Usha Desai – författare

Human–Machine Interface Technology Advancements and Applications focuses on analysis, design, and evaluation perspectives in HMI technological breakthroughs and applications. It covers a wide range of ideas, methodologies, approaches, and instruments to give the reader a thorough understanding of the field's current academic and industry practice and debate. Physical, cognitive, social, and emotional factors are all considered in the work, which is exemplified by key application fields such as aerospace, automobile, medicine, and defense. This book covers AI and machine learning methodologies as well as biological signals and HMI applications. Nanotechnology, user interface design, and interactive systems are also featured. The MATLAB approach to signal processing applications is also included.This book discusses advances in the field of human–machine interfaces and provides practical knowledge in biomedical signal processing, AI, and machine learning. It discusses augmented reality/virtual reality-based HMI applications. It examines advances in nanotechnology, user interface design, and interactive systems.This book is intended to serve as a research guide that will both inform readers about the fundamentals of HMI from academic and industrial perspectives and provide a glimpse into how human-centered designers, such as engineers and human factors specialists, will attempt to design and develop human–machine systems in the future.

Human–Machine Interface Technology Advancements and Applications focuses on analysis, design, and evaluation perspectives in HMI technological breakthroughs and applications. It covers a wide range of ideas, methodologies, approaches, and instruments to give the reader a thorough understanding of the field's current academic and industry practice and debate. Physical, cognitive, social, and emotional factors are all considered in the work, which is exemplified by key application fields such as aerospace, automobile, medicine, and defense. This book covers AI and machine learning methodologies as well as biological signals and HMI applications. Nanotechnology, user interface design, and interactive systems are also featured. The MATLAB approach to signal processing applications is also included.This book discusses advances in the field of human–machine interfaces and provides practical knowledge in biomedical signal processing, AI, and machine learning. It discusses augmented reality/virtual reality-based HMI applications. It examines advances in nanotechnology, user interface design, and interactive systems.This book is intended to serve as a research guide that will both inform readers about the fundamentals of HMI from academic and industrial perspectives and provide a glimpse into how human-centered designers, such as engineers and human factors specialists, will attempt to design and develop human–machine systems in the future.

This book presents various machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: biomedical engineering, signal processing, and computer science.Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications explores recent advancements in the use of AI/ML in practical engineering applications by inviting top experts to share the outcomes of their most recent work. Among the topics explored are detection, measurement, and monitoring of signals (biosensors and biomedical devices) and the use of diagnostic interpretations of bioelectric data using signal-processing techniques. The authors also address several machine learning tasks, such as classification (supervised learning) and clustering (unsupervised learning), in the context of engineering. Finally, the book also describes the development of new biomaterials for use in the body.The book will be a great help to researchers and academics working in the fields of biomedical signaling and/or human-machine interface.

Nanotechnology has become one of the most revolutionary scientific domains of the 21st century, providing answers for healthcare, renewable energy, environmental cleanup, and industrial advancements. Nonetheless, the swift expansion of this technology necessitates a comparable dedication to environmental responsibility and safety. Green Chemistry in Nanotechnology for Sustainable Synthesis, Characterization, and Applications of Nanoparticles meets this requirement by combining green chemistry principles with cutting-edge nanoscience.This edited collection offers an extensive summary of sustainable nanomaterial creation, starting with fundamental principles and synthesis methods based on renewable resources. It explores eco-friendly and bioinspired methods for producing gold, silver, calcium, and metal oxide nanoparticles, backed by in-depth case studies. Extensive coverage is provided on advanced characterization techniques, delivering insights into structural, morphological, and functional assessment. The book examines various applications, such as food packaging, antimicrobial surfaces, catalytic processes, energy devices, and biomedical technologies. A specific part on nanotoxicology showcases contemporary safety evaluation techniques, including organs-on-chips and 3D cell culture systems, while conversations regarding ethics and sustainability stress responsible innovation. The combination of scientific, safety, and social factors sets this work apart in its breadth. By connecting core concepts with real-world uses, the book acts as an essential tool for researchers, scholars, and industry experts aiming to create nanotechnology solutions that are not only efficient but also safe, sustainable, and ethically responsible. Policymakers and regulatory organizations will also appreciate it for developing frameworks that guarantee nanotechnology aids in advancing global sustainability objectives. This book merges green chemistry with nanotechnology, enabling synthesis from sustainable pathways and characterization through advanced techniques jointly with applications and ethical considerations all under one roof. Works by world top-rated scientists propose renewable-resource-based, bioinspired, and eco-friendly routes toward synthesis of widely varying nanoparticles that make sense in real life. Case studies with applications in health care, food packaging, catalysis, and industrial plus environmental remediation work supported by the most advanced tools of nanotoxicological assessment, such as organs-on-chips and 3D cell cultures, are also presented here. Scientific novelty will be integrated with safety and sustainability so that learners can not only create new forms of nanomaterials but also “create safely.” By combining breadth, depth, and practical insight, this work stands as an indispensable guide for researchers, industry professionals, policymakers, and educators shaping the future of sustainable nanotechnology.

