Sanjeev Kumar Gupta – författare

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties.Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies.Highlights include: Power quality enhancement by PV-UPQC for non-linear load Energy management of a nanogrid through flair of deep learning from IoT environments Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis AC power optimization techniques Artificial intelligence and machine learning techniques in power systems automation

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties.Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies.Highlights include: Power quality enhancement by PV-UPQC for non-linear load Energy management of a nanogrid through flair of deep learning from IoT environments Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis AC power optimization techniques Artificial intelligence and machine learning techniques in power systems automation

This contributed book provides a comprehensive exploration of chemical and immunological strategies in cancer therapeutics, emphasizing precision medicine approaches for targeted and effective treatments. It integrates cutting-edge advancements in drug discovery, covering a diverse range of anticancer agents, from natural compounds and metal-based complexes to synthetic inhibitors and nanocarriers.The chapters cover topics such as the rational design and optimization of cancer therapeutics, employing molecular modeling, structure-activity relationship analysis, and AI-driven drug discovery. Additionally, the book highlights immunological interventions, such as immune checkpoint inhibitors and host-directed therapies, showcasing their growing relevance in personalized cancer treatment. Readers will discover diverse perspectives from expert contributing authors, offering a balanced perspective on the potential and limitations of these compounds in cancer therapeutics.This book is intended for researchers, academics, clinicians, and industry professionals engaged in oncology, pharmacology, medicinal chemistry, and natural product research. By combining theoretical insights with practical applications, it aims to advance understanding, inspire innovation, and facilitate the translation of promising cancer therapeutics from the lab to clinical settings.

This book presents selected papers from NIELIT’s International Conference on Communication, Electronics, and Digital Technology (NICEDT 2025) held at NIELIT Ropar, Punjab, India, during 14–15 February 2025. This book is organized in two volumes and covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cybersecurity and forensic, network and mobile security, advance computing, cloud computing, quantum computing, VLSI and semiconductors, electronics system, Internet of Things, robotics and automations, blockchain and software technology, and digital technologies for future.