Addressing Security Challenges in AI-Driven Cyber Security: Enhancing Resilience While Fostering Sustainable Practices with Green Computing
Cyber security; Green computing; greener environment; security challenges; sustainability
The modern cyber security environment changed through increased sophistication and complexity of cyber threats that requires organizations to use artificial intelligence (AI) technologies for strengthened security frameworks. It also limits the adverse impact within the populace by a decrease in carbon dioxide emissions, energy conservation, decrease wastage of electronic gadgets and assistance to sustainability with renewable resources. Among the practices of creating the green environment are the measures of virtualization, improving the quality of the hardware to increase the energy efficiency and using the cooling technologies efficiently. The Sustainable Cyber security practices are examining the measures and innovation for minimizing energy usage by integrated cyber security tools/infrastructure, green data center/network, and practicing green software engineering. In this domain, Sustainability Cyber security employs energy conservative cryptographic algorithms and software architecture, low energy cryptographic physical devices, power-conscious security protocols; efficient virtualization by integrating these approaches, they can advance sustainability into higher security statures. Besides, the enforcement of those security practices will help to address green data center objectives like server virtualization, and other efficiency data storage products. Cyber Security algorithms will act to lessen the time construct of cryptographic operations to less computational power, and hence less energy consumption. It also looks at the direction that sustainability is likely to take in the future, which may include such policies and structures as are likely to promote green computing in cyber security. Also through this case study, we have been able to incorporate green computing into its cyber security programs for a better and environmentally friendly future. The advanced strategic planning through automation enables organizations to develop stronger defense capabilities as they adjust to security threats which keep evolving in the present-day landscape. 2026 Scrivener Publishing LLC.
Geetha, P.; Abirami, G.; Padmavathy, T.; Sivagami, S.; Vinodha, D.
AI Trust, Risk, and Security Management: Framework, Principles, and Practices;pp.205-226
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1002/9781394393022.ch9 https://www.scopus.com/pages/publications/105032825644?origin=resultslist
Geetha P., Department of CINTEL, School of Computing, SRM Institute of Science and Technology (KTR Campus), Chennai, India; Abirami G., Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science & Technology, Chennai, India; Padmavathy T., Department of Database Systems, SCOPE, VIT University, Vellore, India; Sivagami S., Department of CSE, Saveetha School of Engineering, SIMATS, Thandalam, Chennai, India; Vinodha D., Department of Computer Science and Engineering, CHRIST University, Bangalore, India
AI in Financial Fraud Detection and Prevention
anomaly detection; Artificial intelligence (AI); behavioral biometrics; deep learning; fraud detection; machine learning; NLP; predictive analytics
Fraud has always posed problems to financial institutions and with the rapid growth of digital transactions, and its complexity has increased beyond detection. Normal methods of fraud detection that depend on rules only are severely outdated and ineffective against newer types of schemes. It is now imperative to employ more sophisticated mechanisms for fraud detection considering the evolvement of financial crimes. The massive amounts of transnational data that need to be analyzed to detect fraudulent patterns can now be processed with medium to high levels of accuracy using AI with the help of machine learning, deep learning, and natural language processing (NLP), and fraud detection and prevention have been transformed for the better. Algorithms of machine learning like supervised, unsupervised, and reinforcement learning are central to the features of fraud detection. Suspicious transactions are detected during supervised learning by using already existing fraudulent data, whereas unsupervised learning detects all anomalies without any prior defined labels. Through real-time data input, reinforcement learning adjusts its detection methodologies. Deep learning models such as convolutional neural networks and recurrent neural networks identify and process fraud indicators hidden within messages or intricate datasets. Moreover, through intricate analysis of customer interactions, NLP techniques detect fraudulent activities by identifying phishing attempts and deceptive communications. The chapter touches upon the issues of implementing AI oriented fraud detection in realms like e-commerce and entertainment. Identifying fraud from e-commerce is complicated by factors like high volume of transactions, false positives, privacy issues, and the endless frameworks of fraud. Finally, the chapter provides a summary of the main insights and makes recommendations for further investigation like incorporating blockchain, federated learning, and higher explainability to bolster AI powered fraud detection systems. 2026 Scrivener Publishing LLC.
