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• Introduction.- Linear Bi-level Programming.- Possibility Theory and Fuzzy Optimization.- Fuzzy Interactive Multi-level Decision Making.- Aggregation of Fuzzy Systems in Multi-level Decisions.- Multi-level Optimization by Fuzzy Dynamic Programming.- Auction Mechanisms for Solving Multi-level Programming.- Neural Networks for Solving Multi-level Programming.- Metaheuristics for Multi-level Optimization.
• (source: Nielsen Book Data)

This book offers a comprehensive overview of cutting-edge approaches for decision-making in hierarchical organizations. It presents soft-computing-based techniques, including fuzzy sets, neural networks, genetic algorithms and particle swarm optimization, and shows how these approaches can be effectively used to deal with problems typical of this kind of organization. After introducing the main classical approaches applied to multiple-level programming, the book describes a set of soft-computing techniques, demonstrating their advantages in providing more efficient solutions to hierarchical decision-making problems compared to the classical methods. Based on the book Fuzzy and Multi-Level Decision Making (Springer, 2001) by Lee E.S and Shih, H., this second edition has been expanded to include the most recent findings and methods and a broader spectrum of soft computing approaches. All the algorithms are presented in detail, together with a wealth of practical examples and solutions to real-world problems, providing students, researchers and professionals with a timely, practice-oriented reference guide to the area of interactive fuzzy decision making, multi-level programming and hierarchical optimization.
(source: Nielsen Book Data)

• Decision-Making
• Fuzziness and Probability
• Fuzzy Recognition/Classification and Image Processing
• Fuzzy Control and Intelligent Monitoring
• Neural Networks: Construction and Application
• Applications
• Time Series and Data Analysis.

The book reports on the latest advances and challenges of soft computing. It gathers original scientific contributions written by top scientists in the fieldand covering theories, methods and applications in a number of research areas related to soft-computing, such as decision-making, probabilistic reasoning, image processing, control, neural networks and data analysis.

• Statistical Learning by Natural Gradient Descent
• Granular Networks and Granular Computing
• Learning and Decision-Making in the Framework of Fuzzy Lattices
• Lazy Learning: A Logical Method for Supervised Learning
• Active Learning in Neural Networks
• Knowledge Extraction from Reinforcement Learning
• Reinforcement Learning for Fuzzy Agents: Application to a Pighouse Environment Control
• Performance Comparisons of Neural Networks and Machine Learning Techniques: A Critical Assessment of the Methodology
• Digital Systems Design Through Learning
• Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms
• An Integer Programming Approach to Inductive Learning Using Genetic and Greedy Algorithms
• Using Unlabeled Data for Learning Classification Problems
• Problems of Rule Induction from Preterm Birth Data
• Reduction of Discriminant Rules Based on Frequent Item Set Calculation
• Deriving a Concise Description of Non-Self Patterns in an Artificial Immune System.

Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.

This textbook provides an introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
(source: Nielsen Book Data)

• 1. Introduction *
• 2. Fuzzy Logic *
• 3. Fuzzy Control *
• 4. Artificial Neural Networks *
• 5. Neural Networks for Modeling and Controlling Dynamical Systems *
• 6. Evolutionary Optimisation Algorithm *
• 7. Cellular Neural Networks *
• 8. Complex Dynamics and Cellular Neural Networks *
• 9. Neuro-fuzzy Networks *
• 10. Fuzzy Cellular Neural Network
• s11. Fuzzy Systems Optimisation by Means of Genetic Algorithms *
• 12. Neuro-fuzzy Strategies for Monitoring Urban Traffic Noise *
• 13. Modeling and Control of a Robot for Picking Citrus Fruit *
• 14. Modeling and Control of Rapid Thermal Process Systems *
• 15. A Neural Network to Predict Air Pollution in Industrial Area
• s16. Conclusions.
• (source: Nielsen Book Data)

In the last decade, new artificial intelligence methods for the modelling and control of complex systems, namely neural networks, fuzzy logic and probabilistic reasoning have drawn the interest of researchers and engineers. Recently, the advantages achievable by using combinations of these methods, which have independent origin and evolution, have been pointed out, generating a new paradigm which is now termed "soft-computing". This new methodology subsumes the capabilities of neural networks for modelling non-linear systems and for solving classification problems, the power of fuzzy-logic to represent approximate or heuristic reasoning and the large capabilities of evolutionary computation for problem optimisation. The book presents a clear understanding of a new type of computation system, the cellular neural network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox. The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also described. These aspects are of particular interest owing to their growing interest for research and application purposes. Specific topics covered in the text include: fuzzy logic, control and neural networks; artificial neural networks and their application in the modelling and control of dynamical systems; evolutionary optimisation algorithms; complex dynamics and cellular neural networks; and, applications in urban traffic noise monitoring, robot control and rapid thermal process systems.
(source: Nielsen Book Data)

