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.

Addresses the progress and challenges inherent in delivering drugs across the blood brain barrier and offers strategies to maximize effectiveness Draws upon the experience and expertise of international scientists working in the fields of drug delivery and nanomedicine Considers the future possibilities of nanotechnology for delivering nanocarriers that better diagnose and treat brain tumors

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.

Discusses a wide range of promising approaches for the diagnosis and treatment of cancer using the latest advancement in cutting-edge nanomedical technologies Provides foundational information on different types of cancer and their biology to help the reader choose the best nano drug delivery system for patients Presents novel drug delivery systems based on nanoparticles, microparticles, liposomes, self-assembling Micelles and block copolymer micelles

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.

Explores promising approaches for the diagnosis and treatment of dementia Discusses advances in cutting-edge nanomedical technologies Focuses on the design, synthesis and application of nanocarriers in drug delivery

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.

Provides a conceptual understanding of Molecular Pharmaceutics and drug/gene delivery systems of biological origin Presents a detailed description and discussion on nanotechnological carriers, from basics to advances, including gene delivery and protein-oriented delivery Includes regulatory and toxicological requirements for novel drug delivery systems

Functionalized Drug Delivery Systems delves into the potential of various functionalized drug delivery systems, such as polymeric micelles, dendrimers, metallic nanoparticles, and SMEDDS. It offers a comprehensive guide from basic concepts to advanced surface engineering techniques. The book covers liposomes, dendrimers, metal nanoparticles, polymeric micelles, niosomes, and ethosomes, concluding with a chapter on the regulatory framework. This invaluable resource is perfect for pharmaceutical researchers and graduate students keen on advancing their understanding and application of these innovative drug delivery methods. The book also provides consolidated literature reviews, making it a foundational text for new researchers.Its chapters are dedicated to specific techniques, offering detailed insights into each method. Readers will gain knowledge on improving drug absorption and targeted delivery, supported by practical examples and case studies. The final chapter on regulatory frameworks ensures compliance and best practices in developing functionalized drug delivery systems. This book is an essential read for anyone in the field of pharmaceutical research.Provides a conceptual understanding of functionalized drug delivery systemsPresents a detailed description and discussion on different functionalization techniques of nanotechnological carriersDescribes regulatory as well as toxicological requirements of functionalized drug delivery systems

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision makingShowcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systemsDiscusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systemsPresents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience.Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysisThis reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

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:Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.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:Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems.Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles.Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making.Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control.Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles.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.