A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
artificial intelligence; comparative analysis; deep learning; early detection; ensemble learning; lung cancer; Machine learning
Lung cancer is among the most fatal types of cancer, accounting for millions of fatalities globally. The capacity for its early detection has the potential to greatly enhance the outcome of treatments, and in recent times, machine learning (ML) and deep learning (DL) algorithms have emerged as mighty resources in aiding radiologists and doctors. This article describes a comparison study of research articles in which they employed different ML and DL models in lung cancer detection on a wide range of datasets. The analysis establishes that the variety, quality, and source of the dataset are central to determining how reliable reported model performance is. Reproducibility has been made possible with public datasets such as LIDC-IDRI, NSCLC, and Kaggle datasets, whereas private clinical datasets typically lead to improved accuracy since they consist of high-quality curated annotations. Subsequent research has shown that DL models, especially state-of-the-art architectures such as convolutional neural networks (CNNs) and EfficientNet-B3, are well-suited to image classification tasks and consistently outperform classical ML models when large, well-balanced datasets are available. Hybrid approaches that blend CNN-based feature learning with traditional classifiers like support vector machines have also proven highly promising, particularly when applied to overcome challenges such as small sample sizes and noisy images. Directions for future work point toward the integration of standardized, multicenter datasets, explainable AI models, and multimodal learning techniques to reach reliable in-clinic deployment. 2026 Elsevier Inc. All rights reserved.
Angeline, A.; Margaret Savitha, P.; Sahayam, Norra
Explainable AI in Clinical Practice: Methods, Applications, and Implementation;pp.45-67
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-44111-0.00001-9 https://www.scopus.com/pages/publications/105036918135?origin=resultslist
Angeline A., Christ University, Karnataka, Bangalore, India; Margaret Savitha P., Christ University, Karnataka, Bangalore, India; Sahayam N., Data Solution Architect DXC Technologies, United Kingdom
Chatbots in health care: AI-based personalization and EHR integration in patientdoctor communication
AI-driven chatbots; Digital health; Electronic health records; Healthcare communication; Patient satisfaction; Personalization
The artificial intelligence (AI)-driven chatbots in healthcare integration revolutionizes patientprovider interactions for real-time support, communication streamline, and patient engagement. These chatbots connected to natural language processing (NLP) and machine learning provide medical queries resolution, chronic condition management, and scheduling appointments. Despite the advancements, there are gaps remain in the chatbot personalization interactions and Electronic Health Records (EHR) seamless integration. Personalization is crucial for satisfied patient and medical advice. EHR integration enables context-aware responses, error reduction, and better healthcare outcomes. This study effectiveness fosters the evaluation of AI-driven chatbots in healthcare communication personalization and potential benefits examination and EHR integration challenges. Using a mixed-methods approach includes sentiment analysis for patient satisfaction sentiments understanding, thematic analysis for key themes and findings from Patient Message, regression analysis for personalization, EHR integration, and patient outcomes understanding, and structural equation modeling (SEM) highlights the personalization and EHR integration impact on patient satisfaction, engagement, and trust in chatbot technology. The findings reinforcing the healthcare providers need to adopt AI-driven solutions and personalized communication priorities and seamless data integration for patient experience improvement and overall healthcare efficiency. 2026 Elsevier Inc. All rights reserved.
Kuwar, Vishakha; Kumari, Puja; Upreti, Kamal; Gupta, Komal; Shankar, Uma; Bhide, Neeta
Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction;pp.229-242
https://doi.org/10.1016/B978-0-443-41660-6.00018-1 https://www.scopus.com/pages/publications/105023938726?origin=resultslist
Kuwar V., Centre for Online Learning, Dr. D Y Patil Vidyapeeth, Maharashtra, Pune, India; Kumari P., Department of Psychology and Mental Health, Gautam Buddha University, Uttar Pradesh, Greater Noida, India; Upreti K., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Gupta K., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Shankar U., Ramcharan School of Leadership, Dr. Vishwanath Karad MIT World Peace University, Maharashtra, Pune, India; Bhide N., MGM University, Maharashtra, Aurangabad, India
Humancomputer interaction for cognitive, emotional and learning well-being
Adaptive learning; AI-Driven systems; Cognitive well-being; Emotion-aware technology; Emotional well-being; Humancomputer interaction (HCI)
HumanComputer Interaction (HCI) has revolutionized the way humans engage with technology, shaping cognitive, emotional, and learning experiences. This chapter explores HCI's impact on well-being, focusing on cognitive load reduction, emotional stability, and adaptive learning. HCI technologies such as AI-driven decision support, emotion-aware systems, and personalized education platforms enhance user engagement by fostering efficiency and well-being. Cognitive well-being benefits from AI-powered cognitive tools that improve memory, decision-making, and mental agility. Emotional well-being is facilitated by affect-sensitive systems, digital therapeutics, and social HCI that reduce stress and increase emotional engagement. Adaptive learning systems, gamification, and assistive technologies also ensure inclusive education by making learning more personalized and accessible to special needs students. Future HCI trends involve neuroadaptive interfaces, wearable-integrated health technology, and AI-based mental health solutions that all improve personalization and user experience. Yet ethical issues, such as data privacy, digital addiction, and algorithmic biases, need to be addressed in order to maintain responsible technology utilization. Achieving balance between digital and physical interactions is important in the preservation of general well-being. The future of HCI, where it becomes empathetic, adaptive, and ethical interfaces, is a future that sees technology enhance not just efficiency but cognitive, emotional, and learning well-being. By making ethical AI design and user-centric experiences a priority, HCI will continue to unleash human potential and develop durable, well-being-oriented technological solutions. 2026 Elsevier Inc. All rights reserved.
