96 20 16488 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jenefa, J.; Sambandam, Rakoth Kandan; Vinodha, D.; Vetriveeran, Divya; Balakrishnan, Sivaneasan Title A name given to the resource Deep Learning-Based Approach for Automated Cataract Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Deep Learning Models towards Health Informatics Management: Foundations, Challenges and Opportunities;pp.117-130 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003678007-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003678007-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026950840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026950840?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jenefa J., Christ University, Bangalore, India; Sambandam R.K., Christ University, Bangalore, India; Vinodha D., Christ University, Bangalore, India; Vetriveeran D., Christ University, Bangalore, India; Balakrishnan S., Singapore Institute of Technology, 10 Dover Drive, Singapore Description An account of the resource Advancements in deep learning approaches is of profound significance in the early detection of cataracts. Automated cataract detection using deep learning approaches is proposed in this chapter. Initially, two pretrained custom convolutional neural network (CNN) architectures, VGG-19 and MobileNetV2, were implemented to detect cataracts. ODIR-5K dataset is used for training, testing, and validating these models, and it has almost 6,400 fundus images. This preprocessed dataset provides the metadata of the available images and is labeled with diagnostic keywords. Since the dataset is highly imbalanced, class weighting techniques are utilized to avoid the impact of the imbalanced dataset. The performance of the models is evaluated, and results show that the ensemble approach outperforms other pretrained models, demonstrating the efficacy of hybrid CNN architecture in enhancing the accuracy of the diagnosis process. 2026 selection and editorial matter, T. Ananth Kumar, R. Rajmohan, M. Niranjanamurthy and G. Sambasivam. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104045118-2; 978-100367800-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Siju, Marbin; Bose, Shreya; Vijayalakshmi, A. Title A name given to the resource From Preprocessing to Prediction: An Analytical Study on Diabetes Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence for Predictive Healthcare: Towards Personalized Treatment and Disease Prevention;pp.33-62 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003674542-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003674542-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034236743?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034236743?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siju M., Department of Statistics and Data Science, CHRIST University, Bangalore, India; Bose S., Department of Statistics and Data Science, CHRIST University, Bangalore, India; Vijayalakshmi A., Department of Statistics and Data Science, CHRIST University, Bangalore, India Description An account of the resource Early detection of diabetes is crucial for improving a patients long-term health. In this chapter, we study diabetes and diabetes-related factors. We also delve into various imputation techniques used to address missing data. Missing data is generally a very critical issue in healthcare analytics, as a limited history of clinical records often leads to biased analysis and suboptimal model representation. This chapter gives a detailed literature review of data imputation methods. In this chapter, we have done two case studies. In the first case study, mean, median, and mode imputation techniques are applied to artificially created missing values to examine their effect on the structure and distribution of the data. The second case study captures a prediction model for a diabetes diagnosis using the same dataset. Here, a random forest prediction model is created to predict the possible presence of diabetes. An accuracy of 97.07% is achieved on the test data, which shows that diabetes can be predicted by considering other dependable variables. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058017-2; 978-100367454-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource David, Sonia; Sadasivan, Anupama Title A name given to the resource Tech-Enabled Transformations in Gender-Inclusive Healthcare: A Critical Interpretive Synthesis of Artificial Intelligence in India Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence for Predictive Healthcare: Towards Personalized Treatment and Disease Prevention;pp.164-183 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003674542-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003674542-10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034230620?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034230620?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant David S., Department of Psychology, JAIN (Deemed to be) University, Bangalore, India; Sadasivan A., School of Psychological Sciences, CHRIST University, Bangalore, India Description An account of the resource While artificial intelligence (AI) has the potential to revolutionize healthcare, it also runs the risk of exacerbating structural injustices for Indias gender-diverse communities. This critical interpretive synthesis explores the effects of AI-enabled health devices on LGBTQIA+ inclusion, drawing on intersectional feminism, queer theory, and constructivist learning. Reviewing 30 interdisciplinary studies (20102024), three themes emerge: (1) algorithmic bias: AI systems replicate gendered and caste-based exclusions through non-representative datasets and heteronormative design; (2) structural barriers: infrastructural patriarchy, limited gender-sensitive governance, and gaps in Indias AI policy; and (3) digital inequities: low digital literacy, moral conservatism, and caste hierarchies restrict access to affirming care. Research shows AI often erases or misgenders trans and nonbinary identities, causing epistemic harm. Nonetheless, inclusive innovation is possible through participatory, queer-led AI design. The study warns that without centering intersectional justice, AI in healthcare risks amplifying marginalization and epistemic violence. It recommends co-creation with LGBTQIA+ stakeholders, gender-sensitive audits, and care-centered policy reforms. Rejecting techno-solutionism, it advances a Global South, justice-focused approach that prioritizes equity, contextual awareness, and lived realities in AI healthcare design. 