This book is an indispensable guide for anyone looking to understand how AI, machine learning, and data science are revolutionizing drug discovery, development, and delivery, offering practical insights and addressing crucial real-world applications and considerations. Data Science in Pharmaceutical Development offers a comprehensive and forward-looking exploration of how artificial intelligence, machine learning, and data science are reshaping the pharmaceutical landscape. From the earliest stages of drug discovery to advanced delivery systems and post-market surveillance, this volume bridges the gap between innovation and real-world application. Practical examples and case studies bring to life the transformative potential of AI-powered tools in accelerating research, enhancing patient outcomes, and improving efficiency throughout the pharmaceutical product lifecycle. Designed for researchers, industry professionals, and students alike, this book not only showcases cutting-edge technologies but also addresses the ethical, legal, and regulatory considerations critical to their implementation. Whether you’re navigating the complexities of clinical trials, optimizing supply chains, or seeking to understand the implications of smart drug delivery systems, this book is an indispensable guide to the future of medicine and healthcare innovation. Readers will find the book: Explores the role of AI, machine learning, and data science across the entire pharmaceutical pipeline—from drug discovery and clinical trials to smart drug delivery systems;Rich with real-world case studies and practical examples, connecting theory to implementation in modern pharmaceutical research and development;Introduces advanced topics like predictive modeling, personalized medicine, IoT, pharmacovigilance, and nanotechnology-enabled drug delivery;Highlights emerging trends, ethical considerations, and the regulatory framework surrounding AI in healthcare.Audience Research scholars, pharmacy students, pharmaceutical process engineers, and pharmacy professionals in the pharmaceutical and biopharmaceutical industry who are working in drug discovery, chemical biology, computational chemistry, medicinal chemistry, and bioinformatics.

In a world racing toward digital transformation, the need for sustainable, intelligent, and secure systems is greater than ever. This volume unites pioneering research and practical innovations in cyber-physical systems (CPS) powered by IoT, Blockchain, AI, and post-quantum cybersecurity to tackle pressing environmental, industrial, and societal challenges.Contributions from global experts cover blockchain-enabled IoT architectures, energy-aware cybersecurity, explainable AI, and federated learning for edge devices, with applications in healthcare, aviation, finance, and smart agriculture. Each chapter blends real-world insights with forward-looking strategies for resilient, scalable, and green systems.Highlights include:Integration of Blockchain, IoT, and AI for secure, scalable, and sustainable CPS.Green, energy-efficient technologies such as sustainable encryption and low-power threat detection.Advanced AI techniques—explainable AI, federated learning, deep SHAP—for cybersecurity and personalization.Applications spanning healthcare, agriculture, aviation, finance, and legal systems.Post-quantum cryptography strategies for future-proof security.Human-centered, interdisciplinary approaches emphasizing usability, observability, and ethics.Ideal for researchers, practitioners, policymakers, and students, this collection offers a comprehensive roadmap to the sustainable convergence of the physical and digital worlds—building a smarter, safer, and more eco-conscious future.