Upreti, Kamal; Kumari, Puja; Shankar, Uma; Radhakrishnan, G.; Udhaya, S.K.; Malik, Khushboo
Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction;pp.43-65
https://doi.org/10.1016/B978-0-443-41660-6.00008-9 https://www.scopus.com/pages/publications/105023946368?origin=resultslist
Upreti K., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Kumari P., Department of Psychology and Mental Health, Gautam Buddha University, Uttar Pradesh, Greater Noida, India; Shankar U., Ramcharan School of Leadership, Dr. Vishwanath Karad MIT World Peace University, Maharashtra, Pune, India; Radhakrishnan G., KIIT, Kalinga School of Management, Odisha, Bhubaneswar, India; Udhaya S.K., Department of Computer Science and Engineering, Easwari Engineering College, Tamil Nadu, Chennai, India; Malik K., School of Law, Christ University, Delhi NCR, India
Transforming sectors: VR and AR applications in healthcare, education, and social roboticsA case study
Cognitive psychology; Human-computer interaction; Learning; Medical education; Robotics
Virtual reality (VR) and augmented reality (AR) are transforming multiple fields, including healthcare, education, social robotics, and mental health. In medicine, they enhance surgical precision, medical training, rehabilitation, and remote consultations by providing immersive and real-time data-driven environments. Education benefits from interactive and skill-based learning, making complex subjects more engaging and accessible. In social robotics, AR/VR improves human-robot interactions, emotional intelligence in AI assistants, and robotic telepresence. Mental health applications include VR-based exposure therapy, stress management, and PTSD treatment through controlled, immersive simulations. Industrial training leverages VR for hazardous work simulations and AR for real-time assistance, increasing workplace safety and efficiency. In addition, AR supports precision medicine by overlaying real-time visuals during surgeries for improved accuracy. The integration of AI, haptic feedback, and 5G is expected to further advance these technologies. However, ethical concerns regarding data privacy and prolonged VR exposure require careful regulation. Overall, VR and AR continue to revolutionize various sectors, enhancing engagement, efficiency, and personalization in professional and therapeutic settings. 2026 Elsevier Inc. All rights reserved.
Radhakrishnan, G.; Upreti, Shitiz; Multani, Mohd Danish; Maheshwari, Rishit; Gupta, Komal; Tiwari, Akhilesh
Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction;pp.343-367
https://doi.org/10.1016/B978-0-443-41660-6.00007-7 https://www.scopus.com/pages/publications/105023928883?origin=resultslist
Radhakrishnan G., KIIT, Kalinga School of Management, Odisha, Bhubaneswar, India; Upreti S., Maharishi Markandeshwar (Deemed to be University), Haryana, Mullana-Ambala, India, Department of Management, Haryana, India; Multani M.D., UST Global Solutions, Haryana, India; Maheshwari R., Pandit Deendayal Energy University, Gujarat, Gandhinagar, India, Department of Computer Science & Engineering, Gujarat, Gandhinagar, India; Gupta K., Accenture, Banglore, Karnataka, Bangalore, India; Tiwari A., School of Business and Management, Christ (Deemed to be University) Delhi NCR, Uttar Pradesh, Ghaziabad, India
Data-driven education: Leveraging big data, AI, and machine learning for smarter learning environments
Cognitive psychology; Computer in education; Human-computer interaction; Knowledge management; Multimedia information system; Operations management
Big Data, artificial intelligence (AI), and machine learning are transforming education with self-paced learning, precise insights, and automated decisions. The effects these technologies have on education are transformative, especially when it comes to improving student achievement, advancing administrative operations, and personalizing the learning experience, as stated in this chapter. Big Data collects and analyzes immense volumes of data, while AI powers automation, makes predictions, automates evaluations, and enables the possibility of adaptive learning. Chatbots, recommendation engines, and tutoring systems enhance student-focused digital education. Still, integrating these technologies comes with challenges such as privacy and ethical issues, algorithm discrimination, and data security concerns. Some of the new trends are explainable AI (XAI) for ethical decision-making, blockchain and federated learning for privacy-preserving analytic systems, and verifiable credentials. In addition, XR, AI-based virtual laboratories, and neurosymbolic AI will have great consequences on the future learning environment. AI in education offers scalability and inclusivity but demands ethical regulation, governance, and resource management. This chapter recommends sustained effort in research, policy changes, and ethical integration of AI for the best possible use thereof. The education sector, by incorporating human-centered AI approaches, can create a just, sustainable future of learning that is accessible to all students around the world. 2026 Elsevier Inc. All rights reserved.
