Cybernetics, Cognition and Machine Learning Applications
Cybernetics, Cognition and Machine Learning Applications

Author: Vinit Kumar Gunjan

Publisher: Springer Nature

Published: 2022-09-15

Total Pages: 237

ISBN-13: 9811914842

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This book includes the original, peer-reviewed research articles from the 3rd International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2021), held in August 21 – 22, 2021, at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber physical systems and cybernetics.

Quantum Innovations at the Nexus of Biomedical Intelligence
Quantum Innovations at the Nexus of Biomedical Intelligence

Author: Dutt, Vishal

Publisher: IGI Global

Published: 2023-12-29

Total Pages: 306

ISBN-13:

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The convergence of quantum technologies and biomedical intelligence is a frontier of boundless potential. The quantum advancements revolutionize disease detection, personalized medicine, and health monitoring frameworks while confronting the pressing challenge of accountability in machine learning systems within the biomedical domain. How do quantum innovations at the nexus of biomedical intelligence redefine biomedical research and healthcare, addressing critical inquiries such as the transformative potential of quantum computing, machine learning, and sensing technologies? Quantum Innovations at the Nexus of Biomedical Intelligence explores the intricate synergy between quantum mechanics and the biomedical domain. This book elucidates the profound implications and applications arising from the fusion of quantum computing, artificial intelligence, and biomedical sciences. This book introduces biomedical engineering, setting the stage for a deep dive into the transformative role of quantum computing and artificial intelligence. As the narrative unfolds, the text navigates the reader through the uncharted territories of quantum-enhanced machine learning, quantum sensing and their profound impact on diagnostics, personalized medicine, and health monitoring frameworks. The intersection of quantum computing and AI in medical advancements and cybersecurity is illuminated, offering a comprehensive understanding of the multifaceted applications of these cutting-edge technologies. This book is ideal for researchers, scientists, academics, and professionals across diverse disciplines in quantum innovations within biomedical intelligence.

Internet of Things-Based Machine Learning in Healthcare
Internet of Things-Based Machine Learning in Healthcare

Author: Prasenjit Dey

Publisher: CRC Press

Published: 2024-06-10

Total Pages: 242

ISBN-13: 1040031854

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The Internet of Medical Things (IoMT) is a system that collects data from patients with the help of different sensory inputs, e.g., an accelerometer, electrocardiography, and electroencephalography. This text presents both theoretical and practical concepts related to the application of machine learning and Internet of Things (IoT) algorithms in analyzing data generated through healthcare systems. Illustrates the latest technologies in the healthcare domain and the Internet of Things infrastructure for storing smart electronic health records Focuses on the importance of machine learning algorithms and the significance of Internet of Things infrastructure for healthcare systems Showcases the application of fog computing architecture and edge computing in novel aspects of modern healthcare services Discusses unsupervised genetic algorithm-based automatic heart disease prediction Covers Internet of Things–based hardware mechanisms and machine learning algorithms to predict the stress level of patients The text is primarily written for graduate students and academic researchers in the fields of computer science and engineering, biomedical engineering, electrical engineering, and information technology.

Futuristic e-Governance Security With Deep Learning Applications
Futuristic e-Governance Security With Deep Learning Applications

Author: Kumar, Rajeev

Publisher: IGI Global

Published: 2024-01-24

Total Pages: 291

ISBN-13: 1668495988

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In today's rapidly advancing digital world, governments face the dual challenge of harnessing technology to enhance security systems while safeguarding sensitive data from cyber threats and privacy breaches. Futuristic e-Governance Security With Deep Learning Applications provides a timely and indispensable solution to these pressing concerns. This comprehensive book takes a global perspective, exploring the integration of intelligent systems with cybersecurity applications to protect deep learning models and ensure the secure functioning of e-governance systems. By delving into cutting-edge techniques and methodologies, this book equips scholars, researchers, and industry experts with the knowledge and tools needed to address the complex security challenges of the digital era. The authors shed light on the current state-of-the-art methods while also addressing future trends and challenges. Topics covered range from skill development and intelligence system tools to deep learning, machine learning, blockchain, IoT, and cloud computing. With its interdisciplinary approach and practical insights, this book serves as an invaluable resource for those seeking to navigate the intricate landscape of e-governance security, leveraging the power of deep learning applications to protect data and ensure the smooth operation of government systems.

Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models

Author: Sharma, Avinash Kumar

Publisher: IGI Global

Published: 2024-05-02

Total Pages: 375

ISBN-13:

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The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.