Haseena, Shaik Valli; Jasawani, Neha; Ayasha; Suresh, Gaikwad Beena; Suresh, Gaikwad Beena; Shanavas, Simna
Designing Inclusive Classrooms: Integrating Emerging Technologies for Equity and Social Justice;pp.295-327
https://doi.org/10.1002/9781394389223.ch14 https://www.scopus.com/pages/publications/105034021951?origin=resultslist
Haseena S.V., Christ University, Bengaluru, India, Department of Computer Applications, Presidency College, Bengaluru, India; Jasawani N., Department of Computer Applications, Presidency College, Bengaluru, India; Ayasha, Department of Computer Science, Christ College of Science and Management, Bengaluru, India; Suresh G.B., Department of Computer Applications, Presidency College, Bengaluru, India; Suresh G.B., Department of Computer Applications, CMR University, Presidency College, Bengaluru, India; Shanavas S., Department of Computer Applications, Presidency College, Bengaluru, India
Adaptive AI: Transforming Natural Language Processing and Industry Applications
adaptive AI; AI-powered learning; chatbots; finance; machine learning; Natural language processing; personalized healthcare
Adaptive Artificial Intelligence (AI) is an innovative development in the domain of intelligent systems because machines have attained the ability to independently learn, modify, and optimize their operations based on experience and other real-time information. While traditional artificial intelligence (AI) is programmed to follow a certain set of algorithms and rules step-by-step, adaptive AI systems have a level of independence that affords them the flexibility to change their actions. As a result, healthcare, finance, education, manufacturing, and many other industries can now employ AI, which was formerly not possible due to its inflexible nature, for enhanced and efficient decision-making, improved consumer practice, and effective working processes. There is significant scope for adaptive AI to transform Natural Language Processing (NLP) as well as business practices and solve complex problems. On the other hand, there is also the advancement of dependency on data, algorithmic perception, and ethical inquiries concerning the application of these technologies which pose a challenge. This proposed chapter emphasizes the techniques and applications of Adaptive AI in NLP and the ethical challenges that make it authoritative to adopt responsible development of artificial intelligence. 2025 Scrivener Publishing LLC.
Meena Kumari, P.; Ramakrishna Reddy, K.; Manikandakumar, M.
Adaptive Artificial Intelligence: Fundamentals, Challenges and Applications;pp.73-100
https://doi.org/10.1002/9781394389070.ch4 https://www.scopus.com/pages/publications/105023735018?origin=resultslist
Meena Kumari P., ICFAITech, ICFAI Foundation for Higher Education, Hyderabad, India; Ramakrishna Reddy K., ICFAITech, ICFAI Foundation for Higher Education, Hyderabad, India; Manikandakumar M., Artificial Intelligence and Data Science, School of Engineering & Technology, Christ University, Bengaluru, India
Data Driven Emergency Response Management for UAV-Based Future Transportation A Case Study
emergency response management; remote monitoring; secure communication; transportation; UAV interoperability; UAV mapping; UAV operational challenges; Unmanned aerial vehicle
The transportation sector plays a vital role and is heavily involved in emergency response and disaster relief because of the necessity for quick deployment, evacuation and rescue operations, supply chain support, infrastructure restoration, aerial support and surveillance, traffic management, and route optimization, among other things. All of these are areas that can greatly benefit from the use of drones. The capabilities of drones provide an efficient and speedy option for surveying impacted regions, spotting possible dangers, finding survivors, accessing dangerous or inaccessible locations, delivering supplies, and generally facilitating relief efforts with speed and minimal human risk. UAV (Unmanned aerial vehicle)-based transportation can become a precise substitute for time-sensitive items, emergency relief, medical supplies, and more by overcoming obstacles like traffic and geographical accessibility. The success of emergency response management for UAV-based transportation is dependent upon the quality of the data collected and the effectiveness of its analysis and interpretation. Hence, in this chapter, we propose to analyze and explore the challenges and issues involved in the design and implementation of a data-driven emergency response management system for UAV-based future transportation and its applications. 2026 Scrivener Publishing LLC.
Vinodha, D.; Jenefa, J.; Anita, E.A. Mary; Al-Dahidi, Sameer
Drones for Transportation Logistics and Disaster Management;pp.329-370
https://doi.org/10.1002/9781394386451.ch15 https://www.scopus.com/pages/publications/105032099530?origin=resultslist
Vinodha D., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Jenefa J., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Anita E.A.M., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Al-Dahidi S., School of Applied Technical Sciences, German Jordanian University, Amman, Jordan
Journey to Transportation and Logistics Management Using Drone
Drones; environment; industry; logistics; sustainability; transportation
The speedy adoption and amalgamation of drone technology in every sector of life have correlated environmental implications. It has come to reality due to revolutionization on the technology front and the movement for a digitized world by adopting digitization in the course of action. The drones have the capacity to decrease the rate of carbon emissions at significant levels in transportation and logistics management. So, this is becoming the need of an hour to apply the technology for the well-being of an individual. The comprehensive study and assessment in this chapter will address the implications of an environment connected to drone-based transportation and logistics operations. It will comprise the assessment based on academic studies, industry reports, and environmental assessments in terms of carbon emissions, energy consumption, and ecological footprint. The chapter will highlight the concerns and contributions in view of mitigating the environmental implications linked with the deployment of drones in transportation and logistics, enabling stakeholders to develop strategies that foster sustainability in the industry. 2026 Scrivener Publishing LLC.