Jain, Rituraj; Upreti, Kamal; Shankar, Uma; Radhakrishnan, G.; Sharma, Ashish; Malik, Khushboo
Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction;pp.203-226
https://doi.org/10.1016/B978-0-443-41660-6.00003-X https://www.scopus.com/pages/publications/105023943238?origin=resultslist
Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Upreti K., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Shankar U., Ramcharan School of Leadership, Dr. Vishwanath Karad MIT World Peace University, Maharashtra, Pune, India; Radhakrishnan G., KIIT, Kalinga School of Management, Odisha, Bhubaneswar, India; Sharma A., Department of Technology, JIET Universe, Rajasthan, Jodhpur, India; Malik K., School of Law, Christ University, Uttar Pradesh, Ghaziabad, India
Harnessing quorum sensing for disease management
chemical signals; disease management; Gram-negative bacteria; Gram-positive bacteria; Quorum sensing
Quorum sensing (QS) is a bacterial communication system that allows bacteria to coordinate their behavior based on their population size. Both Gram-positive and Gram-negative bacteria use QS, but they employ different chemical signals to communicate. As bacteria grow, they release these signals, and when the attention of these signals reaches a certain position, it triggers specific genes that help the bacteria acclimate to their terrain. 2026 Elsevier Inc. All rights reserved.
Desai, Vinayak; M, Sahana; Mayegowda, Shilpa Borehalli; Nagalapur Gadilingappa, Manjula; Nagshetty, Kavita
Harnessing Quorum Quenching for Disease Management;pp.137-151
https://doi.org/10.1016/B978-0-443-34178-6.00015-4 https://www.scopus.com/pages/publications/105023941542?origin=resultslist
Desai V., Department of Microbiology, Gulbarga University, Karnataka, Gulbarga, India; M S., Department of Microbiology, School of Basic and Applied Sciences, Dayananda Sagar University, Karnataka, Bengaluru, India; Mayegowda S.B., Department of Psychology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, India; Nagalapur Gadilingappa M., Department of Microbiology, School of Basic and Applied Sciences, Dayananda Sagar University, Karnataka, Bengaluru, India; Nagshetty K., Department of Microbiology, Gulbarga University, Karnataka, Gulbarga, India
Quorum quenching for biocontrol of plant diseases
bacterial pathogens; biocontrol; plant diseases; Quorum quenching; quorum sensing
Bacterial pathogens have become a significant threat to world agriculture, causing tremendous crop losses and economic instability. The traditional mode of disease control by chemical pesticides is quickly becoming obsolete with resistant strains and environmental issues. Quorum quenching (QQ) is thus a promising alternative that is sustainable enough to target the bacterial communication system known as quorum sensing (QS). QS enables bacteria to coordinate their virulence factors, biofilm formation, and host colonization according to population density. Attenuating bacterial virulence is achieved through QQ by enzymatic degradation, signal molecule mimicry, or inactivation of autoinducers but without directly killing the pathogens, thereby not allowing resistance. This chapter describes the mechanism of QQ and several applications of this principle in controlling plant diseases through biocontrol agents. Enzymatic hydrolysis catalyzed by lactonases, acylases, and oxidoreductases inactivate autoinducers that include acyl-homoserine lactones and autoinducer-2 (AI-2). Mimicry of QS signal molecules is through synthetic or natural analogs that competitively inhibit QS receptors; sequestration occurs when autoinducers are captured to avert their involvement in QS. QQ is quite efficient in the lab and the field, both as microbial biocontrol agents against the pathogens Pectobacterium carotovorum and Xanthomonas campestris, as well as in transgenic plants expressing QQ enzymes. QQ still faces challenges in developing resistance, delivering QQ enzymes, and integrating with integrated pest management strategies. Future research could be concentrated on optimizing QQ strategies, designing new enzymes with better stability and specificity, and assessing the environmental impact of QQ-based biocontrol. Breakthroughs in synthetic biology and protein engineering would give way to new opportunities in the form of more potent biocontrol agents. The method of disease control in an environment-friendly manner and ensuring food security across the globe will be targeted by overcoming the challenges and through novel research with QQ-based biocontrol. 2026 Elsevier Inc. All rights reserved.
