Patricia Melin,Ivette Miramontes,German Prado Arechiga
Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis
Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis
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- More about Nature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis
This book explores the use of soft computing techniques for medical diagnosis, focusing on optimizing bio-inspired algorithms for accurate and efficient diagnosis. It presents experiments with fuzzy classifiers and dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, making it valuable for graduate students, researchers, and practitioners in engineering and medicine.
Format: Paperback / softback
Length: 127 pages
Publication date: 07 August 2021
Publisher: Springer Nature Switzerland AG
This comprehensive book delves into the effective utilization of diverse soft computing techniques, with a primary focus on optimizing them for accurate and efficient medical diagnosis. The proposed method offers a precise and timely assessment of the risk of a person developing a specific disease, while also demonstrating its adaptability to diagnose various diseases. The book showcases extensive experimentation conducted based on various bio-inspired algorithms, including bird swarm algorithm, flower pollination algorithms, and others.
In particular, these optimizations were directed towards designing fuzzy classifiers for the nocturnal blood pressure profile and heart rate level. Furthermore, the architecture of the artificial neural networks employed in the model was meticulously optimized to achieve optimal results.
To further enhance the model's performance, dynamic parameter adaptation of the bird swarm algorithm was explored using fuzzy inference systems. This involved conducting diverse experiments, where mathematical functions and a monolithic neural network were optimized to compare the outcomes with the original algorithm.
The book holds immense appeal for graduate students pursuing engineering and medicine, as well as researchers and professors actively engaged in proposing and developing innovative intelligent models for medical diagnosis. Additionally, it holds significance for individuals working on metaheuristic algorithms and their applications in the field of medicine.
Weight: 221g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030822187
Edition number: 1st ed. 2022
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