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Metaheuristics in Machine Learning: Theory and Applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Metaheuristics in Machine Learning: Theory and Applications/ edited by Diego Oliva, Essam H. Houssein, Salvador Hinojosa.
其他作者:	Hinojosa, Salvador.
面頁冊數:	XIV, 769 p. 303 illus., 226 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-70542-8
ISBN:	9783030705428

Metaheuristics in Machine Learning: Theory and Applications
Metaheuristics in Machine Learning: Theory and Applications[electronic resource] /edited by Diego Oliva, Essam H. Houssein, Salvador Hinojosa. - 1st ed. 2021. - XIV, 769 p. 303 illus., 226 illus. in color.online resource. - Studies in Computational Intelligence,9671860-9503 ;. - Studies in Computational Intelligence,564.

Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms -- Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms -- Diagnosis of collateral effects in climate change through the identification of leaf damage using a novel heuristics and machine learning framework -- Feature engineering for Machine Learning and Deep Learning assisted Wireless Communication -- Genetic operators and their impact on the training of deep neural networks -- Implementation of metaheuristics with Extreme Learning Machines -- Architecture optimization of convolutional neural networks by micro genetic algorithms -- Optimising Connection Weights in Neural Networks using a Memetic Algorithm Incorporating Chaos Theory -- A review of metaheuristic optimization algorithms for wireless sensor networks -- A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics -- A Review of multi-level thresholding image segmentation using nature-inspired optimization algorithms -- Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering -- Variable Mesh Optimization for Continuous Optimization and Multimodal Problems -- Traffic control using image processing and deep learning techniques -- Drug Design and Discovery: Theory,Applications, Open Issues and Challenges -- Thresholding algorithm applied to Chest X-Ray images with Pneumonia -- Artificial neural networks for stock market prediction: a comprehensive review -- Image classification with Convolutional Neural Networks -- Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico -- Machine Reading Comprehension (LSTM) Review (state of art) -- A Survey of Metaheuristic Algorithms for Solving Optimization Problems -- Integrating metaheuristic algorithms and minimum cross entropy for image segmentation in mist conditions -- A Machine Learning application for Particle Physics: Mexico’s involvement in the Hyper- Kamiokande observatory -- A novel metaheuristic approach for Image Contrast Enhancement based on gray-scale mapping -- Geospatial Data Mining Techniques Survey -- Integration of Internet of Things and cloud computing for Cardiac health recognition -- Combinatorial Optimization for Artificial Intelligence Enabled Mobile Network Automation -- Performance Optimization of PID Controller based on Parameters Estimation using Meta-Heuristic Techniques : A Comparative Study -- Solar Irradiation Changes Detection for Photovoltaic Systems through ANN trained with a Metaheuristic Algorithm -- Genetic Algorithm based Global and Local Feature Selection Approach for Handwritten Numeral Recognition.

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

ISBN: 9783030705428

Standard No.: 10.1007/978-3-030-70542-8doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Metaheuristics in Machine Learning: Theory and Applications
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