Jadhav, Vikram; Pardeshi, Shaktising; More, Bhagyashri; Merugu, Ramchander; Mayegowda, Shilpa Borehalli
Harnessing Quorum Quenching for Disease Management;pp.201-224
https://doi.org/10.1016/B978-0-443-34178-6.00007-5 https://www.scopus.com/pages/publications/105023921004?origin=resultslist
Jadhav V., Department of Chemistry, K. K. Wagh Arts. Science and Commerce College Pimpalgaon (B), Maharashtra, Nashik, India; Pardeshi S., Karmaveer Punjababa Goverdhane Arts, Commerce, and Science College Igatpuri, Maharashtra, Nashik, India; More B., Camp Education Society's, Dr. Arvind B. Telang Senior College Arts, Commerce and Science, Pradhikaran Nigdi, Maharashtra, Pune, India; Merugu R., Department of Biochemistry, Mahatma Gandhi University, Telengana, Nalgonda, India; Mayegowda S.B., Department of Psychology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, India
Artificial intelligence-powered talent acquisition and onboarding
AI-driven talent acquisition; automation; data-driven insights; Large language models; OpenAI; predictive analysis; summarization
Artificial intelligence (AI)-powered instantaneous recruitment analysis and onboarding of the candidates has gained more popularity in recent years, mainly after the COVID-19 phase, largely due to the incredible growth of data-driven content. Basically, the art of summarizing the entire content of the resume of the candidates and leveraging the resumes that are relevant to the job description takes more time-consuming by using the traditional methods. AI is revolutionizing the landscape of talent acquisition and onboarding for applicants/selected candidates, making it much easier than conventional techniques. By harnessing the power of advanced AI-based algorithms and machine learning, organizations and multinational corporations (MNCs) can streamline their recruitment processes, from identifying top talent to integrating new hires seamlessly. AI-based tools automate time-consuming tasks such as screening of the resumes of the voluminous data, scheduling interviews based on each individual job role, and sending out offer letters for the selected applicants. This automation frees up human resource professionals to focus on strategic initiatives and build stronger relationships with candidates. AI algorithms can analyze vast amounts of data to identify candidates who possess the exact skills and qualifications required for a specific role. This precision reduces the risk of mismatches and ensures a higher quality of hires. This article specifically focuses on the tools and techniques based on AI for making the recruitment process easier and their impacts on the actual landscape, along with some recent case studies. The impact of AI on this field of recruitment and its significance on the job market is well analyzed in this article. By embracing AI, organizations can unlock new opportunities for growth and innovation. AI-driven talent acquisition and onboarding not only improve efficiency and accuracy but also enhance the overall candidate experience and foster a more diverse and inclusive workforce. 2026 Elsevier Inc. All rights reserved.
C., Balakrishnan; J., Mahalakshmi; R., Stephen; Warrier, Gayathry S.
Harnessing Artificial Intelligence to Ensure Diverse Global Teams;pp.89-104
https://doi.org/10.1016/B978-0-443-34135-9.00003-9 https://www.scopus.com/pages/publications/105033410452?origin=resultslist
C. B., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; J. M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; R. S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Warrier G.S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
Transforming potential into performance: harnessing AI-driven competency frameworks to nurture talent in higher education
artificial intelligence; automation; Higher education system; Learning; talent management
The Indian Higher Education System is the backbone for economic expansion, creativity and innovation resulting in the progress of the nation. With a huge network of universities, colleges, and institutes, India's higher education system is one of the world's largest, serving millions of students. Despite its rapid growth the Higher Education System in India is witnessing issues like lower research output in comparison with global benchmarks, shortage of qualified academic staff and inadequate quality. Considering the available store house of literature and need gap analysis this paper aims to investigate how competence frameworks powered by artificial intelligence might help to turn promise into performance, to examine the changing talent needs in Higher Education by looking at how the demands of academia, industry, and research are always changing and also to investigate AI's function in Competency-Based Talent Management. Based on the discussions of multiple spectrums namely, Academic excellence, Students employability and Industry readiness, Research Innovation, Teaching effectiveness, Global and National Rankings and Faculty Satisfaction, the paper has suggested the implementation of AI to all pervasively enable the academic Institutions to grow, diversify and enrich the talent acquisition and retention. The paper also focussed on the significance of the inclusion of AI driven automation process to tide over the mundane difficulties of administrative work, to enrich the collaborative research initiatives, to benefit the teaching community to concentrate on modern andragogies, to upscale the Global and National Rankings, to gain academic excellence, to strengthen the employability skills among the students and ultimately to promote the overall satisfaction of the educators and educates. The paper concluded that well trained Higher Education Institutions can optimize their workload judiciously by incorporating AI tools and in turn can contribute to a better learning environment. 2026 Elsevier Inc. All rights reserved.
