459
20
16481
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Faculty Publications
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Gonzalez, Ernesto D. R. S.; Sijariya, Rajeev; Singh, Amit Kumar; Garg, Vikas
Title
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INTEGRATING CLIMATE CHANGE, SOCIAL RESPONSIBILITY AND ELECTRONIC FINANCIAL INCLUSION: A PATHWAY TO SUSTAINABLE DEVELOPMENT
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01-01-2025
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Developments in Corporate Governance and Responsibility;Volume;25;pp.147-164
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<a href="https://doi.org/10.1108/S2043-052320250000025008" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/S2043-052320250000025008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004587376?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004587376?origin=resultslist</a>
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Gonzalez E.D.R.S., University of Talca and Paulista University, Brazil; Sijariya R., Atal Bihari Vajpayee School of Management and Entrepreneurship, Jawaharlal Nehru University, Delhi, India; Singh A.K., Department of Commerce, University of Delhi, India; Garg V., Christ University, Bengaluru, India
Description
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Purpose: This study explores the intersection of climate change social responsibility and electronic financial inclusion (EFI) as critical components of sustainable development. The research aims to identify the synergies between these domains and their potential to drive inclusive growth and resilience. Design/Methodology/Approach: The study integrates literature review and case studies to analyse the role of EFI in enhancing access to financial services, particularly for marginalised communities. It also examines corporate social responsibility (CSR) initiatives aimed at mitigating climate change and promoting environmental sustainability. The research study highlights successful integration models and best practices that demonstrate the impact of multistakeholder collaboration. Findings: The findings reveal that EFI significantly contributes to poverty reduction and economic empowerment by expanding financial access in underserved regions. Moreover, corporate initiatives in climate change mitigation, when aligned with social responsibility, enhance business resilience and foster sustainable practices. The study emphasises the importance of supportive policy frameworks and technological innovations in scaling these efforts. Research Limitations/Implications: The studys focus on case studies may limit the generalisability of the findings. Future research could explore broader geographic regions and diverse economic contexts. Originality/Value: This paper contributes to the understanding of how integrating climate action, social responsibility and EFI can create resilient, equitable and sustainable systems. It offers valuable insights for policymakers, businesses and practitioners aiming to advance sustainable development through innovative and inclusive strategies. 2025 Ernesto D. R. S. Gonzalez, Rajeev Sijariya, Amit Kumar Singh and Vikas Garg Published under exclusive licence by Emerald Publishing Limited.
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Climate change; corporate social responsibility; digital technologies; electronic financial inclusion; financial access; social responsibility; sustainable development
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Emerald Publishing
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ISSN: 20430523;
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English
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Article
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online
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Title
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Faculty Publications
Book Chapter
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Balan, Aishwarya; Nijhawan, Pratham; Ramnani, Vineet Kumar; Upreti, Kamal; Kumari, Puja
Title
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Integrating CPS in digital payments 5.0: A study on security risks and evidence-based solutions
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01-01-2025
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Smart Cyber-Physical Systems: Innovations and Real-World Implications;pp.41-63
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<a href="https://doi.org/10.1201/9781003542513-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003542513-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017505654?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017505654?origin=resultslist</a>
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Balan A., CHRIST (Deemed to be University), Delhi NCR, India; Nijhawan P., CHRIST (Deemed to be University), Delhi NCR, India; Ramnani V.K., CHRIST (Deemed to be University), Delhi NCR, 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
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[No abstract available]
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CRC Press
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ISBN: 978-100354251-3; 978-103289293-1;
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English
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Book chapter
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Title
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Faculty Publications
Book Chapter
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Alfred, Daksh; Kumar, Amit; Upreti, Shitiz; Nijhawan, Pratham; Ahmad, Faraz
Title
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Integrating cyber-physical systems with intelligent transportation: Challenges and opportunities
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01-01-2025
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Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow;pp.176-191
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<a href="https://doi.org/10.1201/9781003569251-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003569251-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024651591?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024651591?origin=resultslist</a>
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Alfred D., Department of Economics, CHRIST University, Delhi NCR, India; Kumar A., Department of Economics, CHRIST University, Delhi NCR, India; Upreti S., Manav Rachna International Institute of Research & Studies (MRIIRS), Haryana, Faridabad, India; Nijhawan P., T& A Consulting Global Pvt. Ltd, Gurgugram, India; Ahmad F., Department of Economics, CHRIST University, Delhi NCR, India
Description
An account of the resource