Rani, Seema; Anjum, Misbah; Chawla, Shivangi; Sharma, Vandana
Drones for Transportation Logistics and Disaster Management;pp.1-23
https://doi.org/10.1002/9781394386451.ch1 https://www.scopus.com/pages/publications/105032074061?origin=resultslist
Rani S., Amity Institute of Information Technology, Amity University, Noida, India; Anjum M., Amity Institute of Information Technology, Amity University, Noida, India; Chawla S., Department of Computer Science, Jamia Millia Islamia, Delhi, India; Sharma V., Computer Science Department, Christ University, Bengaluru, India
Enhancing the Operational Efficiency of FPOs by Using Predictive Model Building in Machine Learning Through Feature Selection Method
feature selection method; FPO operational efficiency; FPO policy inputs; FPO shortcomings; predictive power
This study employs a machine learningbased feature selection approach to identify key factors that significantly enhance the predictive accuracy of models assessing farmer producer organization (FPO) performance. It focused on an FPO in the Anantapur and Kadapa districts of Andhra Pradesh, India; the research investigated the operational effectiveness of small and marginal farmers who are members by the end of 2022. The study reviewed existing shortcomings and proposed necessary policy amendments to ensure the sustainability of FPOs. The study utilized a survey design method, with the exploratory questionnaire through the field study. A sample of 73 small-scale producer farmers from the region was selected for the analysis. The result indicated that a) the reason for the operational efficiency of FPOs, b) the gradient boosting regressor achieved the highest testing accuracy of 0.78 to predict the best feature for FPO performance, and c) FPO insights for policy guidelines. 2025 Scrivener Publishing LLC.
Sreelakshmi, P.; Basavaraj, Santosh; Josephine, V. L. Helen
Green Innovations in Computational Intelligence: Sustainable Strategies and Emerging Technologies;pp.159-187
https://doi.org/10.1002/9781394383658.ch8 https://www.scopus.com/pages/publications/105023235995?origin=resultslist
Sreelakshmi P., School of Business and Management (SBM), Christ University, Bangalore, India; Basavaraj S., School of Business and Management (SBM), Christ University, Bangalore, India; Josephine V.L.H., School of Business and Management (SBM), Christ University, Bangalore, India
Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications
computer vision; Graph convolutional neural networks (GCNNs); graph-structured data; visual data analysis; visual information analysis
Graph Convolution Neural Networks (GCNNs) are an important concept in advancing computer vision by transforming the understanding and modeling of graph-structured data. They have a unique capability to capture intricate relations along with the visual content that goes beyond the traditional and usual convolutional neural networks, it also empowers computers to observe and interpret the complex interconnection between the elements in images, which enhances the depth and nuance of visual dentata analysis. As a revolutionary study in computer vision, GCNNs are poised to transform various industries by unleashing new frontiers in the visual information domains analysis and interpretation. Their multifaceted applications promise to reshape the landscape of computer vision. 2026 Scrivener Publishing LLC.
Malini, A.; Sharma, Vandana; Felicia Lilian, J.; Dhanaraj, Rajesh Kumar; Sharangapriyan, S.; Shrinivas, S.
Graph Convolutional Neural Networks for Computer Vision;pp.1-20
https://doi.org/10.1002/9781394356362.ch1 https://www.scopus.com/pages/publications/105026928660?origin=resultslist
Malini A., School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India; Sharma V., Christ University, Bengaluru, India; Felicia Lilian J., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India; Dhanaraj R.K., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India; Sharangapriyan S., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India; Shrinivas S., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, India
Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions
Data sparsity; Explainable AI; Federated learning; Generative adversarial networks; Generative AI; Personalization; Recommender systems; Transformers; Variational autoencoders
The recommender systems have become a must in delivering personalized experiences across digital platforms. Still, traditional approaches, such as collaborative and content-based filtering, suffer from some inherent limitations: data sparsity, scalability, and dynamic user adaptation. In this context, generative AI emerges as a game-changing solution empowered by state-of-the-art models like variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers to overcome the above-mentioned limitations. These models make possible the synthesis of user-item interaction data, uncovering latent patterns and providing context-aware recommendations, thereby redefining personalization in recommender systems. This chapter provides a detailed survey on the role of generative AI in recommender systems, their components, architectures, and applications. Case studies in e-commerce, entertainment, and education provide insights into how generative models help drive personalization, tackle the cold-start problem, and adapt dynamically to the evolution of user behaviors. Nevertheless, open issues regarding computational complexity, privacy protection, and ethical considerations remain. To address these, the chapter outlines the future enhancements in the areas of federated learning for privacy-preserving collaboration, multimodal data integration for holistic user profiling, and explainable AI frameworks to foster transparency and trust. Bridging these gaps would let generative AI-driven recommenders further revolutionize personalization, scalability, and inclusiveness, opening up a way to innovative solutions across the board in various industries. 2026 by John Wiley & Sons Inc. All rights reserved.
Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits;pp.201-223
https://doi.org/10.1002/9781394351572.ch9 https://www.scopus.com/pages/publications/105038510500?origin=resultslist
Haseena S.V., Presidency College, Christ University, Bengaluru, India; Jaswani N., Presidency College, Bengaluru, India
Graph Neural Networks in Recommendation Systems for Superior User Experiences
Graph Attention Networks (GATs); Graph Convolutional Networks (GCNs); Graph Neural Networks (GNNs); GraphSAGE; Recommendation systems
Recommender systems are now an essential tool for traversing huge amounts of online information, especially as user tastes change in dynamic settings. They can be classified into different types based on their use, such as collaborative filtering and content-based recommendation. Graph Neural Networks (GNNs), however, are best suited to learn from graph-structured data and have become a revolutionary technology in building recommendation systems based on their capacity to capture intricate relationships and dependencies in graph-structured data. Unlike traditional methods, GNN-based recommendation systems capture both local and global connectivity patterns in user-item interaction graphs, enhancing prediction accuracy and robustness. This study looks at key types of Graph Neural Networks, known as GNNs, and includes Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. Each type is good at tackling specific challenges like making personalized recommendations, handling cold-start problems, and managing large-scale data effectively. Big companies across different industries are using GNNs to enhance their services. For example, e-commerce giants like Amazon and Alibaba, and streaming platforms like Netflix and Spotify, use these networks to boost user engagement and satisfaction. The study also covers strategies for applying GNNs. This includes constructing user-item bipartite graphs, including domain-specific features, and employing contrastive learning methods to improve functionality. By combining theoretical concepts with real-world usage, we hope to offer an informed view of GNNs in recommendation systems. In addition, metrics like Precision, Recall, and Normalized Discounted Cumulative Gain (nDCG), providing implementable recommendations for future development in this fast-moving field, will be addressed. 2026 by John Wiley & Sons Inc. All rights reserved.
Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits;pp.121-150
https://doi.org/10.1002/9781394351572.ch6 https://www.scopus.com/pages/publications/105038490822?origin=resultslist
Upadhyay P., Department of Computer Science, Christ University, Bangalore, India; Banu P.K.N., Department of Computer Science, Christ University, Bangalore, India
Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing
Hybrid computing models; Intelligent data processing; Market trends in QISC; Quantum-inspired soft computing (QISC)
Quantum-Inspired Soft Computing (QISC) is an advanced concept in computational intelligence in the current era of hi-technology, which has been adapted to principles underpinning quantum mechanics, including superposition and entanglement in the conven tional computing systems. The current chapter aims to identify the growing trends in the market environment concerning QISC for intelligence data processing, specifying the aspects of its applicability, costuse ratio, and adaptability among different industries. The increased need for the utilization of enhanced methods of data handling arising from big data and AI progress has made QISC viable for handling optimization problems, machine learning, and predictive modeling in addition to quantum computing. Major business sectors, such as finance, health care, supply chain, and energy sectors, have benefited from the use of QISC to enhance operational management, decision making, and system reliability. This chapter also discusses how leading players such as Microsoft, IBM, and DWave are in the course of incorporating QISC in cloud environments as well as in hybrid computing systems. Advancements in hardware, such as GPUs and quantum-inspired processors, and in algorithms, such as tensor networks and reinforcement learning, have further extended the usage of QISC. However, there are issues such as standardization, interdisciplinary qualified staff, and computational complexity, which remain important unsolved tasks for further investigation and cooperation. This chapter ends by briefly pointing out new directions for how QISC can work with AI for NLP and real-time analysis. Understanding QISC in terms of its current market and its positive impact on the future of computational intelligence is the focus of this chapter, which focuses on current market trends. Analyzing key trends and the degree of industry adoption, the research findings provide useful perspectives for academic, practical, and policy purposes. 2026 Scrivener Publishing LLC.
Quantum-Inspired Approaches for Intelligent Data Processing;pp.173-200
https://doi.org/10.1002/9781394336449-008 https://www.scopus.com/pages/publications/105032533536?origin=resultslist
Kapoor S., School of Business and Management, Christ University, Bengaluru, India; Garg V., Symbiosis Institute of Business Management, Noida, Symbiosis International (Deemed University), Pune, India
AI Avatars in Immersive Environments for Communication Skill Training
AI avatar interaction; AI avatars; AI technology; Artificial intelligence; Communication skill training; Virtual reality environments
In the contemporary landscape, communication skills are essential across different spheres, from professional spaces to personal engagements. AI-generated content is gaining popularity in education, which assists students in their assignments and other activities. With the advent and development of technology, AI avatars incorporated with virtual reality environments present a novel and promising pathway for cultivating communication abilities. VR environments are experimenting with AI technology that can create a more realistic environment and provide users with interactive characters around them. The education sector is widely using AI technology for interactive classrooms and presentations, which can improve students' creative communication skills. It is assumed that the AI sector is expected to grow by 48% in the future. The promising potentials of the technology attract more users into the medium as it allows for customization, dynamic assessment patterns, a hybrid learning mode with meaningful interactions, and blended learning experiences. This chapter delves into the potential of AI avatars in virtual reality environments, especially communication skill training applications that help improve communication skills. AI avatars create an interactive space for the users in VR environments that creates an engaged space for the users to be involved in conversations and activities without the fear of being judged. The present study takes an experimental approach with the social learning theory to test whether these AI avatars help users improve communication skills, mainly interpersonal communication skills and public speaking. A mixed methodology is adopted for the research, where data collected from the training and questionnaire data will undergo triangulation to get the appropriate results. The research also addresses ethical considerations like privacy and inclusivity in AI avatar interactions. Overall, the study highlights the effectiveness of incorporating AI avatars in VR environments for improved communication skill development. 2026 Scrivener Publishing LLC. All rights reserved.
