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Leveraging AI and Machine Learning for Healthcare Accessibility: Enhancing Clinical Decision Support Systems in Rural Africa
Healthcare in rural Africa is hindered by resource scarcity, limited infrastructure, and a shortage of trained professionals, contributing to high mortality and morbidity rates. This study examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in clinical decision support systems (CDSS) to address these challenges. Focusing on diseases prevalent in the region, such as malaria, HIV/AIDS, and noncommunicable illnesses like diabetes, the research develops and evaluates AI-enhanced CDSS to improve diagnostic accuracy, treatment planning, and healthcare accessibility. This research contributes a framework for deploying AI-driven CDSS in resource-limited settings, with implications for enhancing global health outcomes. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Enhancing cybersecurity with distributed models and sparse mixture of experts
[No abstract available] -
Hairy Root Engineering for Enhanced Production of Secondary Metabolites
[No abstract available] -
Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support
The potential of artificial intelligence (AI) and machine learning to significantly modify clinical decision support is examined in this chapter. AI algorithms can use extensive databases of imaging outcomes, clinical trials, and medical records to identify complex patterns that lead to precise diagnoses, treatment plans, and progressively affected patient outcomes. A diagnosis includes evaluating the patients condition leveraging information gathered from multiple kinds of tests and their past medical history. AI-driven systems in the healthcare industry are constrained by the difficulty of handling tiny volumes and poor-quality medical data. A better prediction system for low-quality data and the analysis of unusual and sensitive medical cases can be analyzed by more powerful AI technologies. The chapter shows how AI-powered equipment is presently affecting healthcare. With excellent accuracy, device getting-to-know algorithms can examine medical images and identify potential abnormalities in X-rays, mammograms, or other imaging modes that a human might overlook. Furthermore, AI may review an affected persons records and show fitness facts to estimate a patients vulnerability to specific diseases, allowing for active intervention and preventative measures. The chapter concludes with critical tips for optimizing AIs complete range of applications in scientific care. To ensure the ideal and ethical application of these effective technologies, the responsibilities consist of defensive record safety and privacy, tackling algorithmic bias, and inspiring cooperation among clinical experts and AI developers. The healthcare zone can enter a modern section of using statistics to make educated selections through the implementation of AI and machine-gaining knowledge. 2025 selection and editorial matter, Rakesh Kumar and Meenu Gupta individual chapters, the contributors. -
Non-orthogonal multiple access wireless systems using deep learning
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan. -
Emerging Trends and the Future of Business Analytics
[No abstract available] -
Unveiling the Factors of Women Entrepreneurs on Social Media to Achieve Enterprise Sustainability
The research studies in the area of womens entrepreneurship (WE) has received more attention in the last decade due to its impact on bringing balanced development. On one hand, the growth of digital innovation has changed the landscape of entrepreneurship in emerging markets and on the other hand, the advocacy on business sustainability has increased. Prior studies are limited to understand the role of WE in this changing landscape. This study aims to identify the most relevant factors that influence the women entrepreneurs on social media to develop sustainable enterprise. An extensive literature review has been conducted to advance the knowledge on the WE and has been presented in form of a conceptual model to present a comprehensive perspective. Further, the research identifies social factors, psychological factors, resource factors, financial factor, firm-performance related factors, and technological factors. These factors are linked with entrepreneurial orientation among women on social media and therefore this helps in gaining sustainability. These study further present implications, strategies and agenda for future research in the area of WE. 2025 selection and editorial matter, Esra Sipahi Dongul, Serife Uguz Arsu, Richa Goel, and Tilottama Singh; individual chapters, the contributors. -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Quantum cryptography: An in-depth exploration of principles and techniques
Quantum cryptography is evolving in the field of data security and cryptographic research, as it offers a high level of security based on the principles of quantum mechanics. This chapter provides an extensive understanding and in-depth explanation about the basic concepts of the techniques implemented in quantum cryptography. The exploration of the fundamental concepts begins with elaboration on the foundational concepts of quantum mechanics, such as no-cloning, entanglement, superposition, and quantum state measurement, which are crucial for the better understanding of quantum cryptography. Further, the chapter delves more into the quantum key distribution (QKD) protocols such as BB84, BBM92, and B92. All the QKD protocols are analysed and compared based on the underlying principles and techniques. Furthermore, the importance and benefits of the integration of quantum cryptography with the traditional algorithms are also discussed. The chapter also aims to provide thorough study of quantum cryptography principles, challenges, and future directions along with a detailed comprehensive review of quantum cryptographic techniques. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Safeguarding the future through the prevention of cybercrime in the quantum computing era
