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Handbook of Research on Computational Intelligence Applications in Bioinformatics
Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.
Development of Employability Skills Through Pragmatic Assessment of Student Learning Outcomes
The COVID-19 pandemic has shifted the teaching-learning experience dramatically, creating an opportunity for new online and blended learning techniques and tools. This has also added a new dimension to practices and methods already adopted for achieving sustainable development goals (SDGs) within education. This requires a new paradigm shift in the teaching-learning process through the systemic and pragmatic assessment of student learning outcomes so that employability skills and competence can be developed in students for competing at the global level. Development of Employability Skills Through Pragmatic Assessment of Student Learning Outcomes discusses the best practices in the assessment...
Artificial Intelligence of Things
These two volumes constitute the revised selected papers of First International Conference, ICAIoT 2023, held in Chandigarh, India, during March 30–31, 2023. The 47 full papers and the 10 short papers included in this volume were carefully reviewed and selected from 401 submissions. The two books focus on research issues, opportunities and challenges of AI and IoT applications. They present the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of AI algorithms implementation in IoT Systems
Solar Photovoltaic Power Plants
This book discusses control and optimization techniques in the broadest sense, covering new theoretical results and the applications of newly developed methods for PV systems. Going beyond classical control techniques, it promotes the use of more efficient control and optimization strategies based on linearized models and purely continuous (or discrete) models. These new strategies not only enhance the performance of the PV systems, but also decrease the cost per kilowatt-hour generated.
Sustainable Energy and Technological Advancements
This book contains selected papers presented at Second International Symposium on Sustainable Energy and Technological Advancements (ISSETA 2023), organized by the Department of Electrical Engineering, NIT Meghalaya, Shillong, India, during February 24–25, 2023. The topics covered in the book are the cutting-edge research involved in sustainable energy technologies, smart building technology, integration and application of multiple energy sources; advanced power converter topologies and their modulation techniques; and information and communication technologies for smart micro-grids.
Intelligent Computing in Control and Communication
This book consists of peer-reviewed papers presented at the First International Conference on Intelligent Computing in Control and Communication (ICCC 2020). It comprises interesting topics in the field of applications of control engineering, communication and computing technology. As the current world is witnessing the use of various intelligent techniques for their independent problem solving, so this book may have a wide importance for all range of researchers and scholars. The book serves as a reference for researchers, professionals and students from across electrical, electronic and computer engineering disciplines.
Model-Based Reinforcement Learning
Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning co...