2026 selection and editorial matter, Syed Nisar Hussain Bukhari; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058017-2; 978-100367454-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srividya, R.; Joseph, Rose Bindu; Dara, Vijaya Lakshmi; Bunruangses, Montree Title A name given to the resource An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Robotics in Weaponry using Machine Learning and Engineering;pp.451-468 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003663461-26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003663461-26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037563470?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037563470?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Srividya R., Department of Computer Science, M.S. Ramaiah College of Arts Science and Commerce Autonomous, India; Joseph R.B., Department of Mathematics, Dayananda Sagar College of Engineering, Bangalore, India; Dara V.L., School of Business and Management, CHRIST (Deemed to be University), Yeshwanthpur Campus, Bangalore, India; Bunruangses M., Department of Computer Engineering, Rajamangala University of Technology Phra Nakhon, Bangkok, 10300, Thailand Description An account of the resource Next-generation AI-enabled defense is essential to deter enemy drones, swarms, and autonomous vehicles in contested and deceptive environments, with modern battlefields becoming increasingly dependent on autonomous robot platforms. To discover and dissect and eliminate hostile autonomous threats in real-time, this study presents an integrated Adversarial-Resilient Swarm Defense AI Framework (AR-SDAI) with a Spatio-Temporal Transformer, a Multi-Agent Reinforcement Learning Countermeasure Module, and a Hybrid Graph Attention Network. To identify hidden or counterfeit threats and improve defense against enemy attacks, the system begins with applying a Transformer-based situational awareness model to merge multi-sensor battlefield data to fuse. Autonomous defense drones are then controlled by a multi-agent reinforcement learning framework to perform actions of dynamic electronic jamming and optimal interception maneuvers in a swarm environment. Finally, the system can find unusual patterns and create human-understandable counter-strategies for human-in-the-loop control with the help of a graph-based explainability layer that models the interactions of adversary swarms as dynamic graphs. Compared to traditional rule-based and CNN-RNN baselines using experiments on a simulated Red-Blue drone warfare test benchmark, the suggested AR-SDAI is better by 23% in threat detection accuracy, 31% in swarm interception success rate, and 19% in response latency. With its provision of robust, explainable, and flexible AI capability for next-generation robotic warfare settings, the paper in general enhances the state of autonomous defense operations. 2026 Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104094368-7; 978-104107444-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Neetha, S.S.; Singh, Shivangi; Deepa, S. Title A name given to the resource Cyber resilience through adaptive federated learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.187-214 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003660330-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003660330-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035273312?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035273312?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Neetha S.S., Department of Computer Science, Christ University, Bangalore, India; Singh S., Department of Computer Science, Christ University, Bangalore, India; Deepa S., Department of Computer Science, Christ University, Bangalore, India Description An account of the resource This chapter explores the soon-to-be-reached intersection of cyber resilience and adaptive federated learning (FL), presenting a thorough examination of how FL, and especially through its adaptability mechanisms, can significantly enhance an organizations ability to predict, withstand, recover from, and learn from cyber-attacks. It explores the fundamental concepts of cyber resilience and FL, outlines several adaptive methodologies utilized in FL (e.g., adaptive client selection, aggregation, and optimization), and examines their direct contribution to the formation of resilient and privacy-aware cybersecurity systems. Real-world applications in critical infrastructures, accompanied by an honest review of current limitations and research avenues towards the future, present the revolutionary potential embedded within this synergy-based approach in an increasingly sophisticated and interlinked digital era. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104086966-6; 978-104111510-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Haseena, Shaik Valli; Shanavas, Simna; Brundha, N.; Ayasha Title A name given to the resource Cybersecurity vulnerabilities in federated learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.61-86 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003660330-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003660330-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035266396?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035266396?