Joseph, Twinkle Sara; Kunnumpurath, Biju; Bhojan, Anand; Mahalakshmi, J.
Virtual Reality and Augmented Reality with 6G Communication;pp.291-327
https://doi.org/10.1002/9781394336081.ch12 https://www.scopus.com/pages/publications/105025511450?origin=resultslist
Joseph T.S., Department of Media Studies, Christ University, Bengaluru, Karnataka, India; Kunnumpurath B., Department of Media Studies, Christ University, Bengaluru, Karnataka, India; Bhojan A., Department of Computer Science (School of Computing), National University of Singapore, Singapore; Mahalakshmi J., School of Sciences, Christ University, Bengaluru, Karnataka, India
Enhancing Heritage and Cultural Education through Immersive Audio-Visual Techniques
Augmented reality, 3D soundscapes; Cultural heritage; Immersive media; Interactive learning, 6G technology; Virtual reality
Growth in immersive media technologies offers unique opportunities for improved learning experiences. This is important in understanding and preserving heritage and cultural elements. The establishment of hybrid classrooms and the adoption of technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) make the learning experience more immersive and interactive. These experiences can lessen the differences between theoretical and emotional connections which also help learning to be more impactful and effective. This chapter examines how immersive technologies such as audio-visual technology can change the heritage and cultural education of people across diverse ages. The chapter discussed both 3D soundscapes and visual imagery in relation to VR/AR applications and how well these elements are integrated into a unified whole, which provides some interesting immersive cultural heritage education experiences. This study also analyzes use cases such as virtual museum tours with immersive audio-embedded recreations of historical environments, AR-enhanced archaeological site visits supported by ambient sounds, and interactive cultural heritage simulations guided by spatial audio. It further explores successful research integrating 3D soundscapes into AR/VR use cases. These applications highlight potential VR/AR educational contexts where audio-visual enhancement provides multisensory learning that encourages engagement and cognition surrounding heritage and cultural elements. The study also covers the benefits of this approach like increased public engagement with cultural sites and artifacts, improved retention of historical information, and enhanced accessibility to cultural experiences. Ultra-low latency, higher connectivity, and high-speed data transfer would mean that the quality and accessibility offered by 6G will be on another level altogether. On the other hand, AI can bring immersive experiences along with customization, immediate feedback, and interactions in virtual worlds. The chapter further focuses on the opportunities provided by emerging technologies such as 6G and AI tools in improving understanding of culture/heritage preservation and cultures. The chapter ends with the user applicability of audio-visual enhanced VR/ AR in cultural and heritage education. The study suggests continued research and collaboration between various stakeholders to develop better methods to preserve and disseminate the rich heritage and cultural values. Integrating evolving technologies can be an aid to reducing many of the existing limitations by providing better realistic, interactive, and accessible cultural educational tools. 2026 Scrivener Publishing LLC. All rights reserved.
Paul, Ajith; Kunnumpurath, Biju; Balakrishnan, C.; Ramachandran, Selvakumar; Suseelan, Anitha
Virtual Reality and Augmented Reality with 6G Communication;pp.245-269
https://doi.org/10.1002/9781394336081.ch10 https://www.scopus.com/pages/publications/105025495912?origin=resultslist
Paul A., Department of Media Studies, Christ University, Bengaluru, India; Kunnumpurath B., Department of Media Studies, Christ University, Bengaluru, India; Balakrishnan C., Department of Computer Science, Christ University, Bengaluru, India; Ramachandran S., Department of Continuing Education, University of Oxford, Oxford, Oxfordshire, United Kingdom; Suseelan A., School of Architecture, Christ University, Bengaluru, India
Study of Optimization Techniques in Agriculture
AI advancements; Data exchange; Deep learning; Efficiency; Innovation; Integration; IoT; Transformative change
In agriculture, optimization strategies are essential for raising production, sustainability, and resource efficiency. This abstract explores several agricultural optimization approaches and highlights their importance in contemporary farming operations. Obstacles to traditional agricultural operations include climate change, resource constraint, and shifting consumer preferences. Through the application of cutting-edge technologies and rigorous scientific methods, optimization strategies provide answers to these problems. Precision agriculture, which uses data-driven techniques like remote sensing, Geographic Information System (GIS), and Global Positioning System (GPS) to customize farming procedures to particular field conditions, is one important area of optimization. This helps farmers to maximize yields while minimizing waste and the environmental effect of inputs like water, fertilizer, and pesticides. In addition, optimization methods include selective breeding for genetic advancements and biotechnology, with the goal of creating crops with increased nutritional value, disease resistance, and production potential. Under the category of optimization approaches are integrated pest management solutions, which efficiently control pests and illnesses while using less chemical pesticides by applying ecological principles. In summary, agricultural optimization approaches offer a comprehensive strategy to tackle the issues that contemporary farming faces, encouraging resilience, productivity, and sustainability in food production systems. Given the changing global agricultural dynamics, it is imperative that these strategies be further researched and used in order to ensure environmental stewardship and food security. 2026 Scrivener Publishing LLC. All rights reserved.