Hukkeri, Sheetal V.; Kavatekar, Smita; Ashraf, Sharmila; Donni, Sacheth Kiran; Ghosh, Nabanita
Harnessing Artificial Intelligence to Ensure Diverse Global Teams;pp.317-329
https://doi.org/10.1016/B978-0-443-34135-9.00001-5 https://www.scopus.com/pages/publications/105033440281?origin=resultslist
Hukkeri S.V., School of Commerce, JAIN (Deemed-to-be University), Karnataka, Bengaluru, India; Kavatekar S., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bengaluru, India; Ashraf S., School of Commerce, JAIN (Deemed-to-be University), Karnataka, Bengaluru, India; Donni S.K., School of Psychological Sciences, Christ University, Karnataka, Bengaluru, India; Ghosh N., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bengaluru, India
Collaborative security approaches for IoT ecosystems
Collaborative architectures; IoT security; Proactive threat hunting; Resilient IoT ecosystems; Scalable IoT design
The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved..
Thampan, Manju; Joy, Rojal Treesa; Ramadani, Lauresha; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.291-304
https://doi.org/10.1016/B978-0-443-34125-0.00026-X https://www.scopus.com/pages/publications/105027006351?origin=resultslist
Thampan M., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Joy R.T., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Identity and access management for IOT devices
Federated identity management; IoT security; Machine learning authentication; Privacy-preserving techniques; Risk-based access control; Scalable IAM solutions
Identity and Access Management (IAM) for Internet of Things (IoT) devices is crucial in safeguarding the security and integrity of interconnected systems. This critical analysis explores the unique challenges and solutions associated with IAM in the context of IoT environments. IoT devices, characterized by their diverse types and widespread deployment, present significant complexities in managing identities and controlling access. Traditional IAM frameworks often fall short when applied to the dynamic and distributed nature of IoT networks, where devices frequently interact autonomously and may lack standardization. The analysis identifies key issues such as scalability, interoperability, and real-time authentication. It highlights the need for advanced IAM solutions that can handle the vast number of devices, support diverse protocols, and ensure robust security measures. Emerging technologies, including blockchain and AI-driven authentication, offer promising avenues for enhancing IAM in IoT contexts. Blockchain can provide decentralized, tamper-proof identity management, while AI can enhance real-time threat detection and adaptive access control. The review underscores the importance of developing IAM frameworks that are both flexible and scalable to address the evolving security requirements of IoT environments. By addressing these challenges, organizations can better secure their IoT infrastructure and mitigate risks associated with unauthorized access and identity breaches. 2026 Elsevier Inc. All rights reserved..
Jimmy, Mathew Jyyothis; John, Naveen; Sreenath, V.; Etemi, Blerta Prevalla; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.137-151
https://doi.org/10.1016/B978-0-443-34125-0.00020-9 https://www.scopus.com/pages/publications/105027023704?origin=resultslist
Jimmy M.J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; John N., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Sreenath V., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Etemi B.P., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology - RIT Global Campus, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
The role of IoT in optimizing quick commerce operations: a comprehensive analysis of micro-fulfillment centers and consumer satisfaction
Artificial intelligence; Computer in society; Computer science; Computing methodology; Consumer decisions; Database management system; Fast delivery; Geomatics; Information systems; Internet of Things; Machine learning; Micro-fulfillment centers; Operations management; Product assortment; Product demand; Product quality; Quick commerce
This research focuses on integrating Internet of Things (IoT) and AI-driven machine learning algorithms to optimize operations within micro-fulfillment centers for quick commerce in India. The study addresses the growing demand for faster delivery times, driven by the rise of e-commerce, and the challenges associated with managing inventory, logistics, and last-mile delivery. By implementing IoT-enabled devices to monitor real-time data and deploying machine learning algorithms for demand forecasting and route optimization, the study aimed to enhance operational efficiency and reduce delivery times. The research utilized a dataset comprising various operational metrics from micro-fulfillment centers across major Indian cities. A comparative analysis revealed that the AI and IoT integration led to a 22% reduction in delivery time and a 15% improvement in order accuracy. Additionally, the predictive maintenance system, powered by IoT sensors, resulted in a 30% decrease in equipment downtime. The results demonstrate that combining IoT with machine learning optimizes supply chain operation and significantly contributes to meeting consumer expectations in the quick commerce sector. This research provides a comprehensive framework for leveraging technology to address the unique challenges of the Indian market, offering valuable insights for industry stakeholders. 2026 Elsevier Inc. All rights reserved..
Muloor, Kiran Hemanthaj; Shankar Iyer, Lakshmi; Saseekala, M.; Manu, K.S.