Cyber-physical systems (CPS) are revolutionizing the transportation sector, wherein physical processes are combined with computational systems to create efficient, reliable, and safe transportation solutions. This chapter discusses the ways in which CPS impact contemporary transportation development. The theoretical and practical aspects of CPS have been considered as they follow with the intelligent traffic management systems and driverless cars within this scope of work. The first half of the chapter is then applied to architectural design in CPS, discussing how elements of the physical worldinteraction with cars and roads, for exampleare coupled with cyber systems, such as cloud computing, IoT, and communication networks. Important technical breakthroughs in these areas highlight the key aspects that make real-time decision-making and optimization of systems possible: 5G, edge computing, and artificial intelligence. The chapter also reviews simulation-based techniques in analyzing vehicle behavior and traffic flow, which encompasses insights into how CPS might improve traffic safety and efficiency. Simulations can study very complex transportation scenarios like collision avoidance and control of traffic without the need for real data. The chapter discusses cybersecurity risks, legal issues, and the need for standardized infrastructure to support intelligent transportation systems. It also focuses on the challenges presented by laws and policies in the field of CPS. The interaction of drivers, passengers, and traffic operators with these devices further helps grasp the human factor as well as the experience of a CPS user. The final section of the chapter discusses future directions of CPS research and development, specifically regarding how blockchain technology and quantum computing might advance transportation networks. This chapter will, therefore, give the reader a holistic understanding of how CPS may change the face of transportation in the future by bringing its non-data-driven components to the fore. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors.
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Taylor and Francis
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ISBN: 978-104054278-1; 978-103294165-3;
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English
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Book chapter
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Title
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Book Chapter
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Integrating deep learning in an IoT model to build smart applications for sustainable cities
Description
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These days, many CS experts focus their efforts on IoT. IoT is an emerging & cutting-edge technology that enables many items, including vehicles and home appliances, to connect and cooperate via mechanisms like machine to machine communication, big data, and AI. It has found use in a wide range of settings, from smart homes and cities, to healthcare and agriculture, to factory automation. Smart cities are becoming smarter, cars are getting more features, and health and fitness devices are getting more sophisticated thanks to the internet of things. Many problems that are directly relevant to the IoT's development have yet to be resolved. The exponential development of IoT has given birth to new problems, including concerns about personal data and security. There is need of a comprehensive approach that tackles the scalability, security, efficiency, and privacy concerns raised by the widespread deployment of IoT. 2023, IGI Global.
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Khan H.; Veeraiah V.; Jain V.; Rajkumar A.; Ganesh Kumar R.; Gupta A.; Pandey D.
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Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities, pp. 238-261.
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IGI Global
Date
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2023-01-01
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<a href="https://doi.org/10.4018/978-1-6684-6408-3.ch013" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-6408-3.ch013</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151810809&doi=10.4018%2F978-1-6684-6408-3.ch013&partnerID=40&md5=e305aeb9ef00ee661806254c57b722bd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151810809&doi=10.4018%2f978-1-6684-6408-3.ch013&partnerID=40&md5=e305aeb9ef00ee661806254c57b722bd</a>
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ISBN: 978-166846410-6
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Online
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English
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Book chapter
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Khan H., Rungta College of Engineering and Technology, Bhilai, India; Veeraiah V., Adichunchanagiri University, Mandya, India; Jain V., Teerthanker Mahaveer University, Moradabad, India; Rajkumar A., Teerthanker Mahaveer University, Moradabad, India; Ganesh Kumar R., School of Engineering and Technology, CHRIST University (Deemed), Kengeri, India; Gupta A., Vaish College of Engineering, Rohtak, India; Pandey D., Department of Technical Education, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India
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Faculty Publications
Conference Paper
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Nagashruthi, M.K.; Hemanth, K.S.
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Integrating Diverse Approaches in Medical Image Analysis: PAA-CNN and Feature Extraction Fusion for Classiation of Psychological Disorders using Anatomical Scans
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01-01-2025
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Procedia Computer Science;Volume;260;pp.40-47
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<a href="https://doi.org/10.1016/j.procs.2025.03.175" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.03.175</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008868634?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008868634?origin=resultslist</a>
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Nagashruthi M.K., School Of Computer Science and Applications, REVA University, Bengaluru, 560064, India; Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, 560073, India
Description
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A psychological disorder is a condition that impacts a persons behavior. Due to the contemporary way of life, a large number of individuals suffer from disorders like stress, depression, and other similar ones. These might turn into severe issues that would signiantly impact a persons quality of life. We present a sample framework that uses an Anatomical scan captured along with fMRI. Anatomical scans were used to extract characteristics, which were then utilized to classify using a random forest classir. In a follow-up experiment, CNN is applied to features obtained from the piecewise aggregate approximation method for multi-class classiation of psychological disorders. This method performs noticeably better than the conventional feature extraction techniques, and with this approach, obtained an accuracy of up to 79%. Combining several approaches may boost the classiation and prediction accuracy of medical data. 2025 The Authors. Published by Elsevier B.V.