• Neural networks in systems identification and control - supervised learning in multilayer perceptions - the back-propagation algorithm
• identification of two-dimensional state space discrete systems using neural networks
• neural networks for control
• neuro-based adaptive regulator
• local model networks and self-tuning predictive control
• fuzzy and neuro-fuzzy systems in modelling, control and robot path planning - an on-line self constructing fuzzy modelling architecture based on neural and fuzzy concepts and techniques
• neuro-fuzzy model-based control
• fuzzy and neurofuzzy approaches to mobile robot path and motion planning under uncertainty
• genetic-evolutionary algorithms - a tutorial overview of genetic algorithms and their applications
• results from a variety of genetic algorithm applications showing the robustness of the approach
• evolutionary algorithms in computer-aided design of integrated circuits
• soft computing applications - soft data fusion
• application of neural networks to computer gaming
• coherent neural networks and their applications to control and signal processing
• neural, fuzzy and evolutionary reinforcement learning systems - an application case study
• neural networks in industrial and environmental applications.
• (source: Nielsen Book Data)

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.
(source: Nielsen Book Data)

• Theory and methods
• NAARPreC: A Novel Approach for Adaptive Resource Prediction in Cloud
• One True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross Lingual Transfer
• FedCLUS: Federated Clustering from Distributed Homogeneous Data
• On Language Clustering: Non-parametric Statistical Approach
• Method Agnostic Model Class Reliance (MAMCR) Explanation of Multiple Machine Learning Models
• Association Rules based Feature Extraction for Deep Learning Classification
• Global Thresholding Technique for Basal Ganglia Segmentation from Positron Emission Tomography Images
• Oversampling Methods to Handle the Class Imbalance Problem: A Review
• Analog Implementation of Neural Network
• Designing Fog Device Network for Digitization of University Campus
• Word Sense Disambiguation from English to Indic language: Approaches and Opportunities
• Explainable AI for Predictive Analytics on Employee Attrition
• Graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images
• Performance Analysis of Cache Memory in CPU
• Extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation
• Automatic Mapping of Deciduous and Evergreen Forest by Using Machine Learning and Satellite Imagery
• Systems and Applications
• RIN: Towards A Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection
• SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence
• Deep Learning based Model for Fundus Retinal Image Classification
• Reliable Network-Packet Binary Classification
• SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-Source Aggregation and Recommendation of News with a focus on Personalization
• Extraction and Analysis of Speech Emotion Features using Hybrid Punjabi Audio Dataset
• Human Activity Recognition in Videos using Deep Learning
• Brain Tumor Classification using VGG-16 and MobileNetV2 Deep Learning Techniques on Magnetic Resonance Images (MRI)
• Five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms
• An Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database
• A Method for Workflow Segmentation and action prediction from video data - AR Content
• Convolutional Neural Network Approach for Iris Segmentation
• Reinforcement Learning Algorithms for Effective Resource Management in Cloud Computing
• Corpus Building for Hate Speech Detection of Gujarati Language
• Intrinsic use of Genetic Optimizer in CNN Towards Efficient Image Classification
• A Software System for Smart Course Planning
• Meetei Mayek Natural Scene Character Recognition using CNN
• Utilization of Data Mining Classification Technique to Predict the Food Security Status of Wheat
• Hybrid Techniques
• QoS-Aware Service Placement for Fog Integrated Cloud using Modified Neuro-Fuzzy Approach
• Optimizing Public Grievance Detection Accuracy through Hyperparameter Tuning of Random Forest and Hybrid Model. .

This book constitutes the refereed proceedings of the 4th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2022, held in Changa, Anand, India during December 9–10, 2022. The 33 full papers and 3 short papers included in this book were carefully reviewed and selected from 342 submissions. They were organized in topical sections as follows: Theory and Methods; Systems and Applications; and Hybrid Techniques.

