to achieving net-zero emissions, essentially due to inefficiencies in logistics and high energy usage. This research applies the Fuzzy DEMATEL approach to explain and analyze the barriers to sustainability in q-commerce by uncovering interconnections between factors. The findings showed that the primary logistical inefficiency is preceded by high energy usage and sustainable packaging as significant drivers. Other evaluated factors, though with lower scores, are regulatory challenges and consumer awareness. The mitigation of logistical inefficiencies can serve to greatly improve routing and resource management in such a way as to bring significant decreases in carbon footprint. Also, by augmenting consumer awareness for more sustainable practices, one creates an increasing demand for alternative choices, hence giving way to positive feedback that may help drive companies toward adopting even more sustainable approaches. From a policy perspective, the results indicate that regulatory frameworks should support investments in green infrastructure and technologies by engaging the different stakeholders, including businesses, consumers, and governmental entities, in a common strategy toward sustainability. While the present research supplies important insights into the challenges with which q-commerce is confronted while achieving net-zero emissions, it recognizes some constraints, such as potential biases due to expert judgments and the dynamic character of the business. The following studies would include more stakeholders and variables influencing sustainability and broaden the scope. Through addressing these barriers as a collective, the q-commerce industry can move toward achieving its net-zero dreams while advancing broader environmental goals for a greener world.  2026 selection and editorial matter, Siddhartha Roy, Soumya Sen, and Agostino Cortesi; individual chapters, the contributors.</text>
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                <text>Josephine V.L.H., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Ramesh K., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Thaliyan D., School of Business and Management, Christ University, Bangalore, India</text>
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                <text>Its challenging to envision a world without the internet. From acquiring knowledge to ordering food, our lives have become incredibly convenient thanks to it. As technology advances, internet access is becoming easier and more affordable, with India being renowned for having the lowest internet costs. Internet service providers (ISPs) aim to offer better speeds, fewer disruptions, and professional service. They charge fees for allowing customers to shop online, browse the web, stay connected with loved ones, and conduct business. Due to a lack of data comprehension, the company struggles to leverage reports in daily operations. Consequently, BSNL is finding it hard to outshine competitors and become profitable. Key highlights from the project include understanding customer mentality and addressing issues faced by franchise owners. This research aimed to enhance organizational operations by reducing manual interventions and automating customer communication. User sentiments toward the brand and its competitors were analyzed, and exploratory data analysis was conducted to assess the organizations position. Data visualization with Tableau and Python programming were utilized to derive insights from the data.  2025 selection and editorial matter, Shruti Sharma, Ashutosh Sharma, and Trinh Van Chien.</text>
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                <text>The chapter explores the transformative impact of generative AI and large language models (LLMs) in healthcare, emphasizing their potential to revolutionize patient care, clinical operations, and medical research. Generative AI, a subset of artificial intelligence, offers groundbreaking capabilities such as personalized medicine, virtual health assistants, and enhanced diagnostic accuracy. LLMs like Med-PaLM and BioBERT are fine-tuned to perform specific healthcare tasks, such as clinical note summarization and diagnostic support. These models also assist in drug discovery, clinical trials, and pandemic preparedness by analyzing complex medical data and predicting patient outcomes with high accuracy. The chapter also addresses the ethical and regulatory considerations associated with AI in healthcare, including data privacy, bias, and accountability. While the integration of AI technologies promises significant advancements, it also requires stringent regulatory oversight to ensure safety, efficacy, and fairness. The potential of generative AI to generate synthetic medical data offers a secure way to advance research without compromising patient privacy. Additionally, AI can optimize healthcare processes, enhance patient engagement, and accelerate medical research, contributing to a more efficient and personalized healthcare system. The chapter concludes by highlighting the need for continuous collaboration between AI developers, healthcare professionals, and regulators to maximize the benefits of these technologies while addressing the associated risks.  2025 selection and editorial matter, Sakshi Gupta, Umesh Gupta, Moolchand Sharma, Kamal Malik; individual chapters, the contributors.</text>
