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Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis

application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible;

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Soft Computing with NeuroFuzzy Systems

recurrent neuro-fuzzy systems for fault prognosis; and neuro-fuzzy model for QoS based selection of web service. Section 2 focuses on adaptive;

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New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension

interested in applying soft compu-ting techniques, such as neural networks, fuzzy logic and genetic algorithms, in medical diagnosis, but also;

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Deep Neuro-Fuzzy Systems with Python

model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system;

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Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis

professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people;

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EMG Signals Characterization in Three States of Contraction by Fuzzy Network and

neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and;

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Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital;

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Applied Soft Computing

, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI;

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms

This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

applications of fuzzy logic, neural networks and hybrid intelligent systems in medical contexts. Finally, the fifth part focuses on applications;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

applications of fuzzy logic, neural networks and hybrid intelligent systems in medical contexts. Finally, the fifth part focuses on applications;

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Soft Computing

computing algorithms and fuzzy information processing models by developing a library of universal analytic models for fuzzy arithmetic operations;

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Hybrid Artificial Intelligence and IoT in Healthcare

facility by mobility solutions are discussed in detail. It also discusses hybrid AI with bio-inspired techniques, genetic algorithm, neuro-fuzzy;

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A Hybrid Approach for Power Plant Fault Diagnostics

presented were developed by applying Neuro-Fuzzy (NF) methods. The book offers a valuable resource for researchers and practicing engineers alike.;

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms

We describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid;

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Computer Science Advances

chapter presents neuro-fuzzy and cognitive researches for the development of objects on the basis of location and territory. The second chapter;

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply;

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Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

This book describes recent advances on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and;

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Fuzzy Information Processing

, including theory and applications of fuzzy numbers and sets, fuzzy logic, fuzzy inference systems, fuzzy clustering, fuzzy pattern classification;

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Deep Learning for Chest Radiographs

, feature dimensionality reduction, and classification using support vector machine, neuro-fuzzy classifiers Covers the various;

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Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques

models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid;

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Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques

models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

Equalizers are present in all forms of communication systems. Neuro-Fuzzy Equalizers for Mobile Cellular Channels details the modeling of a;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

Equalizers are present in all forms of communication systems. Neuro-Fuzzy Equalizers for Mobile Cellular Channels details the modeling of a;

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An Introduction to Fuzzy Sets

Wilks' theorem to model the hybrid structure of randomness and fuzziness modeling. In closing, a standard fuzzy arithmetic method is used for;

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Handbook of Decision Support Systems for Neurological Disorders

diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available;

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New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic

and literature references based on fuzzy logic. Nowadays, the evolution of intelligence systems for decision making has been reached;

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