Applied Soft Computing

This new volume explores a variety of modern techniques that deal with estimated models and give resolutions to complex real-life issues. Soft computing has played a crucial role not only with theoretical paradigms but is also popular for its pivotal role for designing a large variety of expert systems and artificial intelligence-based applications. Involving the concepts and practices of soft computing in conjunction with other frontier research domains, this book begins with the basics and goes on to explore a variety of modern applications of soft computing in areas such as approximate reasoning, artificial neural networks, Bayesian networks, big data analytics, bioinformatics, cloud computing, control systems, data mining, functional approximation, fuzzy logic, genetic and evolutionary algorithms, hybrid models, machine learning, metaheuristics, neuro fuzzy system, optimization, randomized searches, and swarm intelligence. This book will be helpful to a wide range of readers who wish to learn applications of soft computing approaches. It will be useful for academicians, researchers, students, and machine learning experts who use soft computing techniques and algorithms to develop cutting-edge artificial intelligence-based applications.

This volume presents the proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications, held on the World Wide Web in 2004. It includes lectures, original papers and tutorials presented during the conference. The book brings together outstanding research and developments in soft computing, including evolutionary computation, fuzzy logic, neural networks, and their fusion, and its applications in science and technology.

This new volume explores a variety of modern techniques that deal with estimated models and give resolutions to complex real-life issues. Soft computing has played a crucial role not only with theoretical paradigms but is also popular for its pivotal role for designing a large variety of expert systems and artificial intelligence-based applications. Involving the concepts and practices of soft computing in conjunction with other frontier research domains, this book begins with the basics and goes on to explore a variety of modern applications of soft computing in areas such as approximate reasoning, artificial neural networks, Bayesian networks, big data analytics, bioinformatics, cloud computing, control systems, data mining, functional approximation, fuzzy logic, genetic and evolutionary algorithms, hybrid models, machine learning, metaheuristics, neuro fuzzy system, optimization, randomized searches, and swarm intelligence. This book will be helpful to a wide range of readers who wish to learn applications of soft computing approaches. It will be useful for academicians, researchers, students, and machine learning experts who use soft computing techniques and algorithms to develop cutting-edge artificial intelligence-based applications.

Examines the integration of hardware with stand-alone PV panels and real time monitoring of factors affecting the efficiency of the photovoltaic panels Offers the real time implementation of soft computing and embedded system in the area of solar energy Discusses how soft computing plays a huge role in the prediction of efficiency of stand-alone and grid connected solar PV systems Discusses how embedded system applications with smart monitoring can control and enhance the efficiency of stand-alone and grid connected solar PV systems Explores swarm intelligence techniques for solar PV parameter estimation

This book constitutes best selected research papers presented at the International Applied Soft Computing and Communication Networks (ACN 2019) held in Trivandrum, Kerala, India during December 18 – 21, 2019. The papers are organized in topical sections on real time and multimedia communications, security and privacy, network management and software-defined networks, Internet of Things (IoT) and cyber-physical systems, intelligent distributed systems, mobile computing and vehicle communications, surveillance networks and visual intelligence, and emerging topics. The book is a reference for researchers and scientists engaged in various fields of intelligent systems.