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Haseena S.V., Presidency College, Bengaluru, India; Shanavas S., Presidency College, Bengaluru, India; Brundha N., M S Ramaiah College of Arts Science and Commerce, Bangalore, India; Ayasha, Christ College of Science and Management, Bengaluru, India Description An account of the resource Federated Learning (FL) has been conceived as a dispersed machine learning paradigm facilitating collaborative learning at edge devices without exposing raw data. The model is amenable to privacy preservation and data protection regulation, for example, General Data Protection Regulation compliance. Yet, more widespread deployment of FL reveals a new and extreme spectrum of cybersecurity risks. These consist of data poisoning attacks that can potentially severely contaminate model integrity, model inversion attacks that can potentially recover sensitive data from exchanged gradients, adversarial manipulations where malicious agents take advantage of model weaknesses, and incidental privacy leakage. The impact and real world implication of these attacks differs, for example, a successful poisoning attack in medicine can result in misdiagnosis, model inversion in the finance sector could leak client confidential data, and adversarial attacks in Internet of Things (IoT) would control autonomous devices with safety consequences. This chapter critically reviews these threats taking into consideration attack feasibility, harm extent, and detectability, inspired by recent case studies illustrating their applicability in real world FL deployments. We also analyze the effectiveness of current state of the art countermeasures like robust aggregation methods, differential privacy, and cryptographic methods like secure multiparty computation and homomorphic encryption. By synthesizing current research on attack paradigms and counterattack architectures, the chapter offers practical knowledge towards constructing secure, robust, and trustworthy FL systems, particularly in high-risk applications like medicine, finance, and critical infrastructure. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104086966-6; 978-104111510-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Logeswaran, K.; Savitha, S.; Suresh, P.; Prasanna Kumar, K.R.; Ponselva Kumar, A.P.; Jayasurya, A.S. Title A name given to the resource Regulatory challenges and compliance in federated learning (FL) for financial applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.1-18 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003660330-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003660330-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035271798?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035271798?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Logeswaran K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India; Savitha S., Department of Computer Science and Engineering, K.S.R. College of Engineering, Tamil Nadu, India; Suresh P., Department of Database System, School of CSE, Vellore Institute of Technology, Vellore, India; Prasanna Kumar K.R., Department of Computer Science and Design, Kongu Engineering College, Tamil Nadu, India; Ponselva Kumar A.P., Department of Information Technology, Kongu Engineering College, Tamil Nadu, India; Jayasurya A.S., Department of Electrical and electronics, University Teknoloi Petronas, Petronas, Malaysia Description An account of the resource The financial sector is increasingly turning toward artificial intelligence (AI) for applications such as fraud detection, credit scoring, and risk management. But that makes it contrary to the regulatory environment. New data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India impose stringent conditions on data residency, minimization, and sovereignty. This chapter argues that traditional centralized AI systems which require sensitive data to be collected for processing at one site simply do not sit well with these legal requirements, thereby creating massive compliance risks for financial institutions. By way of an extensive architectural study and practical application, this chapter demonstrates that the very basic functions of a traditional AI system tend to contravene prohibitions on cross-border transfers of data. Instead, we propose Federated Learning (FL) as a compliance-by-design solution that solves this sticking point. In other words, by inverting the discredited approachand bringing the algorithm to the data rather than the other way aroundFL ensures that practitioners in different institutions and jurisdictions collaborate on model training without sharing raw data. Only aggregated and anonymized updates on the model are sentinherently complying with certain data residency and data minimization principles. Besides advocating for FL as a core compliant innovation pathway, this chapter also touches on a number of regulatory uncertainties and other potential issues arising from this technology, such as liability, model security, and a need for industry-wide standards. To this end, the chapter clearly states that the adoption of privacy-preserving technologies such as FL has become integral. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104086966-6; 978-104111510-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Blessing, N.R. Wilfred; Alankritha, S.; Kanna, R. Rajesh; Varghese, Jithu; Doshi, Ruchi Title A name given to the resource Building Trustworthy 6G Networks with Generative Adversarial Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.48-55 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040302824?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040302824?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Blessing N.R.W., College of Computing and Information Sciences, University of Technology and Applied Sciences-Ibri, Oman; Alankritha S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Kanna R.