Quantum computing is an emerging field that holds great promise for solving complex problems at an unprecedented speed by harnessing the principles of quantum mechanics. However, this disruptive technology also introduces new challenges, particularly in the realm of cybersecurity. Quantum computing can lead to cyberattacks such as cryptographic attacks, data breaches, blockchain vulnerabilities, social engineering, and phishing attacks. It is important to note that, at present, these risks are largely theoretical, as practical, large-scale quantum computers capable of breaking current cryptographic systems are not yet available. However, it is crucial for researchers, organisations, and policymakers to anticipate and address these potential threats in advance by developing quantum-resistant cryptographic algorithms, improving security protocols, and raising awareness about the evolving landscape of cyberthreats in the quantum computing era. There is a need for preparing safeguard measures form the quantum threat by investing in quantum-safe technologies, training cybersecurity professionals in quantum-resistant techniques, and fostering collaboration among industry, academia, and government entities. As quantum computing progresses, the landscape of cybercrime is expected to evolve, necessitating the development of robust laws to mitigate potential threats. The chapter aims at understanding the intersection of quantum computing and cybercrime, highlighting the potential implications and risks associated with quantum advancements in the context of cybersecurity. The chapter also emphasises the need for proactive measures and policies to mitigate the risks posed by quantum computing to cybersecurity. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Integrating intelligence: The convergence of computer science and engineering in cyber-physical systems
The dynamic and innovative paradigm known as cyber physical systems (CPSs) arises from the merging of digital technology and physical infrastructure. This chapter provides a thorough analysis of CPSs, covering the basic ideas, constituent parts, a range of applications, and their integration with more complex subjects. Fundamentally, CPSs represent the smooth fusion of computational and physical components, enabling real-time control, analysis, and monitoring. The fundamentals of CPSs are explained in this chapter, with a focus on how they facilitate the development of interconnected networks that can coordinate complicated tasks across multiple domains. A close examination of the complex interactions that occur between sensors, actuators, processors, and communication networks in CPS designs demonstrates how these components work together to gather, process, and distribute data. Furthermore, a wide range of industries, including infrastructure, manufacturing, transportation, and healthcare, are impacted by the diverse applications of CPSs. CPSs transform conventional processes, improving efficiency, safety, and production. Examples of these processes include intelligent healthcare devices that monitor patient vitals and smart transportation systems that optimise traffic flow. When CPSs are combined with more complex subjects, they become even more powerful, accelerating innovation and change in a variety of fields. By enabling CPSs to process and analyse data at network edges, edge computing can lower latency and bandwidth consumption. Algorithms for machine learning improve decision-making, allowing CPSs to adjust and gain knowledge from real-world data. By protecting CPSs from cyberattacks, security and resilience measures guarantee the availability and integrity of vital systems. Furthermore, human CPS contact opens up new collaborative paradigms and gives people the ability to communicate with intelligent systems in a natural way. To sum up, this chapter gives readers a thorough grasp of CPSs and how they have revolutionised contemporary life. It adds to the continuing conversation on CPS research, innovation, and implementation by clarifying their basic ideas, elements, applications, and integration with more complex subjects. With ongoing research and cooperation, CPSs have the potential to completely transform our world and bring in a new era of intelligence, creativity, and connectivity. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Exploring the impact of smart watches on health management for senior citizens: A qualitative study
The growing prevalence of chronic diseases and the aging population necessitate innovative health management solutions. The study presented in this chapter investigated the role of smart watches in managing health among senior citizens, focusing on usability, accessibility, and social support. Using a mixed-methods approach, involving semi-structured interviews and structured surveys with 12 senior citizens aged 60-69, we explored their experiences with using smart watches over 6-12 months. Our findings highlight that while smart watches provide significant health benefits, such as monitoring vital signs and promoting physical activity, usability challenges persist due to small text and complex interfaces. Social support from family is crucial for adoption and effective use. Our participants reported improved health outcomes, increased motivation for physical activity, and better communication with healthcare providers. However, privacy and security concerns, along with the need for customisable and user-friendly designs, were emphasised. This study underscores the potential of smart watches to enhance health management for senior citizens, advocating for targeted design improvements and robust data protection measures to maximise their benefits. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Gestational diabetes prediction using hybrid probabilistic machine learning models
[No abstract available] -
Bioinformatics Tools and Deep Learning for Plant High-Throughput Phenotyping and Phenomics