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anatomical images; feature extraction; machine learning; peicewise aggregate approximation; prominent volumes
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Elsevier B.V.
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ISSN: 18770509;
Language
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English
Type
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Conference paper
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All Open Access; Gold Open Access
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Reviews
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Integrating dye-sensitized solar cells and supercapacitors: portable powerpacks for future energy applications
Description
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Integrating energy storage and harvesting devices have been major challenges and significant needs of the time for upcoming energy applications. Photosupercapacitors are combined solar cell-supercapacitor devices which can provide next-generation portable powerpacks. Owing to advantages like economic and environmental friendliness, dye-sensitized solar cells (DSSCs) offer vast potential for being integrated with energy accumulation devices like supercapacitors. Over the past few years, various types of harvesting cum storage power devices combining DSSCs and supercapacitors have been reported. Over time the devices have improved in both performance and stability providing a broad outlook to possible future advancements including commercialization. We still have many challenges that are yet to be resolved in order to take these powerpacks to the next level of applications in portable and wearable electronics and communication devices. In this context, a detailed analysis and comparison of already reported photo-powered integrated supercapacitors based on DSSCs would give further insights into future advancements. In this review, we have discussed the development of photosupercapacitors, their fabrication strategies, and different materials used as counter electrodes, electrolytes, and dye sensitizers. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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Davis J.K.; George J.; Balachandran M.
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Journal of Materials Science, Vol-59, No. 43, pp. 20176-20203.
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Springer
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2024-01-01
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<a href="https://doi.org/10.1007/s10853-024-10344-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10853-024-10344-w</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208781259&doi=10.1007%2Fs10853-024-10344-w&partnerID=40&md5=de1b397922a8db714cf047dfb4f2d870" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208781259&doi=10.1007%2fs10853-024-10344-w&partnerID=40&md5=de1b397922a8db714cf047dfb4f2d870</a>
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Restricted Access
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A related resource
ISSN: 222461; CODEN: JMTSA
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Online
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English
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Review
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Davis J.K., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, India; George J., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, India; Balachandran M., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, India
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Faculty Publications
Book Chapter
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Nanjundan, Preethi; Mathew, Neha Elizabeth; George, Jossy P.
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Integrating edge computing and iot into smart cities: Fusing digital twins with practical urban planning
Date
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01-01-2025
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Digital Twins for Smart Cities and Urban Planning: From Virtual to Reality;pp.210-226
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<a href="https://doi.org/10.1201/9781003510338-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003510338-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004338826?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004338826?origin=resultslist</a>
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Nanjundan P., Department of Data Science, CHRIST University, Bengaluru, Karnataka, India; Mathew N.E., Department of Data Science, CHRIST University, Bengaluru, Karnataka, India; George J.P., Department of Data Science, CHRIST University, Bengaluru, Karnataka, India
Description
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[No abstract available]
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CRC Press
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ISBN: 978-100351033-8; 978-103283631-7;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
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Faculty Publications
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Singh, Shalini; Anand, Satyam; Anandi; Yadav, Shubhanker
Title
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Integrating Education, Technology, and Sustainable Living: Advancing the Circular Economy for Future Generations
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01-01-2025
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Circular Economy and Sustainable Innovation: Pathways and Applications for a Greener Future, Volume 2;pp.231-251
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<a href="https://doi.org/10.1007/978-3-032-00437-6_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-00437-6_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025619854?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025619854?origin=resultslist</a>
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Singh S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India; Anand S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India; Anandi, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India; Yadav S., Jaipuria Institute of Management, Lucknow, India
Description
An account of the resource