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Varghese J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Doshi R., Department of Computer Science &amp; IT, Universidad Azteca, Chalco, Mexico Description An account of the resource The imminent dawn of sixth-generation (6G) networks promises a future of unparalleled connectivity and communication speeds. However, this technological leap necessitates robust security measures to counter increasingly sophisticated cyberthreats targeting the intricate 6G infrastructure. This chapter investigates the potential of Generative Adversarial Learning (GALs) as a transformative tool for building trustworthy 6G networks. With the advent of 6G networks on the horizon, ensuring trustworthiness in communication systems becomes paramount. This chapter proposes a novel approach leveraging GAL to fortify the security and reliability of 6G networks. In traditional network security paradigms, adversaries exploit vulnerabilities, necessitating constant reactive measures. However, the proactive nature of GANs enables the creation of realistic synthetic data to train robust Intrusion Detection Systems (IDS). By simulating diverse attack scenarios, a GAN-based IDS can identify and adapt to emerging threats, mitigating potential risks in real time. Moreover, GANs facilitate the generation of synthetic network traffic, enabling thorough testing of network defenses without risking actual data. Taking a proactive stance enables network operators to predict and preempt potential vulnerabilities before they are exploited. Our solution involves harnessing the power of Generative Adversarial Networks (GANs) to address 6G network security comprehensively. GANs create authentic network traffic, allowing IDS to be trained effectively in identifying and mitigating actual cyberthreats. Moreover, GANs can learn to discern typical network patterns, thus alerting to potential anomalies that may signify ongoing or imminent attacks. This proactive strategy empowers security teams to maintain an edge in navigating the constantly evolving cyberthreat landscape. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Resmi, K.R.; Joseph, Amitha; George, Bindu; Job, Dhanya; Cyriac, Sebastian Title A name given to the resource Ethical and Privacy Considerations in GAN-Based Security Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.235-245 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-17</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040316894?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040316894?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Resmi K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Joseph A., Department of Computer Science, St Alberts College (Autonomous), Kerala, Ernakulam, India; George B., Department of Computer Science, Nirmala College, Kerala, Muvattupuzha, India; Job D., Department of Computer Applications, BPC College, Kerala, Piravom, India; Cyriac S., Department of Information Technology, Southeast College, Weyburn, SK, Canada Description An account of the resource Generative Adversarial Networks (GANs) have caused a great transformation in a number of fields, including anomaly detection, healthcare, data encryption, deep learning (DL), machine learning (ML), and entertainment. They are also employed in various security applications. This chapter explores mainly the privacy and ethical concerns associated with the use of GANs. Although GANs have enormous possibilities for security applications such as biometrics, intrusion detection, and data augmentation, implementing them requires careful consideration regarding privacy and ethical issues. GANs ability to produce remarkably lifelike deepfakes poses a risk since it might promote social engineering attacks. Moreover, these discrepancies may be increased by biased security programs that arise from biases in training data. Even with all of the potential benefits, ethical and privacy concerns must be prioritized for the safe and reliable application of GANs in security. This chapter also lays the foundation of GAN-based applications in Cybersecurity. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vineetha, K.R.; Resmi, K.R.; Amrutha, K.; Blessie, E. Chandra; Omanakuttan, Midhun Title A name given to the resource GAN-Based Metaheuristic Techniques for Data Generation and Imbalance Data Control Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.220-234 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-16" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-16</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040279942?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040279942?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vineetha K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Resmi K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Amrutha K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Blessie E.C., School of Innovation, KG College of Arts and Science, Tamil Nadu, Coimbatore, India; Omanakuttan M., Computing Department, International College of Dundee Scotland, United Kingdom Description An account of the resource In cybersecurity today, the power of features such as threat detection, anomaly spotting, and predictive analytics depends heavily on having abundant, properly dispersed datasets. The actual datasets often fall short, suffering both from a lack of volume and skewed class distribution, for example, a flood of routine network activity records overshadowing the infrequent but vital records of malicious behavior. The performance of data-driven models hinges on access to abundant, well-distributed data. However, real-world datasets frequently exhibit inadequate sample sizes and pronounced class imbalances, limiting the viability of complex models. This chapter proposes a novel strategy for generating synthetic data and effectively managing class imbalance, leveraging the integration of Generative Oppositional Networks (GANs) and sophisticated metaheuristic optimization techniques. Rather than settling for fixed GAN architectures, our approach progressively enhances a dynamic GAN framework by deploying a metaheuristic search to identify optimal network topologies, antidote scaling factors, and training schedules. This iterative calibration enables the model to adaptively respond to the imbalance and ensures a richer, balanced synthetic training environment. This adaptive optimization addresses common GAN training pitfalls, mode collapse, and instability while consistently producing synthetic samples that are both precise and varied. What sets the framework apart is its built-in ability to detect and over-represent minority classes, intelligently augmenting the dataset to correct class imbalance without falling back on naive duplication. By equipping GANs with metaheuristic reasoning, the study seeks to elevate data synthesis beyond current limits, generating more robust and impartial machine learning models in any domain where data collection is limited or systematically skewed. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kanna, R. Rajesh; Priya, T. Mohana; Onyema, Edeh Michael; Goel, Shreya; Varghese, Jithu Title A name given to the resource Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.196-205 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040282191?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040282191?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kanna R.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Priya T.M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Onyema E.M., Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria; Goel S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Varghese J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India Description An account of the resource Cyberthreat Intelligence (CTI) feeds serve as crucial resources for organizations seeking to fortify their defenses against emerging cyberthreats. However, these feeds often suffer from deficiencies such as incomplete data, false positives, and a lack of contextual information. This chapter proposes an innovative approach to address these challenges by leveraging Generative Adversarial Networks (GANs) to enhance CTI feeds. We introduce ThreatGAN, a novel GAN architecture specifically designed for cyberthreat modeling. Trained on accurate CTI data, ThreatGAN learns to generate synthetic yet realistic threat indicators, including malicious uniform resource locators (URLs), Internet Protocol (IP) addresses, and attack patterns. We demonstrate the efficacy of ThreatGAN in filling gaps in existing feeds, reducing false positives, and providing essential contextual information. The quantitative and qualitative evaluation shows that ThreatGAN significantly improves CTI quality. This technique can strengthen organizations cyber defenses by enabling them to work with higher quality, more complete Threat Intelligence. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Prashant; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource Three-level biometric digital voting system Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Progressive Computational Intelligence, Information Technology and Networking;pp.486-491 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003650010-80" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003650010-80</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010780378?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010780378?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar P., CHRIST University, Bengaluru, India; Poonia R.C., CHRIST University, Bengaluru, India; Shanbhog M., CHRIST University, Bengaluru, India Description An account of the resource The aim of this research paper is to design and develop a new model of electronic voting machine (EVM) with enhanced features, that are not present in the current EVM model. The paper proposes the new model of EVM that uses biometric identifiers, such as fingerprints, iris recognition, and facial recognition, to make voting more secure and efficient. Previously, voting was done with paper ballots. This method suffered from issues like over voting, lost or misplaced ballots, and it consumed a lot of time in counting paper ballots and was also harmful for the environment due to the use of papers. The new EVM system aims to solve those issues because many security levels are added to this EVM system. It can use biometric identifiers to ensure each voter is authenticated securely and records their votes accurately. This EVM system can prevent fraud, such as voter casting multiple votes or a person illegally voting on behalf of others. The system first verifies the voters, and then the voters can cast their votes. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. Subject The topic of the resource Biometric identifiers; Eacial recognition; Election fraud prevention; Fingerprint verification; Iris recognition Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104042391-2; 978-104109427-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Khushwant; Naib, Bharat Bhushan; Yadav, Mohit; Bhatia, Sandeep; Yadav, R.K.; Panda, Deepak Kumar Title A name given to the resource Prognosis of relocation disease in animals using aggregation method with optimization techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Progressive Computational Intelligence, Information Technology and Networking;pp.46-52 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003650010-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003650010-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010777972?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010777972?