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                <text>Jose J., Department of Commerce, Christ University, Karnataka, Bengaluru, India; Ghosh N., Christ University, Karnataka, Bengaluru, India; Simran Hemanshu Naik K., Department of Commerce, Christ University, Karnataka, Bengaluru, India; Nanjappa K., Department of Commerce, Christ University, Karnataka, Bengaluru, India; Premal Desai M., Department of Commerce, Christ University, Karnataka, Bengaluru, India</text>
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                <text>Stakeholders participation is vital to the success of circular business models, and the investor is perceived as the principal stakeholder. So, it is essential to understand the factors shaping investment behavior in the circular economy. In this context, this study is an initial attempt to explore the factors playing a role in shaping investor behavior. The study explores the role of perception on sustainable investments, awareness of ESG practices, and environmental considerations in shaping investment behavior. The study finds that perception of sustainable investment and awareness of ESG practices have significant effects on sustainable investment behavior. It is seen that digital technologies, including financial technologies, have a huge role in bringing up circular economy efforts. In this context, by building fuzzy logic, artificial intelligence can be an effective tool in determining investor behavior in the hands of corporates. At the same time, from the investors side, building digital financial literacy is required to deal with sustainable investments by upholding independence in decision-making.  2026 selection and editorial matter, Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar, and Youqing Fan; individual chapters, the contributors.</text>
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                <text>Anand Shankar R.M., ISBR Business School, Bangalore, India; Mynavathi L., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bangalore, India; Kavatekar S., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bangalore, India; Kiran Kumar M., Faculty of Management  Finance, CMS Business School JAIN (Deemed-to-be) UNIVERSITY, India; Annam A.K., SSMRV College, Bangalore, India</text>
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                <text>Government interventions play a crucial role in nurturing technopreneurship and advancing sustainability. The proposed chapter explains the significance of governmental support in bridging the financial, infrastructural, and knowledge gaps that technopreneurs face. It categorizes the types of support into financial aid, regulatory frameworks, infrastructure development, and educational programs, providing a structured overview of each. A detailed analysis of policy frameworks that foster innovation and sustainability is presented, supported with global examples such as the United States Small Business Innovation Research (SBIR) program, Israels Innovation Authority, Indias Digital India Initiative and Germanys High-Tech Strategy 2025. These examples illustrate how strategic policies can catalyse technological advancements and economic growth. The chapter further includes case studies from diverse regions, showcasing successful policy implementations and their tangible impacts. These case studies offer practical insights and best practices, demonstrating how tailored policies can create robust technopreneurial ecosystems. Finally, the chapter addresses the challenges in policy implementation and offers recommendations for future directions, emphasizing the need for adaptive, inclusive, and collaborative policy approaches. This comprehensive exploration aims to provide policymakers, academicians, and technopreneurs with valuable knowledge on leveraging government support for sustainable technopreneurial success.  2025 selection and editorial matter, Rajender Kumar, Rahul Sindhwani, Raman Kumar, Punj Lata Singh, and J. Paulo Davim.</text>
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                <text>Pabba D.P., Departamento de Electricidad, Facultad de Ingenier, Universidad Tecnolica Metropolitana, Santiago, Chile; Bangolla H.K., Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan; Vaddadi V.S.C.S., Departamento de Mecica, Facultad de Ciencias Ficas y Matemicas, Universidad de Chile, Santiago, Chile; Yadav S.K., Department of Physics and Electronics, Christ (Deemed to be University), Bangalore, India; Soosairaj A., Department of Physics, St. Joseph's College (Autonomous), Affliated to Bharathidasan University, Tiruchirappalli, India; Thirumurugan A., Sede Vallenar, Universidad de Atacama, Vallenar, Chile; Aepuru R., Departamento de Mecica, Facultad de Ciencias Ficas y Matemicas, Universidad de Chile, Santiago, Chile</text>