Applied Soft Computing and Embedded System Applications in Solar Energy deals with energy systems and soft computing methods from a wide range of approaches and application perspectives. The authors examine how embedded system applications can deal with the smart monitoring and controlling of stand-alone and grid-connected solar photovoltaic (PV) systems for increased efficiency. Growth in the area of artificial intelligence with embedded system applications has led to a new era in computing, impacting almost all fields of science and engineering. Soft computing methods implemented to energy-related problems regularly face data-driven issues such as problems of optimization, classification, clustering, or prediction. The authors offer real-time implementation of soft computing and embedded system in the area of solar energy to address the issues with microgrid and smart grid projects (both renewable and non-renewable generations), energy management, and power regulation. They also discuss and examine alternative solutions for energy capacity assessment, energy efficiency systems design, as well as other specific smart grid energy system applications. The book is intended for students, professionals, and researchers in electrical and computer engineering fields, working on renewable energy resources, microgrids, and smart grid projects. Examines the integration of hardware with stand-alone PV panels and real-time monitoring of factors affecting the efficiency of the PV panels Offers real-time implementation of soft computing and embedded system in the area of solar energy Discusses how soft computing plays a huge role in the prediction of efficiency of stand-alone and grid-connected solar PV systems Discusses how embedded system applications with smart monitoring can control and enhance the efficiency of stand-alone and grid-connected solar PV systems Explores swarm intelligence techniques for solar PV parameter estimation Dr. Rupendra Kumar Pachauri is Assistant Professor – Selection Grade in the Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies (UPES), Dehradun, India. Dr. Jitendra Kumar Pandey is Professor & Head of R&D in the University of Petroleum and Energy Studies (UPES), Dehradun, India. Mr. Abhishek Sharma is working as a research scientist in the research and development department (UPES, India). Dr. Om Prakash Nautiyal is working as a scientist in Uttarakhand Science Education & Research Centre (USERC), Department of Information and Science Technology, Govt. of Uttarakhand, Dehradun, India. Prof. Mangey Ram is working as a Research Professor at Graphic Era Deemed to be University, Dehradun, India.

This book provides a better understanding of Fuzzy set theory, Fuzzy logic and Neural Networks and various other techniques seem very well suited for modeling and controlling a real system. Energy is of major importance to civilization, because it is driving force which binds human race. The estimation of energy in the form of renewable and sustainable is one of the important aspects to understand the how resources are harnessed and to predict what might happen under various possible future conditions. Using available modelling techniques to generate the best algorithms, the objective is to determine the best solution in terms of comparing the performances of the solutions through different parameters for a specific case. Consumption of Fossil fuels at a rapid pace has generated an alarming situation and with the subsequent increase in the number of vehicle the pollution level has reached well beyond human's control. This is frightening enough to observe the fact that the pollution level has surpassed all records and the need of the hour is to find an alternate fuel which can really be of great assistance in reducing the exhaust emission and augment the performance parameters of engine. Major researches are carried out on various engines to draw closer towards a realistic solution. Experiments performed on various engines are considered to be time consuming and the expenses met to perform these experiments are too costly, so the need of soft computing techniques involved in this area. Soft computing can be better described as the process to find the solution to an inexact problem. Soft computing has showed lot of potential in giving the researchers the exact solution may be in case of validating or predicting the performance and emission parameters. Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference system (ANFIS), Fuzzy Expert System (FES), Response Surface Methodology (RSM) and Support Vector Machine (SVM) are the various soft computing techniques widely used. This book focuses on to carry out the comprehensive review and various other experimental works of various researchers who have carried out the work on these various soft computing techniques on various engines with various alternative fuels On the basis of modelling techniques, time is saved to a great extent and the capital investment involved is comparably very low. Various modelling technniques are being readily used to predict the performance parameters for various engines and modelling techniques have become the readily available tool to compare and validate the experimental work being carried out by researchers to get accurate matching with the experimental data.The benefit of this issue will be at large in connecting with varieties of work done in the field of Biomass which includes wood and wood waste, municipal solid waste. Landfill gas and biogas. Ethanol, Biodiesel, Hydropower, Geothermal, Wind, Solar.Thus soft computing techniques are fast and reliable hence, they can be a substitute for conventional experiments.

Although computational intelligence and soft computing are both well-known fields, using computational intelligence and soft computing in conjunction is an emerging concept. This combination can effectively be used in practical areas of various fields of research. Applied Computational Intelligence and Soft Computing in Engineering is an essential reference work featuring the latest scholarly research on the concepts, paradigms, and algorithms of computational intelligence and its constituent methodologies such as evolutionary computation, neural networks, and fuzzy logic. Including coverage on a broad range of topics and perspectives such as cloud computing, sampling in optimization, and swarm intelligence, this publication is ideally designed for engineers, academicians, technology developers, researchers, and students seeking current research on the benefits of applying computation intelligence techniques to engineering and technology.