Sustainable living is crucial in reducing societys reliance on natural resources, a goal intimately connected to the principles of the circular economy. This conceptual framework highlights the interplay between education, technology, and the standard of living as fundamental aspects driving sustainable practices. Education empowers individuals with the knowledge and skills necessary to raise awareness and encourage sustainable behavior, shaping beliefs and intentions towards resource efficiency. However, recent studies suggests, increased access to information can paradoxically lead to greater complexities and dilemmas, creating challenges in enacting sustainable consumption and inadvertently slowing sustainable development efforts. Furthermore, the role of technology in the circular economy is indispensable. Advanced technological solutions facilitate the efficient use of resources, offering innovative approaches to mitigate environmental impact while enhancing the quality of life. Earlier literatures underscores the importance of education, funding, and innovation in green technology to achieve Sustainable Development Goals (SDGs) by 2030, highlighting the current state and research hotspots in this domain. A comprehensive literature review utilizing the Web of Science database reveals global contributions to SDG research, emphasizing the need for a balanced approach that integrates education, technology, and a sustainable standard of living. Together, these elements contribute to the development of a society that fosters a culture of sustainability. By promoting a circular economy, where resources are reused, recycled, and repurposed, we can ensure that the needs of the present are met without compromising the ability of future generations to thrive. This approach not only preserves environmental health but also cultivates resilient communities capable of navigating the complexities of sustainability in a rapidly changing world. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subject
The topic of the resource
Circular economy (CE); Digital transformation; Eco-centric business models; Education for sustainability; Environmental awareness; Green finance; Green technology; Innovation in sustainability; Renewable energy; Resource efficiency; Sustainable living
Publisher
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Springer Science+Business Media
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ISBN: 978-303200437-6; 978-303200436-9;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Irfan, Mohammad; Verma, Jatin; Parameswaran, S.; Sheikh, Irfan Ahmed
Title
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Integrating Emerging Technologies: Enhancing Supply Chain Optimization Through AI, IoT, and Blockchain
Date
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01-01-2025
Source
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Enhancing Social Sustainability in Manufacturing Supply Chains;pp.199-220
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<a href="https://doi.org/10.4018/979-8-3693-9740-4.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9740-4.ch007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000710660?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000710660?origin=resultslist</a>
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Irfan M., Christ University, India; Verma J., Christ University, India; Parameswaran S., Christ University, India; Sheikh I.A., Ethiopian Civil Service University, Ethiopia
Description
An account of the resource
The rapid rise of technologies like AI, IoT, and blockchain is transforming supply chain management by boosting efficiency, transparency, and resilience. This paper examines how these technologies optimize supply chains, focusing on predictive analytics, real- time monitoring, and secure data exchange. AI excels in automation and decision- making, IoT enables real- time data gathering, and blockchain ensures trust and transparency. Case studies from Amazon, Maersk, Walmart, and Pfizer illustrate improvements in efficiency, risk reduction, and cost savings. Additionally, challenges like high costs, data privacy, and lack of standardization are discussed, along with future trends in edge computing, quantum computing, and digital twins. The study highlights AI, IoT, and blockchain as essential to building smarter, adaptable supply chains capable of thriving in todays global economy. 2025, IGI Global Scientific Publishing.
Publisher
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IGI Global
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A related resource
ISBN: 979-836939742-8; 979-836939740-4;
Language
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English
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Book chapter
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Faculty Publications
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Kapoor, Shalini; Jain, Samkit; Gupta, Praveen Kumar; Vidushi; Bhardwaj, Shashank
Title
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Integrating Explainability and Fairness in Credit Risk Prediction: A Hybrid Approach Using Tabnet, LightGBM, SHAP, and Counterfactual Explanations on the FICO Dataset
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01-01-2026
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2026 2nd International Conference on Computing, Sciences and Communications, ICCSC 2026;
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<a href="https://doi.org/10.1109/ICCSC67078.2026.11468663" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCSC67078.2026.11468663</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038013135?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038013135?origin=resultslist</a>
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Kapoor S., Scset, Bennett University, Greater Noida, India; Jain S., Scset, Bennett University, Greater Noida, India; Gupta P.K., Scset, Bennett University, Greater Noida, India; Vidushi, Christ University, Bengaluru, India; Bhardwaj S., Kiet University, Delhi-NCR, Ghaziabad, India
Description
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Transparent and fair credit risk assessment is essential for responsible lending in modern financial systems. This paper presents an interpretable and ethically grounded machine learning framework for loan default prediction using the FICO Explainability Challenge dataset. We combine LightGBM, a high-performing gradient boosting model for tabular data, with TabNet, a deep learning architecture that provides intrinsic interpretability through attentive feature selection. To enhance transparency, SHapley Additive exPlanations (SHAP) are employed for global and local feature attribution, while counterfactual explanations generated using the DiCE framework offer actionable recourse. Fairness is evaluated and mitigated using IBM's AI Fairness 360 toolkit. Experimental results demonstrate that the proposed hybrid approach achieves strong predictive performance while ensuring interpretability and fairness, making it suitable for trustworthy and regulation-compliant credit risk modeling. 2026 IEEE.
