Umesh Gupta – författare

Nanotechnology-Based Targeted Drug Delivery Systems for Brain Tumors addresses brain anatomy and tumors and the progress and challenges in delivering drugs across the blood brain barrier. Several chapters are devoted to the latest technologies and advances in nanotechnology, along with practical solutions on how to design more effective nanocarriers for drug and gene delivery. This valuable resource prepares readers to develop novel drug delivery systems for the treatment of brain tumors that further promote the latest nanomedical technologies.

Nano Drug Delivery Strategies for the Treatment of Cancers discusses several current and promising approaches for the diagnosis and treatment of cancer by using the most recent developments in nanomedical technologies. The book presents introductory information about the biology of different types of cancer in order to provide the reader with knowledge on their specificities. In addition, it discusses various novel drug delivery systems, detailing their functionalities, expected outcomes and future developments in the field, focusing on brain, mouth and throat, breast, lung, liver, pancreas, stomach, colon, bool, skin and prostate cancers.

The book is a valuable source for cancer researchers, oncologists, pharmacologists and nanotechnologists who are interested in novel drug delivery systems and devices for treatment of various types of cancer that take advantage of recent advances in this exciting field.

Nanomedicine-Based Approaches for the Treatment of Dementia explores a wide range of promising approaches for the diagnosis and treatment of dementia. The book begins with introductory sections on dementia and brain ailments that are followed by further chapters that discuss detailed information about challenges of drug delivery across the blood-brain barrier as well as the current status. This book helps readers design and develop novel drug delivery systems and devices for the treatment of dementia that take advantage of recent advances in nanomedical technologies.

Numerous drug delivery systems have been developed recently for dementia. Unfortunately, most of them are ineffective since dementia is not a single disease. It is an umbrella term for several neurodegenerative conditions which alter brain functions. Due to this, there is an urgent need for innovative technologies/nano drug delivery systems to improve the targeting and delivery of therapeutic as well as diagnostic agents specifically for treating dementia.

Molecular Pharmaceutics and Nano Drug Delivery: Fundamentals and Challenges provides a thorough resource for both beginners and established scientists, bringing fundamental knowledge about key challenges of these carriers down to the molecular level. The book satisfies the need of availability of literature at single platform with the detailed knowledge to understand crucial aspects, such as regulatory, clinical, toxicological and the formulation requirements of these carriers. This is a valuable resource for graduates, pharmaceutical researchers and anyone working on aspects of pharmaceutics, molecular pharmaceutics and nano-drug/gene delivery.

So called 'novel drug delivery systems' are numerous, with each having different approaches to their production, characterization and evaluation. The proper understanding of these dosage forms, as well as their critical attributes such as toxicity and regulatory requirements are aspects which researchers should know before they begin working on these carriers. This book provides this critical information.

This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.

This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.

Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems'' safety, reliability, and performance while improving user trust and acceptance.

This book:

The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI''s key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems'' safety, reliability, and performance while improving user trust and acceptance.

This book:

The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI''s key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.

This book explores the intersection of artificial intelligence and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches.

Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of generative AI, generative AI models, and possible areas in healthcare where generative AI can work. It discusses how generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes.

The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science.

This book explores the intersection of artificial intelligence and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches.

Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of generative AI, generative AI models, and possible areas in healthcare where generative AI can work. It discusses how generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes.

The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science.