IoT Security: Fundamentals and Key Enabling Technologies;pp.473-487
https://doi.org/10.1016/B978-0-443-34125-0.00014-3 https://www.scopus.com/pages/publications/105026989292?origin=resultslist
Muloor K.H., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Shankar Iyer L., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Saseekala M., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Manu K.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Emerging trends and security in IoT devices
AI integration; Anomaly detection; Blockchain; Connectivity; Data privacy; Device authentication; Edge computing; Emerging trends; IoT; Secure communication; Security
The Internet of Things has rapidly evolved, connecting billions of devices and transforming various industries. This chapter explores the latest emerging trends in IoT devices, focusing on advancements in connectivity, edge computing, and artificial intelligence (AI) integration. As IoT adoption grows, so does the importance of security, given the increasing vulnerabilities and potential threats. This chapter delves into key security challenges, including data privacy, secure communication protocols, and device authentication. It also discusses cutting-edge security solutions, such as blockchain technology and AI-driven anomaly detection, to safeguard IoT ecosystems. By examining both technological advancements and security considerations, this chapter provides a comprehensive overview of the current landscape and future directions for IoT devices. 2026 Elsevier Inc. All rights reserved..
Santhosh Kumar, P.; Etemi, Blerta Prevalla; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.275-290
https://doi.org/10.1016/B978-0-443-34125-0.00012-X https://www.scopus.com/pages/publications/105027071557?origin=resultslist
Santhosh Kumar P., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Etemi B.P., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology-RIT Global Campus, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Modern privacy preserving strategies for IoT security
Computer security; Computer security and privacy; Cryptography; Information systems; Network (computer science); Operations management
The proliferation of Internet of Things (IoT) devices has brought about a revolution in various industries and everyday life, enhancing connectivity and efficiency. Nevertheless, this rapid adoption has also given rise to notable security and privacy challenges, leading to the need for robust solutions to safeguard sensitive data. This study delves into contemporary strategies for preserving privacy specifically designed for IoT security, with a focus on the most recent trends and technologies. By the year 2024, it is projected that the global count of IoT devices will exceed 30 billion, exhibiting a compound annual growth rate (CAGR) of 26.7% from 2021 to 2024. This exponential growth has led to a significant increase in the amount of data produced and transmitted by IoT devices, thereby creating fresh opportunities as well as vulnerabilities. Privacy apprehensions are crucial, given that these devices frequently amass sensitive personal and organizational data. The research scrutinizes cutting-edge privacy-preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, which present promising resolutions for safeguarding data while upholding functionality. Federated learning has garnered attention as a decentralized approach that permits data processing to occur locally on devices rather than being sent to central servers, thereby reducing data exposure. Homomorphic encryption facilitates data processing while encrypted, ensuring a high level of security without disclosing the underlying information. Conversely, differential privacy introduces statistical noise to data, guaranteeing that individual data points are not easily discernible, thus preserving user privacy. This section also accentuates the significance of adhering to regulations and the impact of frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on shaping the advancement and acceptance of privacy-preserving technologies. Moreover, an exploration is made into the incorporation of blockchain for immutable and transparent data management in IoT environments. This manuscript furnishes an exhaustive overview of the prevalent trends and technologies within this realm, providing insights into the future trajectory of IoT security and privacy. 2026 Elsevier Inc. All rights reserved..
Dash, Adarsh Kumar; Sehgal, Chiranjeev; Dey, Hemangshu; Ramadani, Lauresha; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.125-136
https://doi.org/10.1016/B978-0-443-34125-0.00003-9 https://www.scopus.com/pages/publications/105026966910?origin=resultslist
Dash A.K., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Sehgal C., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Dey H., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Human factors and social engineering in IoT attacks
AI integration; Human factors; IoT security; Social engineering; Threat detection; User education
This chapter explores how social engineering and human factors contribute to IoT assaults, emphasizing how human mistake and psychological manipulation weaken linked systems. Cybercriminals are increasingly using social engineering techniques to trick people into disclosing private information or jeopardizing security measures as IoT devices become more and more integrated into everyday life and vital infrastructure. This study examines important strategies like baiting, pretexting, and phishing, highlighting their unique uses in Internet of Things settings. It also looks into how human behavior affects security procedures, showing how dangers are increased by insufficient awareness and training. The chapter offers important insights into how human factors and IoT security interact by examining current case studies and actual attack scenarios. It ends with tactical suggestions for enhancing security awareness, fortifying authentication procedures, and putting in place efficient defenses against social engineering risks in IoT ecosystems. Future research should focus on developing AI-driven security measures, flexible defense strategies, and strong policy frameworks to strengthen IoT security. Tackling these human-related vulnerabilities is essential to building a safer and more reliable IoT ecosystem. 2026 Elsevier Inc. All rights reserved..
Baby, Annmary; Thomas, Rithin Paul; Ramadani, Lauresha; Rajesh Kanna, R.