Nanjundan, Preethi; Indu, P.V.; Thomas, Lijo
Driving Innovation by Dynamic Optimization: The Challenges of Reshaping Industry;pp.485-508
https://doi.org/10.1002/9781394315727.ch24 https://www.scopus.com/pages/publications/105038505796?origin=resultslist
Nanjundan P., Department of Data Science, CHRIST University, Pune Lavasa Campus, Pune, India; Indu P.V., Department of Data Science, CHRIST University, Pune Lavasa Campus, Pune, India; Thomas L., Department of Data Science, CHRIST University, Pune Lavasa Campus, Pune, India
Integration of Industrial Robots to Enhance Warehouse Efficiency in an Industry 4.0 Environment Using Digital Twin Technology
CODESYS; digital twin; factory IO; industrial robots; Industry 4.0; sequential function chart; sustainable manufacturing; warehouse automation
An automated warehouse serves as a facility designed for the storage of materials with a comprehensive inventory management system. This system meticulously monitors the loading and unloading of materials with minimal human intervention. The core operations within an automated warehouse are executed through the utilization of sophisticated software tools. This specific system falls within the purview of Manufacturing Execution Systems (MES). The integration of industrial robots is a pivotal aspect of warehouse automation, serving to meet the demands of efficiency while simultaneously reducing space requirements and overall operational costs within the warehouse management system. Industrial robots excel in executing processes swiftly and with precision, enhancing overall operational efficiency. This paper outlines the authors approach, which involves the programming of an industrial controller and the utilization of an Industry 4.0 Application Programming Interface (API) to facilitate communication with a 3D warehouse in Factory IO for loading materials. The programming of the industrial controller is carried out using the Sequential Function Chart (SFC) language, adhering to the IEC61131-3 standard. 2026 Scrivener Publishing LLC.
Pounraj, Manikandan; James Periyanayagam, L.; Lobo, Jennifer Pearline; Ashwitha, P.
Integrated Systems: Embedded, Signal Processing, and Communication;pp.1-11
https://doi.org/10.1002/9781394311767.ch1 https://www.scopus.com/pages/publications/105032384312?origin=resultslist
Pounraj M., Centre of Excellence in Automation, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; James Periyanayagam L., Centre of Excellence in Automation, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Lobo J.P., Centre of Excellence in Automation, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Ashwitha P., Centre of Excellence in Automation, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
Green Finance and Sustainability: Landscape of Indian Financial Institutions
Climate risk management; Corporate sustainability; ESG; Green Climate Fund; Green finance; Renewable energy; Sustainability
Today, green finance and sustainability have become integral parts of the global financial landscape, and it is the Indian financial institutions that are leading the way. This chapter delves into the intricate landscape of green finance and sustainability in Indian financial institutions. It focuses on regulation, green bonds, sustainable banking, public and private sector initiatives, and the challenges and opportunities they present. The Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI) are at the forefront of these initiatives. The RBI, for instance, is already taking significant steps, such as incorporating sustainability into its policy framework, by considering renewable energy in its priority sector lending norms. SEBI, conversely, has made it mandatory for all listed companies to disclose their ESG practices under the Business Responsibility and Sustainability Reporting (BRSR) framework, a move that promotes transparency and sustainable practices. Green bonds are a potent financial instrument for directing capital toward environmentally sustainable projects. In India, the Indian Renewable Energy Development Agency and several banks have proactively issued green bonds to finance renewable energy, energy efficiency, and clean transportation projects. The State Bank of Indias issuance of its first green bond in 2018, raising USD 650 million for a green project, is a testament to SBIs dedication to sustainable finance and a model for other financial institutions. Indian banks are increasingly integrating environmental, social, and governance (ESG) criteria, a set of standards for a companys operations that socially conscious investors use to screen potential investments in their lending and investment decisions fostering longterm green banking practices. They also provide green loans for projects with positive environmental impacts and incorporate climate risk assessment into their risk management frameworks driven by regulatory mandates and market demand for responsible investments. Both the public and private sectors in India are actively promoting green finance. The government provides support and technical know-how for fund arrangements for green projects through initiatives like the Green Climate Fund and the International Solar Alliance. Major corporations, including Tata Power and Reliance Industries, also invest heavily in renewable energy sources aligning with corporate sustainability trends. However, green financing in India still faces challenges. For instance, the general public is unaware of green financial products, which hampers their adoption. The limited number of such products and weak regulatory support pose significant hurdles. Despite these challenges, the potential for green finance in India is immense. The increasing recognition of climate change-related risks is driving demand for resilient investments. The progress in renewable energy and efficiency technologies is creating new investment opportunities, and international collaborations can enhance the capacity of Indian financial institutions to support green projects. Finally, the green finance and sustainability landscapes in Indian financial institutions are dynamic and changing rapidly. Regulatory systems, innovative financial instruments, sustainable banking practices, and proactive initiatives from public and private sectors collectively drive green finance growth and support by the countrys strive toward green financing of its transition to a sustainable and resilient economy. 2025 Scrivener Publishing LLC.
Hridhya, P.K.; Desai, Kavitha; Ananthan, Sriram; Manickam, Thirupathi; Devaraja Nayaka, K.; Joel Jebadurai, D.