Subject
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Counterfactual Explanations; Credit Risk Prediction; Explainable AI; Fairness; LightGBM; SHAP; TabNet
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833159576-0;
Language
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English
Type
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Conference paper
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Title
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Faculty Publications
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Shobana, V.; Maheshwari, S.; Savithri, M.; Ramasamy, Siva Shankar; Kumar, N.
Title
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Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Date
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01-01-2025
Source
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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025 - Proceedings;pp.461-469
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<a href="https://doi.org/10.1109/ICMLAS64557.2025.10969073" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMLAS64557.2025.10969073</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004811323?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004811323?origin=resultslist</a>
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Shobana V., Dr. N.G.P. Arts and Science College, Department of Computer Science with Cyber Security, Coimbatore, India; Maheshwari S., Dr. N.G.P. Arts and Science College, Department of Computer Science, Coimbatore, India; Savithri M., Christ University, Department of Data Science, Bengaluru, India; Ramasamy S.S., International College of Digital Innovation, Chiang Mai University, Thailand; Kumar N., Dr. N.G.P. Arts and Science College, Department of Computer Science, Coimbatore, India
Description
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Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE.
Subject
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Brain Stroke; Explainable Artificial Intelligence (XAI); Machine Learning
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
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ISBN: 979-833150574-5;
Language
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English
Type
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Conference paper
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Faculty Publications
Article
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Rajagopal, Manikandan; Mandala, Gowthamm; Ranganathan, Nagendran; Varthan Velumani, Manoj; Patro, Pramoda; Arumugam, Shankar
Title
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Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics
Date
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01-01-2025
Source
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Journal of Fuzzy Extension and Applications;Volume;6;Issue;3;pp.484-505
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<a href="https://doi.org/10.22105/jfea.2025.490702.1715" target="_blank" rel="noreferrer noopener">https://doi.org/10.22105/jfea.2025.490702.1715</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007700712?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007700712?origin=resultslist</a>
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Rajagopal M., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Mandala G., Biological Research Student, Purdue University, West Lafayette, United States; Ranganathan N., Department of Computer science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, India; Varthan Velumani M., Axtria Inc, Berkeley Heights, 07922, NJ, United States; Patro P., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Arumugam S., Department of ECE, Manakula Vinayagar Institute of Technology, Puducherry, India
Description
An account of the resource
This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%. 2025, Research Expansion Alliance (REA). All rights reserved.
Subject
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Clinical decision-making; Healthcare; Hesitant fuzzy sets; Machine learning; Patient data
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Research Expansion Alliance (REA)
Relation
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ISSN: 27831442;
Language
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English
Type
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Article
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Format
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online
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Agarwal, Rupanshi; Sanghi, Akash; Agarwal, Gaurav; Gupta, Amit Kumar; Upreti, Kamal
Title
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Integrating intelligence: The convergence of computer science and engineering in cyber-physical systems
Date
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01-01-2025
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Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services;pp.49-67
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<a href="https://doi.org/10.1201/9781003508618-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003508618-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218319127?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218319127?origin=resultslist</a>
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Agarwal R., Department of Computer Science and Engineering, Invertis University, Uttar Pradesh, Bareilly, India; Sanghi A., Department of Computer Science and Engineering, Invertis University, Uttar Pradesh, Bareilly, India; Agarwal G., Department of Computer Science and Engineering, Invertis University, Uttar Pradesh, Bareilly, India; Gupta A.K., Department of Computer Applications, KIET Group of Institutions, Uttar Pradesh, Ghaziabad, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Uttar Pradesh, Ghaziabad, India
Description
An account of the resource
The dynamic and innovative paradigm known as cyber physical systems (CPSs) arises from the merging of digital technology and physical infrastructure. This chapter provides a thorough analysis of CPSs, covering the basic ideas, constituent parts, a range of applications, and their integration with more complex subjects. Fundamentally, CPSs represent the smooth fusion of computational and physical components, enabling real-time control, analysis, and monitoring. The fundamentals of CPSs are explained in this chapter, with a focus on how they facilitate the development of interconnected networks that can coordinate complicated tasks across multiple domains. A close examination of the complex interactions that occur between sensors, actuators, processors, and communication networks in CPS designs demonstrates how these components work together to gather, process, and distribute data. Furthermore, a wide range of industries, including infrastructure, manufacturing, transportation, and healthcare, are impacted by the diverse applications of CPSs. CPSs transform conventional processes, improving efficiency, safety, and production. Examples of these processes include intelligent healthcare devices that monitor patient vitals and smart transportation systems that optimise traffic flow. When CPSs are combined with more complex subjects, they become even more powerful, accelerating innovation and change in a variety of fields. By enabling CPSs to process and analyse data at network edges, edge computing can lower latency and bandwidth consumption. Algorithms for machine learning improve decision-making, allowing CPSs to adjust and gain knowledge from real-world data. By protecting CPSs from cyberattacks, security and resilience measures guarantee the availability and integrity of vital systems. Furthermore, human CPS contact opens up new collaborative paradigms and gives people the ability to communicate with intelligent systems in a natural way. To sum up, this chapter gives readers a thorough grasp of CPSs and how they have revolutionised contemporary life. It adds to the continuing conversation on CPS research, innovation, and implementation by clarifying their basic ideas, elements, applications, and integration with more complex subjects. With ongoing research and cooperation, CPSs have the potential to completely transform our world and bring in a new era of intelligence, creativity, and connectivity. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors.