IoT Security: Fundamentals and Key Enabling Technologies;pp.93-104
https://doi.org/10.1016/B978-0-443-34125-0.00002-7 https://www.scopus.com/pages/publications/105027093334?origin=resultslist
Baby A., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Thomas R.P., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Bangalore; Ferrock; Hempcrete; Smart city; Sustainable development; Urban planning
This chapter explores the potential of green structures and nature-based solutions in the context of Bangalore, a city that is rapidly urbanizing and struggling with the environmental impacts of traditional building materials such as concrete. Concrete, while being a mainstay of modern construction, contributes significantly to global CO2 emissions (Fischetti etal., 2023). The problem is how we can maintain Bangalore's rapid growth without concrete's ill effects. What are the alternatives to concrete, how do the different materials compare with each other, and how will it affect Bangalore? In Bangalore, using materials like clay and mud blocks instead of bricks, simple Kota tiles and clay tiles for flooring, or Hempcrete would have a significant environmental benefit. This chapter also discusses nature solutions, which are nature-based strategies for creating sustainable and climate-resilient solutions to address infrastructure needs for Bangalore. These solutions often offer higher quality, lower cost, and more excellent resiliency than traditional infrastructure. Composition and Properties of Hempcrete is a biocomposite building material that combines the internal woody core of the industrial hemp plant with a lime-based binder. The result is a lightweight, insulating, and flame-retardant material with excellent thermal properties. The porous cellulosic structure of the hemp core provides exceptional bonding strength with lime, making foam concrete a versatile material that can be used in both new construction and renovation of existing buildings. One of the main thermal properties and advantages is its high thermal mass. This means they can store heat and keep buildings warmer in the winter and cooler in the summer (Hemp). This property reduces the need for energy-intensive heating and cooling systems, making buildings made from Hempcrete more energy-efficient and cost-effective in the long run. 2026 Elsevier Inc. All rights reserved.
Rai, Rashmi; Rajesh, Roshan; Agarwal, Kushagra; Srivastava, Anubha
Future Smart Cities: A Blueprint for Inclusive and Sustainable Living;pp.143-165
https://doi.org/10.1016/B978-0-443-33667-6.00011-2 https://www.scopus.com/pages/publications/105032981778?origin=resultslist
Rai R., Christ University, School of Business and Management, Karnataka, Bangalore, India; Rajesh R., Christ University, School of Business and Management, Karnataka, Bangalore, India; Agarwal K., Christ University, School of Business and Management, Karnataka, Bangalore, India; Srivastava A., Christ University, School of Business and Management, Karnataka, Bangalore, India
Impact of fine-tuning large language model in society: a comprehensive study
artificial intelligence; computing; information management; information retrieval; knowledge management; Machine learning; natural language processing; transformers
The fine-tuned large language models (LLMs) have revolutionized artificial intelligence (AI) and natural language processing (NLP) with key innovations in neural architectures, particularly with the transformer. The recent advancements of LLM have witnessed that models like bidirectional encoder representations from transformer (BERT), generative pretrained transformer (GPT)-2, GPT-3, and text-to-text transfer transformer (T5) show outstanding performance in understanding and generating human-like text at scale. Researchers use fine-tuned models to excel in their responses to specific tasks or domains. The purpose of fine-tuning the LLM models is to improve the performance of LLM in special fields such as education, research, literature summarization, contract analysis, and creative content generation. Fine-tuning LLM models also has issues like amplifying biases, ethical issues, and regulatory implications, remarkably when LLMs are fine-tuned for emerging domains that may hold harmful stereotypes or misinformation. Fine-tuned LLMs also provide substantial societal benefits, including expert-level knowledge to underserved regions and personalizing educational resources for self-directed learning. The study also discusses the technical aspects of fine-tuning LLMs by examining how general-purpose models are transformed into efficient models. The impact on society and the need for a framework that can shape the deployment of models, with ethical guardrails, transparency, and public engagement to ensure responsible development and use of fine-tuned LLMs. The current work explores the various steps that can be taken for bias mitigation and transparent documentation for different stakeholder engagements. The purpose of the chapter is to analyse the perspectives from technical foundations with ethical, cultural, and policy considerations and provides an integral view of the societal impact of fine-tuned LLMs. 2026 Elsevier Inc. All rights reserved.
Chandra, J.; Joseph, Alwin; Joseph, Joel; Upadhyay, Priyansha; Kumar, Satyam
Challenges and Applications of Generative Large Language Models;pp.23-45
https://doi.org/10.1016/B978-0-443-33592-1.00008-5 https://www.scopus.com/pages/publications/105032982650?origin=resultslist
Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Upadhyay P., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Kumar S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
Applications and future directions in multimodal large language model: opportunities and challenges
automation; customer care; healthcare assistant; information management; machine learning; Multimodal large language models
Multimodal large language models (MLLMs) are an application of artificial intelligence that is rapidly growing by integrating numerous use cases. MLLMs have the capability to process data from several sources, including structured and unstructured. It enables large language models (LLMs) to give insights to the user by analyzing data from various formats. The traditional way of analyzing the data was done with a single data format. While using MLLMs, multiple data modalities are handled to manage complicated multimodal tasks, like generating content, multimodal perception, and augmenting human-computer interaction. This chapter discusses in detail the insights from data from multiple modalities and domains of the use cases of MLLMs. We also discuss the advantages of MLLMs and explain the transformative benefits from unimodal systems to multimodal systems in different sectors. We also focus on the ethical usage of MLLMs by addressing the challenges related to privacy, operational limits, bias, computational difficulties, and data scarcity. The scarce assessment metrics and trials in accomplishing robust explainability are also discussed. To train these MLLMs, acquiring and training the data utilizes more computation and power consumption, along with addressing the data security and privacy concerns. The chapter also discusses the sensible usage of AI through different problems and practices. Detailed analysis and strategies for addressing global challenges and promoting novelty in model development, outlining how these MLLMs shape the upcoming technological innovation focusing on ethical application of technology with an advantage on society. 2026 Elsevier Inc. All rights reserved.
