Shaik Vaseem Akram – författare

The book discusses the latest optimization techniques for Microgrid 4.0, including convex optimization, metaheuristic optimization, and machine learning- based optimization. It covers the latest power electronics technologies for Microgrid 4.0, including DC– DC converters, DC– AC inverters, and solid- state transformers.This book:• Discusses the significance of the Industry 4.0 revolution for enhancing the control and operations of microgrids with an objective of sustainability and resilience.• Presents the role of renewable energy in microgrids for clean and sustainable energy.• Covers topics such as advanced control, communication, optimization, power electronics, and energy storage.• Explains the latest advancements in control techniques for Microgrid 4.0, including hierarchical and decentralized control, model predictive control, and fuzzy logic control.• Highlights the economic analysis of Microgrid 4.0, including cost–benefit analysis, life cycle assessment, and business models.This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, environmental engineering, and energy engineering.

The book discusses the latest optimization techniques for Microgrid 4.0, including convex optimization, metaheuristic optimization, and machine learning- based optimization. It covers the latest power electronics technologies for Microgrid 4.0, including DC- DC converters, DC- AC inverters, and solid- state transformers.This book:* Discusses the significance of the Industry 4.0 revolution for enhancing the control and operations of microgrids with an objective of sustainability and resilience.* Presents the role of renewable energy in microgrids for clean and sustainable energy.* Covers topics such as advanced control, communication, optimization, power electronics, and energy storage.* Explains the latest advancements in control techniques for Microgrid 4.0, including hierarchical and decentralized control, model predictive control, and fuzzy logic control.* Highlights the economic analysis of Microgrid 4.0, including cost-benefit analysis, life cycle assessment, and business models.This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, environmental engineering, and energy engineering.

The book discusses the latest optimization techniques for Microgrid 4.0, including convex optimization, metaheuristic optimization, and machine learning- based optimization. It covers the latest power electronics technologies for Microgrid 4.0, including DC- DC converters, DC- AC inverters, and solid- state transformers.This book:* Discusses the significance of the Industry 4.0 revolution for enhancing the control and operations of microgrids with an objective of sustainability and resilience.* Presents the role of renewable energy in microgrids for clean and sustainable energy.* Covers topics such as advanced control, communication, optimization, power electronics, and energy storage.* Explains the latest advancements in control techniques for Microgrid 4.0, including hierarchical and decentralized control, model predictive control, and fuzzy logic control.* Highlights the economic analysis of Microgrid 4.0, including cost-benefit analysis, life cycle assessment, and business models.This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, environmental engineering, and energy engineering.

Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation. This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare. Sample topics explored in AI in Disease Detection include: Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their dataIdentification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristicsAI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenariosClinical validation processes for AI disease detection models and how they can be validated for accuracy and effectivenessDelivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.

Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection

AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.

This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.

Sample topics explored in AI in Disease Detection include:

Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their dataIdentification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristicsAI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenariosClinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness

Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.