• Chapter 1. Benchmarking state-of-the-art methodologies for optic disc segmentation
• Chapter 2. Automated Student Emotion Analysis During Online Classes using Convolutional Neural Network
• Chapter 3. Transfer Learning based Malware Classification
• Chapter 4. A Study on Metric-Based and Initialization-Based Methods for Few-Shot Image Classification
• Chapter 5. A Fast and Efficient Methods for Eye pre-processing and DR Level Detection
• Chapter 6. A deep neural model CNN-LSTM network for automated sleep staging based on a single-channel EEG signal
• Chapter 7. An Ensemble Model for Gait Classification in Children and Adolescent with Cerebral Palsy: A Low-Cost Approach
• Chapter 8. Imbalanced Learning of Regular Grammar for DFA Extraction from LSTM Architecture
• Chapter 9. Medical Prescription Label Reading using Computer Vision and Deep Learning
• Chapter 10. Autoencoder-based Deep Neural Architecture for Epileptic Seizures Classification
• Chapter 11. Stock Market Prediction using Deep Learning Techniques for Short and Long Horizon
• Chapter 12. Improved CNN Model for Breast Cancer Classification
• Chapter 13. Performance Assessment of Normalization in CNN with Retinal Image Segmentation
• Chapter 14. A novel multi-day ahead index price forecast using multi-output based deep learning system
• Chapter 15. Automatic Retinal Vessel Segmentation using BTLBO. etc.

This book provides an insight into the 11th International Conference on Soft Computing for Problem Solving (SocProS 2022). This international conference is a joint technical collaboration of the Soft Computing Research Society and the Indian Institute of Technology Mandi. This book presents the latest achievements and innovations in the interdisciplinary areas of Soft Computing, Machine Learning, and Data Science. It brings together the researchers, engineers, and practitioners to discuss thought-provoking developments and challenges, in order to select potential future directions. It covers original research papers in the areas including but not limited to algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and healthcare, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

• Artificial Intelligence Techniques and Applications
• Computing and Optimization
• Data Analytics and Technologies
• Data Mining and Image Processing
• Mathematical and Statistical Learning.

This book constitutes the refereed proceedings of the 7th International Conference on Soft Computing in Data Science, SCDS 2023, which was held virtually in January 2023. The 21 revised full papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections on artificial intelligence techniques and applications; computing and optimization; data analytics and technologies; data mining and image processing; mathematical and statistical learning.

This book constitutes the refereed proceedings of the Third International Conference on Soft Computing and its Engineering Applications, icSoftComp 2021, held in Changa, India, in December 2021. Due to the COVID-19 pandemic the conference was held online. The 29 full papers and 4 short papers presented were carefully reviewed and selected from 247 submissions. The papers present recent research on theory and applications in fuzzy computing, neuro computing, and evolutionary computing.

This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing, and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2021), organized online. The book offers valuable insights into soft computing for teachers and researchers alike; the book will inspire further research in this dynamic field.
(source: Nielsen Book Data)

• Modified Artificial Potential Field Approaches for Mobile Robot Navigation in Unknown Environments
• ASIC Implementation of Fixed-point iterative, Parallel and Pipeline CORDIC Algorithm
• Adaptive Sensor Ranking based on Utility Using Logistic Regression
• A Hybrid Approach for Intrusion Detection System
• Inspection of Crop-Weed Image Database Using Kapurs Entropy and Spider-Monkey-Optimization
• Implementation of Fuzzy based Multi-Carrier and Phase-Shifted PWM Symmetrical Cascaded H-Bridge Multilevel Inverter
• Derived Shape Features for Brain Hemorrhage Classification
• Prediction of Crime Rate Using Data Clustering Technique
• Analysis of Fractional Order Deterministic HIV/AIDS Model During Drug Therapy Treatment
• Comparison of Performance of Four Element Microstrip Array Antenna Using Electromagnetic Band Gap Structures
• Optimal Renewable Energy Resource Based Distributed Generation Allocation in a Radial Distribution System.

This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.

• Study of Squeeze Film in a Ferrofluid Lubricated Longitudinally Rough Rotating Plates
• Study of Longitudinal Roughness on Hydromagnetic Squeeze Film Between Conducting Rotating Circular Plates
• Fuzzy Logic Based Packet Dropping Detection Approach for Mobile Ad-hoc Wireless Network
• Optimization of Parking Lot Area in Smart Cities Using Game Theory
• Analysis of Spatial Domain Image Steganography Based on Pixel Value Differencing Method
• Passive Bandwidth Estimation Techniques for QoS Routing in Wireless LANs
• Toward Automatic Scheduling Algorithm With Hash-based Priority Selection Strategy
• Agent-Based Modeling of the Adaptive Immune System Using Netlogo Simulation Tool
• Object Classification from Shape Detection
• Analysis of Road Networks Using the Louvian Community Detection Algorithm
• Congestion Control Using Fuzzy Based Node Reliabilityand Rate Control
• Impact of Inertia Weight and Cognitive and Social Constants in Obtaining Best Mean Fitness Value for PSO.