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh K., University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, India; Naib B.B., School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India; Yadav M., Department of Mathematics, University Institute of Sciences, Chandigarh University, Mohali, Punjab, India; Bhatia S., School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India; Yadav R.K., Department of Data Science, Christ University, Bengaluru, Karnataka, India; Panda D.K., School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India Description An account of the resource In the most cutting-edge setting, health data is processed by machine learning algorithms, which are used to forecast illnesses. Dementia, especially Alzheimer's disease (AD), is a leading cause of diminished quality of life in the elderly. Early diagnosis by medical professionals increases the likelihood of reducing the aggressiveness of the disease. In this study, we develop a new uncertainty-based clustering model to handle the centroid selection ambiguity and the issue of noisy instances and outliers that lower the efficiency of prediction models. This work employs an uncertainty-based optimization technique to handle the unknown pattern of AD patients, since it is relatively tough to handle unknown patterns with unsupervised learning algorithms. Converting the instances in the AD dataset to the membership value of the dependent variable allows for an accurate determination of whether they belong as AD patterns or non-AD patterns. This proposed study takes a migration-based optimization method to animal migration, where the best instances are chosen as centroids and fresh instances are evaluated for clustering; this minimizes outliers throughout the clustering process by using comparable patterns. To make sure, we check the fitness values of each instance; the ones with the highest values are called centroids. To control the unknowns when dealing with outliers, the fuzzy Euclidean distance is employed. By comparing it to current state-of-the-art clustering methods, the OASIS dataset simulation results show that the proposed uncertainty-based Animal Migration optimization method (UAMO) performs better. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. Subject The topic of the resource Aggregation; Animals translocation; Optimization; Outliers; Unpredictability Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104042391-2; 978-104109427-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Neethu, P.S.; Patil, Kavita Avinash; Annalakshmi, T.; Baby Chithra, R.; Joy, Salna; Joyce, Muddasu Shiny Title A name given to the resource Child safety gadget with home security Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Progressive Computational Intelligence, Information Technology and Networking;pp.371-376 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003650010-60" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003650010-60</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010779376?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010779376?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Neethu P.S., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, India; Patil K.A., Department of Electronics and Communication Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India; Annalakshmi T., Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, India; Baby Chithra R., Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India; Joy S., Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, India; Joyce M.S., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, India Description An account of the resource The Child Safety Gadget Home Security System introduces the initial five iterations of a technologically advanced system that addresses child safety and home security concerns by integrating innovative subsystems. We started with a primary password and incremented towards advanced functionality like a camera for visual verification, a lock mechanism for entry security, and finally, app integration. Finally, we extend the system with additional capabilities to fully implement security solutions based on features like an OLED display and sensors. The concealed pseudo-passcode alert system, which wrists parents up in emergencies to respond quickly, is central to our system. Our framework prioritizes cost-effectiveness, simplicity of scalability, and use effectiveness to provide an affordable solution that can protect children's safety while increasing the efficiency of intelligent sensor networks in home environments. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. Subject The topic of the resource Child safety; Home security; Pseudo-code Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104042391-2; 978-104109427-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Raghuvanshi, Roli; Sharma, Tanushree; Rena, Ravinder; Rai, Rashmi Title A name given to the resource Artifcial intelligence for smart city development in BRICS economies: Opportunities and challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Handbook of Artificial Intelligence for Smart City Development: Management Systems and Technology Challenges;pp.136-171 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003649892-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003649892-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010750512?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010750512?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raghuvanshi R., University of Delhi, New Delhi, India; Sharma T., Manipal University Jaipur, Jaipur, India; Rena R., Durban University of Technology, Durban, South Africa; Rai R., Christ University, Bangalore, India Description An account of the resource The urbanization process and migration of labour from rural areas at a fast pace is making the application of artificial intelligence (AI) inevitable for the economies, especially the emerging economies. Today, when we look at the cities, we find them to be sitting on a plethora of data, which are produced through usage of Internet of Things (IoT), sensors, smart meters, telecommunication devices, traffic management systems, installed cameras etc., despite posing ample challenges and creating various opportunities. This chapter strives to understand the status of smart cities in Brazil, Russia, India, China and South Africa (BRICS) as BRICS nations are considered to be doing a lot on this front and they are facing challenges in terms of implementation of the same. This chapter suggests that the inclusivity of the citizens in the AI based smart cities will not only play a requisite part in the evolution of smart cities in an effective manner but also contribute towards the growth of BRICS economies on a whole. 