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CRC Press
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ISBN: 978-104030115-9; 978-103283279-1;
Language
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English
Type
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Book chapter
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Dublin Core
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Title
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Faculty Publications
Conference Paper
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Gupta, Shrutika; Naskar, Annesha; Misra, Bitan; Hemanth, K.S.; Chakraborty, Sayan; Kulkarni, Aparna Shrikant
Title
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Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
Date
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01-01-2025
Source
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Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.418-423
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<a href="https://doi.org/10.1109/ICRCICN68210.2025.11364795" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRCICN68210.2025.11364795</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035367086?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035367086?origin=resultslist</a>
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Gupta S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Naskar A., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science and Engineering, West Bengal, India; Hemanth K.S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Chakraborty S., JIS College of Engineering, Department of Computer Science and Technology, West Bengal, Kalyani, India; Kulkarni A.S., MIT Academy of Education, Alandi, Department of Computer Science and Engineering (Data Science), Pune, India
Description
An account of the resource
To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE.
Subject
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ARCANE; Clustering; K-means++; Machine Unlearning
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISSN: 28323645;
Language
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English
Type
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Conference paper
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Singha, Surjit; Singha, Ranjit; Thomas, Jishamol; Natal, Melita Stephen; Haokip, Alphonsa Diana
Title
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Integrating Koch Rajbongshi indigenous knowledge into teacher education: Bridging local traditions with global standards
Date
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01-01-2025
Source
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Funds of Knowledge in Teacher Education: Sustaining Local Diversity Amidst Global Standards;pp.463-486
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<a href="https://doi.org/10.4018/979-8-3693-8094-9.ch021" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8094-9.ch021</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010796493?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010796493?origin=resultslist</a>
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Singha S., Kristu Jayanti College (Autonomous), Bengaluru, India; Singha R., Christ University, Bengaluru, India; Thomas J., Christ College of Science and Management, Alambady, India; Natal M.S., Amity University, Greater Noida, India; Haokip A.D., Don Bosco College (Autonomous), India
Description
An account of the resource
This chapter examines the integration of Koch Rajbongshi indigenous knowledge into teacher education, preserving cultural diversity while aligning with global standards. The Koch community, spanning India, Nepal, Bhutan, and Bangladesh, holds rich cultural assets, including oral traditions and ecological wisdom, which can enhance curricula. Recognizing the Koch Rajbongshi as a Scheduled Tribe and including their language in the Eighth Schedule of the Indian Constitution would aid in preserving their heritage. The chapter proposes strategies for integrating indigenous knowledge into mainstream education, addressing challenges and opportunities. It underscores the collective role of educators, policymakers, and Indigenous communities in fostering inclusive education and highlights global best practices for sustaining indigenous knowledge in formal learning systems. 2025, IGI Global Scientific Publishing.
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IGI Global
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ISBN: 979-836938096-3; 979-836938094-9;
Language
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English
Type
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Book chapter
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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George, Jossy; Yadav, Jai; Nair, Akhil M.