This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.

• A Brain like Computer Made of Time crystal: Could a Metric of Prime alone Replace a User and Alleviate Programming Forever?.- Optimum Selection of Energy-Efficient Material: A MCDM Based Distance Approach.- Role of Sodium, Potassium and Synaptic Conductance in STN-GPe Model of Basal Ganglia in Parkinson Disease.- A New Hybrid Algorithm Using Chaos Enhanced Differential Evolution For Loss Minimization With Improvement of Voltage Profile of Distribution Systems.- Fractal and Periodical Biological Antennas: Hidden Topologies in DNA, Wasps and Retina in the Eye.- Efficient Multiprocessor Scheduling Using Water Cycle Algorithm.- Estimating Software Reliability Growth Model Parameters Using Opposition Based Shuffled Frog-Leaping Algorithm.
• (source: Nielsen Book Data)

This book provides a reference guide for researchers, scientists and industrialists working in the area of soft computing, and highlights the latest advances in and applications of soft computing techniques in multidisciplinary areas. Gathering papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2016), which was held in Jaipur, Rajasthan, India, on December 28-30, 2016, it focuses on applying soft computing to solve real-life problems arising in various domains, from medical and healthcare to supply chain management, image processing and cryptanalysis. The term soft computing represents an umbrella term for computational techniques like fuzzy logic, neural networks and nature inspired algorithms. In the past few decades, there has been an exponential rise in the application of soft computing techniques to address complex and intricate problems in diverse spheres of life. The versatility of these techniques has made them a favourite among scientists and researchers alike.
(source: Nielsen Book Data)

• Chapter 1. A CNN-based Approach for Facial Emotion Detection
• Chapter 2. Implementation of Artificial Neural Network for Demanufacturing Operation in the Rail Industry
• Chapter 3. Diversified Recommendation Generation using Graph Convolution Neural Network
• Chapter 4. Brain Tumor detection with GLCM feature extraction and hybrid classification approach
• Chapter 5. Optimization of an inventory model with selling price and stock sensitive demand along with trade credit policy
• Chapter 6. A new family of generalized Euler-Genocchi polynomials associated with Hermite polynomials
• Chapter 7. 6G-Enabled Internet of Medical Things
• Chapter 8. An Inductively Degenerated LNA for ISM Applications: Design & Performance Comparison
• Chapter 9. Prostate Cancer Risk Analysis Using Artificial Neural Network
• Chapter 10. On two bivariate kinds of (p; q)-Euler polynomials
• Chapter 11. Different stages of Watermelon Diseases Detection Using optimized CNN
• Chapter 12. Machine Learning: An Analytical Approach for Pattern Detection in Diabetes
• Chapter 13. A Dynamic Weighted Federated Learning for Android Malware Classification
• Chapter 14. Entropy measure for the linguistic q-rung orthopair fuzzy set
• Chapter 15. Empirical Analysis of Unsupervised Link Prediction Algorithms in Wighted Networks.

This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and health care, to supply chain management, image processing and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2022), held at University Institute of Technology, Himachal Pradesh University Shimla, Himachal Pradesh, India. The book offers valuable insights into soft computing for teachers and researchers alike; the book inspires further research in this dynamic field.

This book presents the proceedings of the Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST '05), May 25-27, 2005, Muroran, Japan. It brings together the original work of international soft computing/computational intelligence researchers, developers, practitioners, and users. This proceedings provide contributions to all areas of soft computing including intelligent hybrid systems, agent-based systems, intelligent data mining, decision support systems, cognitive and reactive distributed artificial intelligence (AI), internet modelling, human interface, and applications in science and technology.
(source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the AFFS International Conference on Fuzzy Systems, AFFS 2002, held in Calcutta, India, in Feburary 2002. The book presents 74 papers from 19 different countries selected out of approximately twice as many submissions. Among the topics addressed are fuzzy systems, soft computing, neural networks, pattern recognition, image processing, evolutionary computation, and data mining.
(source: Nielsen Book Data)