2025 Roli Raghuvanshi, Tanushree Sharma, Ravinder Rena, Rashmi Rai. All rights reserved. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104040552-9; 978-104109401-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramakrishnan, Rashmi; Al-Khayri, Jameel M.; Nagella, Praveen Title A name given to the resource Phytochemicals and Biological Activities of Flowers of Chrysanthemum Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Bioactive Compounds in Edible Flowers;pp.102-128 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003645979-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029210116?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029210116?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramakrishnan R., Department of Life Sciences, School of Sciences, CHRIST University, Bengaluru, 560029, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, 31982, Saudi Arabia; Nagella P., Department of Life Sciences, School of Sciences, CHRIST University, Bengaluru, 560029, India Description An account of the resource A prominent edible flower in the Asteraceae family, Chrysanthemum morifolium is used extensively in traditional medicine and as a functional food because of its many different bioactive compounds. It has significant pharmacological properties and is abundant in flavonoids, phenolic acids, terpenoids, polysaccharides, and derivatives of caffeoylquinic acid. Its neuroprotective, hepatoprotective, anticancer, antidiabetic, antioxidant, anti-inflammatory, and larvicidal qualities have all been shown in scientific research. The reduction of oxidative stress and apoptosis in brain cells is associated with its neuroprotective effects, whereas the modulation of antioxidant pathways is associated with its hepatoprotective benefits. Because of its capacity to trigger apoptosis and control the cell cycle in cancer cells, the flower is said to have anticancer properties. It also supports its antidiabetic potential by improving glucose metabolism and insulin sensitivity. Its anti-inflammatory and antioxidant properties aid in the fight against oxidative stress and associated illnesses. Although moderate consumption is advised, toxicological research indicates that it is generally safe to consume. Processing methods such as formulation, drying, and extraction affect the compounds stability and bioavailability. Through a thorough examination of its phytochemistry, biological activities, safety profile, and processing considerations, this chapter emphasizes C. morifolium as a valuable natural therapeutic and functional food ingredient, highlighting its potential to prevent disease and promote health. 2026 Hosakatte Niranjana Murthy. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058108-7; 978-104107986-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lamani, Shrinivas; Murthy, Hosakatte Niranjana Title A name given to the resource Phytochemicals and Biological Activities of Flowers of Clitoria ternatea (Butterfly Pea) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Bioactive Compounds in Edible Flowers;pp.63-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003645979-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029214990?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029214990?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lamani S., Department of Botany, DVS College of Arts and Science, Shimoga, 577201, India, Department of Botany, Karnatak University, Dharwad, 580003, India; Murthy H.N., Department of Botany, Karnatak University, Dharwad, 580003, India, Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea, Department of Life Sciences, CHRIST University, Bengaluru, 560039, India Description An account of the resource In recent years, many countries have witnessed a significant rise in the adoption of plant-based remedies, leading to a substantial increase in the global demand for herbal resources. This chapter explores Clitoria ternatea, a species with remarkable potential due to its bioactive constituents and diverse biological activities. Most of the unique secondary metabolites and their analogues identified in this plant are anthocyanins, including ternatins, preternatins, cyanidin, and delphinidin, along with phenolic acids, terpenoids, and phytosterols derived from the flowers. Altogether, 32 anthocyanins and 19 additional metabolites have been documented across various studies. Different extracts of C. ternatea have demonstrated a wide range of biological properties, notably antioxidant, antimicrobial, anti-inflammatory, antidiabetic, cytotoxic, and anticancer activities. To support young researchers investigating potential chemotherapeutic agents for the treatment and prevention of cancer and other diseases, this chapter focuses on the biological functions of C. ternatea, with particular emphasis on its anticancer and antidiabetic benefits. 2026 Hosakatte Niranjana Murthy. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058108-7; 978-104107986-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Murthy, Hosakatte Niranjana; Yadav, Guggalada Govardhana; Paek, Kee Yoeup; Park, So-Young Title A name given to the resource Edible Flowers: An Updated Review on Nutritional Composition, Phytochemicals, and Biological Activities Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Bioactive Compounds in Edible Flowers;pp.3-34 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003645979-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029214254?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029214254?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Murthy H.N., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, 560029, India, Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea, Department of Botany, Karnatak University, Dharwad, 580003, India; Yadav G.G., Department of Botany, Karnatak University, Dharwad, 580003, India, Department of Botany, ADB First Grade College, Harapanahalli, 583131, India; Paek K.Y., Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea; Park S.