Title
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Integrating LightGBM and XGBoost for Robust Plant Disease Classification: A Homogenous Stacking Approach
Date
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01-01-2025
Source
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Lecture Notes in Networks and Systems;Volume;1377 LNNS;pp.409-422
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<a href="https://doi.org/10.1007/978-981-96-5370-6_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5370-6_30</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018796519?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018796519?origin=resultslist</a>
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George J., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Yadav J., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Nair A.M., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India
Description
An account of the resource
In addressing the critical challenge of early and accurate plant disease diagnosis, this study explores the application of a novel homogeneous multi-layered stacking model utilising Light Gradient Boosting Model (LGBM) and Extreme Gradient Boost (XGB) for the detection of plant diseases. Traditional approaches often rely on basic stacking methods; however, this research seeks to explore the intricacies of altering model architecture, combining the strengths of LGBM and XGB classifiers to build a highly accurate and efficient disease detection system. Comprehensive evaluations were conducted using metrics such as AUCROC curves, Confusion matrix and F1 scores. The ROC curve for the stacked model demonstrated superior performance with a score of 85.12%, compared to 83.09% for the single LightGBM model used for comparative analysis. The future scope of ML in agriculture includes integrating such models with real-time monitoring systems and expanding its applications to diverse crops and environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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AUCROC curve; Confusion matrix; F1 score; Homogenous stacking; Plant disease prediction
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Springer Science and Business Media Deutschland GmbH
Relation
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ISSN: 23673370; ISBN: 978-981965369-0;
Language
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English
Type
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Conference paper
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Sadasivam, Boobalakrishnan; Rajamohan, Kavitha
Title
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Integrating LLMs into Smart Home Architecture: Design, Implementation, and Experimental Insights
Date
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01-01-2025
Source
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5th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2025;
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<a href="https://doi.org/10.1109/ICMNWC66779.2025.11354247" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMNWC66779.2025.11354247</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033689042?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033689042?origin=resultslist</a>
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Sadasivam B., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Rajamohan K., Christ (Deemed to be University), Department of Computer Science, Bangalore, India
Description
An account of the resource
Smart home systems are a key application of the Internet of Things (IoT) paradigm. These devices are either directly or indirectly connected to a network in order to perceive actions based on user willingness or sensing. As Information and Communication Technology (ICT) advances, Large Language Models (LLMs) are growing increasingly potent. Agents with LLM capabilities could be very supportive for smart home systems because of their natural language comprehension. In this paper, we introduce an LLM-powered smart home agent with real-time connectivity to smart home devices. This proposal has been experimented with and evaluated with typical smart home use cases. This experimental architecture illustrates a realtime scenario of a standard smart home, where sensors, lights, and switches are interconnected in a multi-tiered environment. The sensor, gateway, and switch modules are connected to the smart home edge server via a Web of Things (WoT) interface. The experiment has been conducted with various types of smart home use case prompts, including status requests, control requests, automation requests, and reasoning requests. The outcome of the experiment indicates that the addition of LLM to smart homes excels in natural conversational patterns compared to keywordbased agents. The prompt response time, which is unsuitable for time-sensitive tasks like anomaly detection, is a drawback, and edge LLMs could be a solution. 2025 IEEE.
Subject
The topic of the resource
Agents; Large Language Model; Sensors; Smart Home; Web of Things
Publisher
An entity responsible for making the resource available
Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833155664-8;
Language
A language of the resource
English
Type
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Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments
Subject
The topic of the resource
Air quality; AQI; Insights into pollutant dynamics; Machine learning; Prediction model
Description
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Air pollution poses a significant environmental and health challenge in Delhi, India. This research focuses on predicting the Air Quality Index (AQI) for Delhi utilizing machine learning techniques. The research methodology encompasses comprehensive steps such as data collection, preprocessing, analysis, and modeling. Data comprising various pollutants and meteorological parameters were gathered from the Central Pollution Control Board (CPCB) spanning from January 1, 2016, to December 30, 2022. Missing values were imputed using the IterativeImputer method with RandomForestRegressor as the estimator. Data normalization and variance reduction were achieved through Box-Cox transformation. Spearman Rank Correlation analysis was employed to explore relationships between features and AQI. Initial evaluation of nine machine learning algorithms identified Random Forest and XGBoost as the top performers based on accuracy. These algorithms were further optimized using 5-fold cross-validation with RandomizedSearchCV. The results demonstrated the efficacy of both algorithms in AQI prediction. Notably, PM2.5 and CO concentrations emerged are most influential features, highlighting the potential for AQI improvement in Delhi through the reduction of these pollutants. This research distinguishes itself through a meticulous examination of the complex interconnections between pollutants and AQI, providing invaluable insights to inform targeted interventions and enduring policies geared towards improving air quality in Delhi. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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K K.; S.K A.; R D.; Ravindiran G.