High-throughput phenotyping and phenomics are essential for advancing plant research and improving crop performance. The integration of bioinformatics tools and deep learning methodologies has transformed the way data is processed and analyzed in these fields. Bioinformatics tools facilitate the management and interpretation of large-scale genomic and phenotypic data, enabling researchers to extract valuable insights. Deep learning algorithms, particularly convolutional neural networks, have shown significant promise in automating the analysis of complex plant images and enhancing trait identification and prediction. This synergy between bioinformatics and deep learning accelerates the identification of key traits, improves the precision of phenotypic assessments, and supports the development of more resilient and productive crops. This chapter highlights how these advanced technologies contribute to more effective and scalable plant phenotyping and phenomics efforts. 2025 selection and editorial matter, Jen-Tsung Chen; individual chapters, the contributors. -
Generative AI for Healthcare Security: Addressing Privacy Challenges through Anomaly Detection in Healthcare Communications
Cybersecurity within the healthcare sector is paramount due to the sensitive nature of patient data and critical healthcare services. This chapter explores the role of Generative AI (GAI), particularly using BERT embeddings and the Isolation Forest algorithm, in enhancing cybersecurity measures. It begins by discussing the significance of cybersecurity in healthcare and the potential threats healthcare organizations face, emphasizing the need for robust security measures to protect patient data and ensure uninterrupted healthcare services. The chapter provides an overview of GAI and its applications in cybersecurity, focusing on its ability to detect anomalies in healthcare communications. A detailed case study demonstrates the practical implementation of GAI techniques for anomaly detection in healthcare emails, highlighting the effectiveness of BERT embeddings and Isolation Forest in identifying potential security breaches. Furthermore, the chapter discusses the broader implications of generative AI in healthcare cybersecurity, addressing privacy concerns and ethical considerations. The findings underscore the importance of integrating advanced AI technologies with robust privacy-preserving measures to safeguard patient data while promoting technological innovation in healthcare cybersecurity. 2025 selection and editorial matter, Anoop V.S., Suhasini Verma, Usharani Hareesh Govindarajan. -
Quantum leap in quick commerce: Harnessing quantum computing for sustainable and efficient logistics
This chapter explores how quantum computing can revolutionise the quick commerce industry, focusing on logistics and supply chain management to boost efficiency and sustainability. Quick commerce, an emerging trend in e-commerce, promises incredibly fast delivery speeds to satisfy ever-growing consumer expectations. But this rapid expansion isnt without its hurdles, particularly when it comes to maintaining smooth operations and being eco-friendly. Quantum computing steps in as a potential game-changer, bringing its powerful processing abilities to the table. Integrating quantum computing into quick commerce could transform logistics operations, from planning delivery routes to managing warehouse resources. Its not just about speeding things up; its about rethinking the entire supply chain, including how we handle inventory and the final leg of delivery. Quantum algorithms, which are built on the principles of quantum mechanics, can help companies predict demand more accurately, restock shelves faster, cut down on waste, and enhance overall efficiency. These algorithms are especially good at optimising routes in real time, considering various factors to ensure quicker, more dependable deliveries. This study aims to bridge the gap between the theory and practice of quantum computing in logistics. It examines how quantum computing can be used, its possible benefits, and the challenges it might face in the quick commerce sector. The chapter argues that quantum computing could usher in a new era of logistics management characterised by unprecedented efficiency in routing deliveries, controlling inventory, and allocating resources. Highlighting the use of quantum algorithms for dynamic routing and demand forecasting underscores the potential for creating a more agile and eco-friendly delivery system. Ultimately, this research shines a light on how we can turn the conceptual promise of quantum computing into real-world improvements in quick commerce logistics, advocating for a future where quantum computing leads to a sustainable and efficient quick commerce ecosystem. 2026 selection and editorial matter, Pushan Kumar Dutta, Pronaya Bhattacharya, Jai Prakash Verma, Ashok Chopra, Neel Kanth Kundu and Khursheed Aurangzeb; individual chapters, the contributors. -
Enhancing Log File Analysis in Digital Forensics and Incident Response through Machine Learning
Log file analysis is crucial for identifying and exploring digital security incidents by recording system and network traffic. The growing volume and complexity of log data do not allow traditional analytical methods to be used, which led to the need for the development of more advanced analytical tools. This chapter shows a new method to infer practical information from the log file analysis using machine learning algorithms combined with Python programming. The technique has the following structure: Data preprocessing, Feature extraction, and then using multiple machine learning models such as RandomForestClassifier, Gradient Boosting Classifier, SVM, XGBoostClassifier, and MLPClassifier. Adding Python greatly improves these advanced models' accuracy and efficiency in analyzing log files. The XGBoostClassifier achieved the highest accuracy, which was 0.9198 as precision, and it indicates good applicability to complicated log data compared to another model in our test. This section compares the machine learning models using the UNSWNb15 dataset, which provides a broad range of network traffic data. The chapter contains some visualizations of flagship results and a detailed discussion about the results, discussing the challenges and limitations of the proposed approach. It also suggests future research directions. The results also typify the specifics of how Python and machine learning can be disrupted to develop digital forensics incident response practicability, bringing forth such innovations that cater to solving the cyber world's rapidly transitioning threat landscapes and tooling up valued scientific knowledge in the domain. 2026 selection and editorial matter, Vinay Aseri, Sumit Kumar Choudhary, and Adarsh Kumar; individual chapters, the contributors. -