Chandra, J.; Malviya, Meenakshi; Sabu, Samson; Rajendran, Rajesh Kanna; Joseph, Alwin
Challenges and Applications of Generative Large Language Models;pp.219-241
https://doi.org/10.1016/B978-0-443-33592-1.00006-1 https://www.scopus.com/pages/publications/105032983757?origin=resultslist
Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Malviya M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Sabu S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Rajendran R.K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
Human gut microbiota and regulation of human behavior
Bacteria; Behavior; Gut microbiota; Hormones; Regulation
The human body is often referred to as a microbiome, embodying a multitude of microbes. Specifically, the gut microbiome constitutes a cluster of bacteria within the gastrointestinal (GI) system. In both healthy individuals and those facing health issues, these bacteria significantly impact human physiology. They form an integral part of the unconscious system that exerts influence over human behavior. Globally, researchers are actively investigating the components of a good gut microbiome to enhance our comprehension of the role gut microorganisms play in health and disease. The gut harbors a diverse array of bacteria, encompassing both hazardous and beneficial types. Furthermore, studies on specific health-improving parasitic species are yielding biological insights that may propel the development of novel drugs. The gut microbiome directly impacts an individual's health by secreting physiologically active compounds like vitamins, essential amino acids, and lipids. Moreover, it indirectly influences the immune system and metabolic functions, thereby affecting moods and cognitive functions. Recognition of the role played by gut microbiota extends to various diseases and disorders. Evidence demonstrates significant interactions between the microbiome and certain drugs, profoundly shaping their intended effects. The interplay between the immune system, gut microbiota, and psychiatric disorders such as eating disorders, as well as conditions like cancer, autoimmune diseases, and autism spectrum disorder, is increasingly evident through ongoing research. This is unsurprising given the pivotal roles played by daily caloric intake, eating habits, and food composition in regulating various biological systems. Recent research sheds light on the intricate gut-brain axis (GBA), a two-way communication system where the gut receives signals from the brain, and vice versa. Microbes in the GI tract produce neurotransmitters that influence crucial functions like learning, memory, attention, and emotions. Even a slight change in the gut microbiome can lead to inflammatory reactions, connecting to the hypothalamic-pituitary-adrenal (HPA) axis and influencing human behavior. Under stressed conditions, changes in the gut microbiome can harm helpful bacteria, reduce the variety of bacteria in our gut, and allow harmful bacteria to grow. This makes us more likely to get sick and causes inflammation in our gut. Some studies even suggest that chemicals produced by the gut during an infection can affect our brain, making us more prone to feeling anxious or depressed. Knowing that our gut microbiome influences our behavior, it's essential to pay attention to what we eat, avoid drugs that harm our gut bacteria, and consider taking probiotic supplements in our diet. 2025 Elsevier Inc. All rights reserved.
Noel, Joe Karunya; Mahadevan, Madhumitha; Umesh, Mridul; Suresh, Sreehari
Advancing Science and Innovation in Healthcare Research: Health Horizons;pp.295-316
https://doi.org/10.1016/B978-0-443-33351-4.00020-9 https://www.scopus.com/pages/publications/105019729127?origin=resultslist
Noel J.K., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Mahadevan M., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Umesh M., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Suresh S., Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Tamil Nadu, Chennai, India
Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine
Clinical decision-making; Computational intelligence; Electronic health records; Explainable artificial intelligence (XAI); Health prediction systems; interpretability; Medical data analysis
Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved.
Kannan, M.; Sindhu, V.; Umamageshwari, D.; Keerthika, K.
Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics;pp.245-271
https://doi.org/10.1016/B978-0-44-333234-0.00020-0 https://www.scopus.com/pages/publications/105033423078?origin=resultslist
Kannan M., Department of Computer Science, CHRIST University, Bangalore, India; Sindhu V., Department of Computer Science, CHRIST University, Bangalore, India; Umamageshwari D., Department of Computer Science, CHRIST University, Bangalore, India; Keerthika K., Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India