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                <text>Energy harvesting modules are becoming increasingly vital for developing autonomous, self-powered microelectronic devices. MXenes, a class of two-dimensional (2D) transition metal carbides/nitrides, have recently gained attention as promising candidates for energy applications due to their excellent electrical conductivity, large specific surface area, and tunable properties. MXene-based nanogenerators (NGs) represent a cutting-edge advancement in energy harvesting technology, harnessing the unique properties of MXenes to enhance performance. Incorporating MXenes into composite materials facilitates efficient ion/electron transport and increases the surface charge density, leading to higher output performance. Additionally, MXenes abundant functional groups and tunable surface properties enable strong interactions with polymer matrices, resulting in composites with superior mechanical strength and flexibility. This book chapter delves into the various properties of MXenes, highlighting their importance in energy harvesting technology and their inherent piezoelectric properties. It also covers using MXene-based composite materials in NG technology, focusing on MXenepolymer, MXenemetalorganic framework (MOF), and MXenecarbon composites. Additionally, the chapter discusses the applications of these composite-based NGs in various fields, including wearable technology and biomedical devices. Finally, the chapter summarizes the recent advancements in this field and future aspects in enhancing the use of MXene in energy harvesting technology.  2026 Taylor &amp;amp; Francis Group, LLC.</text>
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                <text>Natarajan J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vimal P.H., Department of Computer Science and Engineering, Christ University, Bangalore, India; Sen D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Lawrence C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University, Sarawak, Malaysia; Chintale P., SEI Investment Company, Downingtown, PA, United States; Patil S.A., Amazon AWS, Seattle, WA, United States</text>
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                <text>Sarkar, Monsoon; Singhal, Abhinav; Glory, C.</text>
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                <text>Reimagining Healthcare: The South African PPP Revolution</text>
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                <text>Sustainable Healthcare Systems in Africa: Technologies, Practices, and Management;pp.64-78</text>
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                <text>Sarkar M., Christ University, India; Singhal A., Christ University, India; Glory C., Christ University, India</text>
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                <text>Publicprivate partnerships (PPPs) are rapidly being recognized as transformative methods for improving healthcare delivery and funding, particularly in resource-constrained environments. Global and national policy frameworks, supported by organizations such as the African Development Bank (AfDB) and the World Health Organization (WHO), emphasize the potential for PPPs to fill significant gaps in healthcare infrastructure and service delivery. Countries across Africa, notably South Africa, Kenya, Nigeria, and Rwanda, have put in place national PPP frameworks that formalize partnerships in healthcare, focusing on risk sharing, accountability, and sustainability. South Africas National Treasury PPP Unit is a regional pioneer in promoting PPP development that balances private-sector innovation with governmental control. Such frameworks allow PPPs to mobilize private resources, enhance public spending efficiency, and provide access to high-quality healthcare, particularly in marginalized communities. Despite positive developments, PPPs in African healthcare confront hurdles due to fragmented legal frameworks and low institutional capacity to manage complicated contracts. The AfDBs 20212031 PPP strategic framework seeks to fill these gaps by providing African States with resources to establish enabling environments and prepare viable healthcare projects for the market. Diverse models in South Africa and other countries, such as Kenyas Managed Equipment Services (MES) and Ghanas BuildOperateTransfer (BOT) programs, show how adaptable PPPs can improve healthcare finance and delivery. However, current regulatory frameworks are complicated and often disconnected, emphasizing the need for unifying legal standards to assure transparency and accountability. This chapter highlights the insights that present a strong PPP model adapted to healthcare financing. It highlights the necessity of transparent systems, good risk management, and combining publicprivate expertise to handle current healthcare concerns. PPPs can improve healthcare accessibility and quality, increase patient satisfaction, and strengthen healthcare systems by promoting improved governance, policy consistency, and capacity building. Strategically honed, PPPs can drive long-term breakthroughs, positioning healthcare systems better to address the changing demands of African communities and beyond.  2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors.</text>
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