Financial Innovation for Global Sustainability;pp.359-383
https://doi.org/10.1002/9781394311682.ch15 https://www.scopus.com/pages/publications/105014403033?origin=resultslist
Hridhya P.K., Department of Professional Studies, Christ University, Karnataka, Bangalore, India; Desai K., SVKMs Narsee Monjee Institute of Management Studies, Karnataka, Bangalore, India; Ananthan S., Department of Business Management, Western Community College, Vancouver, BC, Canada; Manickam T., College of Business, University of Buraimi, Al Buraimi Governorate, AI Buraimi, Northern part of Oman, Oman; Devaraja Nayaka K., College of Business, University of Buraimi, Al Buraimi Governorate, AI Buraimi, Northern part of Oman, Oman; Joel Jebadurai D., Department of MBA, St. Josephs College of Engineering, Tamil Nadu, Chennai, India
Customer Acceptance of Sustainable Fintech Innovation: A Study from an Indian Customer Perspective
And customer acceptance; FinTech; Perceived relief; Perceived usefulness; Sustainable development
Financial technology, or FinTech, aims to simplify and enhance financial services. FinTech is frequently referred to as financial technology. The customers day-today financial operations, processes, and existence are all significantly impacted by the financial transactions that they engage in. Although there has been a notable rise in the literacy rate among Indian consumers in the last 20 years, financial literacy seems to still be in its nascent phase. Hence, the research investigates the determinants that impact the adoption of FinTech services, which include a range of societal, ecological, and environmental advantages. This research presents a comprehensive model that expands on the TAM model by including reported relief as a mediator and perceived ease of use as an independent variable. The study also takes into account consumer acceptance as the dependent variable. Through quantitative approaches, a total of 253 responses have been collected from Indian citizens from online chatrooms and financial education sites. A Google Form link has been shared with the students and participants in these online chatrooms. After some time had passed, the quantitative data were evaluated using SmartPLS by employing the approach of partial least squares. The research findings indicate that consumers perception of the usefulness and alleviation provided by sustainable fintech services greatly influence their overall adoption of these services. The research conducted a moderation analysis utilizing the PLS-MGA technique to determine whether generation, Gen Z or Gen Y, had a significant moderating impact and shown more willingness to use FinTech services. Eventually, the research offered practical recommendations for managers to provide suitable, dependable, and enduring services to clients at a fair price that aligns with their needs and eventually enhances their psychological well-being. 2025 Scrivener Publishing LLC.
Yadav, Pinnika Syam; Kiran, P.B. Narendra; Sharma, Abhishek; Vemula, Rajesh; Shrama, Ankitha
Financial Innovation for Global Sustainability;pp.337-357
https://doi.org/10.1002/9781394311682.ch14 https://www.scopus.com/pages/publications/105014412788?origin=resultslist
Yadav P.S., School of Business and Management, Christ University, Bengaluru, India; Kiran P.B.N., School of Business and Management, Christ University, Bengaluru, India; Sharma A., Mittal School of Business, Lovely Professional University, Punjab, Jalandhar, India; Vemula R., School of Business and Management, Christ University, Bengaluru, India; Shrama A., Mittal School of Business, Lovely Professional University, Punjab, Jalandhar, India
Efficient Load Balancing and Resource Allocation in Networked Sensing SystemsAn Algorithmic Study
AI algorithms; energy efficiency; Load balancing; machine learning; performance optimization; resource allocation
In the current environment, data generation and data transmission are increasing exponentially in day-to-day life. These exponentially growing data might create heavy traffic when transmitted between systems. Also, this affects many functionalities like configuration of networked systems, system and routing configuration parameters, load managing factors of network devices, etc. A dynamic traffic control mechanism needs to be adopted with the help of load-balancing algorithms and efficient resource allocation mechanisms to deal with heavy data traffic. Load balancing algorithms in networked sensing systems aim to distribute the workload evenly among sensor nodes to optimize network performance and energy efficiency and prolong the network lifetime. Resource allocation mechanisms in a networked sensing system involve allocating and distributing network resources efficiently, such as energy, bandwidth, processing power, etc., to optimize performance and increase the networks lifetime. To achieve efficient resource allocation with a balanced load, notable works have been done in optimization and machine learning. The work gives a scientific analysis of traditional and Artificial Intelligence algorithms from a centralized and distributed perspective. Researchers can take this analysis forward when deciding on algorithms based on their application and infrastructural needs. 2025 Scrivener Publishing LLC.