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Earth Science Informatics, Vol-17, No. 4, pp. 3733-3748.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/s12145-024-01382-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12145-024-01382-8</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196501568&doi=10.1007%2Fs12145-024-01382-8&partnerID=40&md5=cc5ab7ab481a0078a9faaec8a8af5f8b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196501568&doi=10.1007%2fs12145-024-01382-8&partnerID=40&md5=cc5ab7ab481a0078a9faaec8a8af5f8b</a>
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Restricted Access
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ISSN: 18650473
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Online
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English
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Article
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K K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, 532127, India; S.K A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, India; R D., Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, 600123, India; Ravindiran G., Department of Civil Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Telangana, Hyderabad, 500090, India
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Adak, M.; Yadav, Rashmi Akshay; Hiremath, Shashank M; Gorkhe, Monika; Gopukumar, S.T.; Sankari, V.
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Integrating Machine Learning with Financial Risk Modeling for Portfolio Management
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01-01-2025
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International Conference on Communication, Computer and Information Technology, IC3IT 2025;
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<a href="https://doi.org/10.1109/IC3IT66137.2025.11340922" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3IT66137.2025.11340922</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033366493?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033366493?origin=resultslist</a>
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Adak M., Yeshwantrao Chavan College of Engineering, Department of Applied Mathematics, Nagpur, India; Yadav R.A., Christ University, Department: School of Business and Management-MBAk, Karnataka, Bangalore, India; Hiremath S.M., Presidency Business School, Presidency College, Department of Mba, Karnataka, Bengaluru, India; Gorkhe M., Symbiosis Skills and Professional University, Department: School of Bfsi, Pune, India; Gopukumar S.T., Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Department of General Surgery, Tamil Nadu, Chennai, India; Sankari V., K.Ramakrishnan College of Engineering, Department of Ai&ds, Trichy, India
Description
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Financial markets may be unpredictable and volatile; the ability to perform proper risk forecasting and effectiveness in performing an efficient portfolio is of primary importance when making wise investment choices. The nonlinear trends, and time dependence applied in financial data are usually not captured in conventional predictive models. The research is suggesting a new hybrid architecture LSTXplain that combines with and is afforded capabilities of SHAP, and exogenized with LSTM networks as well as Experimental learning. The aim of this paper, which is entitled Integrating Machine Learning with Financial Risk Modeling to Portfolio Management is to combine sequential learning with interpretability in an attempt to deepen financial risk prediction and portfolio optimization. The model is intended to forecast various measurements of financial risk, such as volatility and Value-at-Risk, and is also likely to establish the causes of each of these estimates. LSTXplain uses historical stock prices, technical features and optionally, sentiment scores designed using financial news to train a robust deep learner. Model outputs are then fed through SHAP that allocates a value of importance of a feature and discover that this allows analysts to know and trust what the model does. In order to compare the framework, Yahoo Finance data was applied, and the findings were compared to the traditional models ARIMA, SVM, Random Forest, and MLP. It has a prediction accuracy of over 98 percent which does not just complement the risk forecasting but enables a portfolio management to act. The analysis is a bridge between the performance of DL and explainable AI in the financial risk prediction. Statistical significance were applied to prove that such improvements are significant, and it is established that results are significant at p<0.05. 2025 IEEE.
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Explainable AI; Financial Risk Forecasting; LSTM; Portfolio Management; SHAP
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152483-8;
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Book Chapter
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Integrating mindfulness and addiction awareness in higher education: Strengthening resilience and promoting well-being
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This chapter explored integrating mindfulness and addiction awareness within higher education. The journey uncovers these practices' profound potential in enhancing student resilience and well-being. The transformative impact of a mindful approach is underscored by examining their symbiotic relationship, individual benefits, and intersection with microlearning. From understanding addiction's prevalence among students to fostering a compassionate learning environment, the discussion navigates ethical considerations, cultural sensitivity, and challenges. A resounding call to action resonates, urging higher education institutions to embed these practices strategically, cultivating an environment prioritizing holistic student growth and development. The promise lies in a brighter future-a generation of self-aware, resilient individuals empowered to navigate challenges with poise, empathy, and well-being. 2024, IGI Global.
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Singha R.
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Global Perspectives on Micro-Learning and Micro-Credentials in Higher Education, pp. 235-256.
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IGI Global
Date
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-0343-6.ch014" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-0343-6.ch014</a>
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ISBN: 979-836930345-0; 979-836930343-6
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Online
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English
Type
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Book chapter
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Singha R., Christ University, India