-Y., Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea Description An account of the resource Edible flowers have been identified as a possible source of vitamins, minerals, protein, fat, and carbohydrates. Additionally, they are abundant in bioactive substances such as terpenoids, alkaloids, phenolics, flavonoids, glucosinolates, and essential oils. The pharmacological properties of edible flowers have been shown to include anti-aging, anti-cancer, antidiabetic, anti-inflammatory, antimicrobial, antioxidant, cardioprotective, hepatoprotective, and neuroprotective effects in recent years. The most recent information on the nutritional, phytochemical, and biological properties of agathi, lotus, moringa, and banana flowers is presented in this review. This analysis concludes by shedding light on the latest data regarding the use of edible flowers in the food, pharmaceutical, and cosmetic industries. 2026 Hosakatte Niranjana Murthy. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058108-7; 978-104107986-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Yadav, Guggalada Govardhana; Murthy, Hosakatte Niranjana Title A name given to the resource Chemistry and Biological Activities of Hibiscus sabdariffa L. Flowers Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Bioactive Compounds in Edible Flowers;pp.225-245 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003645979-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029202710?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029202710?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Yadav G.G., Department of Botany, A. D. B. First Grade College, Harapanahalli, 583131, India, P. G. Department of Studies in Botany, Karnatak University, Dharwad, 580003, India; Murthy H.N., P. G. Department of Studies in Botany, Karnatak University, Dharwad, 580003, India, Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea, Department of Life Sciences, CHRIST University, Bengaluru, 5600039, India Description An account of the resource Hibiscus sabdariffa L. is one of the underutilized plants of the Malvaceae family, used for its nutritional and medicinal properties. Its native range spans central Africa, including the Central African Republic, Chad, Congo, Gabon, Ghana, Nigeria, and South Sudan, and naturalized globally. Traditionally, flowers are used as a food, either consumed fresh or prepared into various recipes. Flowers are also used in the preparation of beverages, fermented and herbal drinks, ice cream, jam, wine, cakes, puddings, chocolates, sauce, soup, and stew. The medicinal properties of the flowers are due to the presence of a wide array of bioactive compounds, such as anthocyanins, flavonoids and their glycosides, phenolic acids, terpenoids, alkaloids, coumarins, organic acids, vitamins, and fatty acids. A total of 78 secondary metabolites belonging to different phytochemical groups mentioned above have been identified in the flowers. The activity of H. sabdariffa flowers against hypertension, hyperlipidemia, cancer and tumours, oxidation, obesity, microbial infection, inflammation, and anaemia is reported extensively. The present chapter summarizes the distribution of H. sabdariffa and traditional uses, bioactive compounds, and pharmacological activities of its flowers. Further, the toxicological studies concerning flowers were also discussed. 2026 Hosakatte Niranjana Murthy. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058108-7; 978-104107986-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Joseph, Kadanthottu Sebastian Title A name given to the resource Phytochemicals and Biological Activities of Flowers of Hibiscus x rosa-sinensis (Chinese Rose) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Bioactive Compounds in Edible Flowers;pp.209-224 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003645979-11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003645979-11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029208743?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029208743?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph K.S., Department of Life Sciences, Christ University, Bangalore, India Description An account of the resource The flowers of Hibiscus rosa-sinensis L., are a flowering shrub of Malvaceae family. The plant is commonly known as a Chinese rose or shoe flower. The plant is distributed in the tropical and sub-tropical regions of the world and crossing has led to a large number of cultivars with varying patterns in size, shape, and colour. This chapter provides a comprehensive analysis of the distribution, traditional uses, phytochemical composition, and diverse biological activities associated with the flowers of this ornamental plant. The flowers have played a significant role in various traditions, as a remedy for various ailments and as a source of food. A rich profile of bioactive compounds was observed during phytochemical phytochemical investigations. These bioactive compounds included anthocyanins, flavonols, flavan-3-ols, flavanonols, flavanone, flavones, other phenolic compounds, essential oil, etc. which contributed to the medicinal properties of the flowers. The flower extracts demonstrated various biological activities like Hypolipidemic, Cardioprotective, Hypotensive, Anticancer, Antidiabetic, Cytotoxicity, Antimutagenic, Larvicidal, Anti-inflammatory, Anticonvulsive, Wound Healing, Hepatoprotective, Antioxidant, Antimicrobial, Antifertility, Antispermatogenic, antipyretic among other activities. This chapter bridges ethnopharmacological knowledge with scientific research, showcasing the potential of H. rosa-sinensis flowers in the development of novel therapeutic drugs as well as natural product development. 2026 Hosakatte Niranjana Murthy. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104058108-7; 978-104107986-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online