Krishnasamy, Lalitha; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Jenefa, J.
https://doi.org/10.1002/9781394310890.ch6 https://www.scopus.com/pages/publications/85219153943?origin=resultslist
Krishnasamy L., Department of Artificial Intelligence & Data Science, Nandha Engineering College, Tamil Nadu, Erode, India; Vetriveeran D., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Jenefa J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India
Artificial Intelligence and Machine Learning Applications in Threat Detection
Artificial intelligence (AI); Banking; Education; Finance; Healthcare; Machine learning (ML); Self-driving cars; Threat detection
Artificial intelligence (AI) and machine mastering (ML) have emerged as progressive breakthroughs in medical prognosis, especially in the area of early most cancers diagnosis. These trendy gadgets significantly enhance the accuracy and performance of tumor detection. By analyzing huge records units, synthetic intelligence structures can identify small styles and anomalies in medical imaging, which lets in malignant tumors to be recognized earlier and greater appropriately than traditional methods. AI and ML are needed to accelerate tumor analysis and category in the context of most cancers remedy. These technologies assist doctors quickly perceive any abnormalities in radiological pictures inclusive of X-rays, MRIs and CT scans. Artificial intelligence reduces the workload of clinical specialists through automating this manner, enabling faster and greater correct analysis. In addition, AI can assist tailor remedy packages contemplating a patient's specific records, maximizing treatments and predicting remedy outcomes. In addition to improving the performance and speed of diagnosis, the aggregate of AI and ML in tumor detection suggests the capability to boom the general effectiveness of cancer treatment and in the end enhance affected person consequences. 2025 Scrivener Publishing LLC.
Indu, P.V.; Nanjundan, Preethi; Thomas, Lijo
Protecting and Mitigating Against Cyber Threats: Deploying Artificial Intelligence and Machine Learning;pp.41-56
https://doi.org/10.1002/9781394305216.ch3 https://www.scopus.com/pages/publications/105015729511?origin=resultslist
Indu P.V., Department of Economics and Analytics, Christ University, Pune, India; Nanjundan P., Department of Data Science, Christ University, Pune, India; Thomas L., Department of Psychology, Christ University, Pune, India
Blockchain for Enhancing Security and Privacy in the Smart Healthcare
Blockchain; privacy; security; smart healthcare
The rapid digitalization of healthcare systems and the growing integration of smart technologies have made robust security and patient privacy protection critical concerns. Traditional healthcare systems struggle with maintaining the confidentiality, integrity, and accessibility of sensitive patient data. This section explores how blockchain technology can serve as a transformative solution to address these challenges and elevate security and privacy standards in smart healthcare environments. Blockchain, a decentralized and tamper-resistant distributed ledger technology, offers a novel approach to secure data sharing and storage. In the realm of smart healthcare, blockchain can establish a transparent and immutable record of patient information, medical history, and treatment plans, ensuring data integrity by thwarting unauthorized modifications and tampering. The cryptographic features inherent in blockchain provide a strong basis for safeguarding patient privacy. Smart contracts, which are programmable and self-executing scripts on the blockchain, enable precise access control, permitting only authorized entities to access specific patient data. Additionally, the utilization of decentralized identifiers (DIDs) and verifiable credentials enhances patient identity management, mitigating the risks of identity theft and unauthorized access. This section also delves into case studies and ongoing initiatives leveraging blockchain in smart healthcare applications. From electronic health records (EHRs) and interoperability to medical supply chain management, the adoption of blockchain technology showcases promising outcomes in enhancing security, transparency, and privacy. Various challenges and potential drawbacks of implementing blockchain in healthcare are also explored in this section. In conclusion, the integration of blockchain technology in smart healthcare holds significant potential for transforming data security and privacy practices. As the healthcare sector embraces digital evolution, understanding the implications and advantages of blockchain becomes crucial for constructing resilient and patient-centric healthcare ecosystems. 2024 Scrivener Publishing LLC.
Digital Twin and Blockchain for Smart Cities;pp.343-370
https://doi.org/10.1002/9781394303564.ch16 https://www.scopus.com/pages/publications/85207576904?origin=resultslist
Tyagi A.K., Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, Delhi, India; Seranmadevi R., Department of Professional Studies, Christ University, Karnataka, Bangalore, India
Study of State-of-the-Art Performance Metrics in NLP: Specifically for Text Summarization in the Medical Domain Using the SumPubMed Dataset
BERT scores; Metrics; NLP; SUPERT; Text summarization
Text summarization is becoming very important given the number of documents produced each year across domains. In this paper we explore the various traditional metrics for text summarization, such as ROUGE, BLEU, METEOR, etc., and look at improving the performance of the existing metric by taking the stateof-the-art untrained metric SUPERT, and clubbing it with a readability score and a penalty for long summaries. The SUMPUBMED dataset was used for this research and a BERT extractive summarizer was used for generating the summaries. It was found that using a readability score with an unsupervised metric such as SUPERT helped in assessing the quality of the summary more accurately than earlier metrics. We compared the metrics such as SUPERT scores and BERT scores with and without involving the human annotated summaries in the SUMPUBMED dataset and found that untrained metrics perform better than when involving a reference annotated summary. 2025 Scrivener Publishing LLC.
Sridhar, M.; Irfan, Mohammad; Kathirmani, Sukumar
Generative AI: Disruptive Technologies for Innovative Applications;pp.91-106
https://doi.org/10.1002/9781394302932.ch5 https://www.scopus.com/pages/publications/105012674558?origin=resultslist
Sridhar M., NSB Academy, Business School, Bangalore, India; Irfan M., Christ University, Bangalore, India; Kathirmani S., Quelit Innovations Pvt Ltd, Chennai, India