Bhargav Appasani – författare

The book covers the emerging communication and computational technologies for future cyber-physical systems and discusses the security of in-vehicle communication protocols using automotive embedded systems, presenting an in-depth analysis across various domains, such as manufacturing, transportation, health-care, and smart cities.

This book:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, and information technology.

The book covers the emerging communication and computational technologies for future cyber-physical systems and discusses the security of in-vehicle communication protocols using automotive embedded systems, presenting an in-depth analysis across various domains, such as manufacturing, transportation, health-care, and smart cities.

This book:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, and information technology.

Engineering Applications of AI for Demand Forecasting explores how Artificial Intelligence enhances prediction accuracy across modern engineering systems. As industries move toward Industry 4.0, the book highlights the role of AI in processing large, dynamic datasets for smarter decision-making. This book brings together contemporary research that demonstrates AI’s ability to enhance precision, efficiency, and adaptability in diverse forecasting environments. By covering cybersecurity analytics, anomaly detection, logistics forecasting, sustainable supply chain management, and predictive maintenance, it demonstrates AI’s versatility in complex environments. The book also showcases AI applications in renewable energy forecasting, peak load prediction, smart meter analytics, and prosumer-driven demand modeling. Combining theory with practical case studies, it serves as a valuable resource for engineers, researchers, practitioners, and students.

Engineering Applications of AI for Demand Forecasting explores how Artificial Intelligence enhances prediction accuracy across modern engineering systems. As industries move toward Industry 4.0, the book highlights the role of AI in processing large, dynamic datasets for smarter decision-making. This book brings together contemporary research that demonstrates AI’s ability to enhance precision, efficiency, and adaptability in diverse forecasting environments. By covering cybersecurity analytics, anomaly detection, logistics forecasting, sustainable supply chain management, and predictive maintenance, it demonstrates AI’s versatility in complex environments. The book also showcases AI applications in renewable energy forecasting, peak load prediction, smart meter analytics, and prosumer-driven demand modeling. Combining theory with practical case studies, it serves as a valuable resource for engineers, researchers, practitioners, and students.

This book is the first on Smart Grid 3.0.

The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0. It offers new control solutions, architectures and energy management strategies that are based on artificial intelligence and deep learning techniques.

The book details the hardware and software implementation of fault identification or detection based on synchrophasor data and machine learning. It also discusses blockchain architectures for smart grid applications such as electric vehicles, home automation and automatic metering infrastructure.