Genetic Diversity of Garcinia gummi-gutta and Sustainable Utilization
The chapter discusses the consequences of using Garcinia gummi-gutta, often known as Malabar Tamarind, sustainably while diving into the complex web of genetic variation inside the crop. Giving a thorough overview, the chapter starts by detailing the botanical and genetic traits of this enigmatic species, revealing the morphological quirks and genetic differences that make it distinct. Examining the range and preferred habitats helps to highlight the ecological niches that are essential to its existence. It delves intently into the complex web of phytochemicals found in various plant parts and explains their range of biological functions. A crucial component of this study is a thorough examination of the techniques used to gauge the genetic diversity of populations of G. gummi-gutta. The assessment of G. gummi-gutta's conservation status indicates that threats to the species genetic richness need to be taken seriously and quickly addressed. The difficulties in attaining sustainable use are examined in detail, offering a comprehensive grasp of the nuances related to overexploitation and conservation initiatives. This study of G. gummi-gutta offers evidence of the complex interplay in the field of botanical resources between genetic diversity, conservation, and sustainable use. 2025 Hosakatte Niranjana Murthy. -
Monitoring the Virtual Realm: Ethical Dilemmas and Connotations in the Metaverse?Artificial Intelligence Connection
Skip to main content Taylor & Francis Group Logo T&F eBooks ? Search for keywords, authors, titles, ISBN Advanced Search About Us Subjects Browse Products Request a trial Librarian Resources What's New!! HomeComputer ScienceArtificial IntelligenceApplying Metaverse Technologies to Human-Computer Interaction for HealthcareMonitoring the Virtual Realm: Ethical Dilemmas and Connotations in the Metaverse?Artificial Intelligence Connection Monitoring the Virtual Realm: Ethical Dilemmas and Connotations in the Metaverse?Artificial Intelligence Connection Chapter Monitoring the Virtual Realm: Ethical Dilemmas and Connotations in the Metaverse?Artificial Intelligence Connection ByMeera Mathew Book Applying Metaverse Technologies to Human-Computer Interaction for Healthcare Edition1st Edition First Published2025 ImprintAuerbach Publications Pages18 eBook ISBN9781003491668 Share Share ABSTRACT The virtual world will be altered significantly as a result of the incorporation of metaverses into digital communication. Immersive, cooperative, and resilient 3D cybernetic environments that surpass conventional web surfing define the metaverse. Modern technology and dynamic forces that fortify the metaverse are what propel this advancement, since they allow its hybrid virtual-physical nature to be effortlessly integrated. The development and fulfilment of virtual world technologies require core capabilities including blockchain, artificial intelligence (AI), cloud computing, and 5G and 6G connection. Web3, which uses blockchain technology and smart contracts to create a decentralized, user-centric Internet, is all about the practical and geographical visibility of metaverses. Nonetheless, these ideas are connected to the broader evolution and do not conflict with one another. Because of its multiple functions, the metaverse may be used for a wide range of tasks. The gaming and entertainment sectors employ the metaverse in some of its most well-known uses. Users may enjoy a vibrant and imaginative setting in the metaverse where they can play games, watch films, go to concerts, etc. Because it may offer instructors and students a virtual environment where it is feasible to conduct training and experiments that cannot be experienced in the actual world owing to potential hazards or expenses, the metaverse can have various applications and consequences in the field of education. The metaverse will also benefit corporate growth, employee cooperation and communication, the creation of more realistic simulation models for urban development, process optimization, and many other areas. However, there are drawbacks to the metaverse as well. These include addictiveness, impairment of the ability of the mind to discriminate between actual reality and augmented or virtual reality, privacy protection, safeguarding people's digital identities, information confidentiality, and the requirement for sophisticated hardware and software infrastructure in order to receive, send, simulate, and process information in real time. The Indian Information Technology Act of 2000 and its implementing rules created India's current data protection system, which places requirements on businesses managing sensitive and personal data. Businesses must create organizational safeguards to protect data and get consent before processing any data. As the metaverse integrates more deeply into our digital world, a single legal framework is critical for managing the convergence of artificial intelligence and citizen privacy. In the light of newly introduced Indian Digital Personal Data Protection Act (DPDP Act) of 2023, data fiduciaries, data holders, and data processors have to be cautious of data collection and dissemination, and for this reason, metaverse app developers, app retainers, and app disseminators need special attention. Companies that employ moral artificial intelligence strategies are more prepared to navigate moral and societal traps associated with conducting business in the metaverse. 2025 selection and editorial matter, B. Sundaravadivazhagan, Balasubramaniam S, Pethuru Raj, and K. Shantha Kumari.