"Recent Trends in Artificial Neural Networks: from Training to Prediction" ed. by Ali Sadollah, Carlos M. Travieso-Gonzalez
ITExLi | 2020 | ISBN: 1789854202 9781789854206 1789854199 9781789854190 1789858593 9781789858594 | 124 pages | PDF | 14 MB
ITExLi | 2020 | ISBN: 1789854202 9781789854206 1789854199 9781789854190 1789858593 9781789858594 | 124 pages | PDF | 14 MB
This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance.
Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy.
Contents
1.Time Series from Clustering: An Approach to Forecast Crime Patterns
2.Encountered Problems of Time Series with Neural Networks: Models and Architectures
3.Electric Transmission Network Expansion Planning with the Metaheuristic Variable Neighbourhood Search
4.An Improved Algorithm for Optimising the Production of Biochemical Systems
5.Object Recognition Using Convolutional Neural Networks
6.Prediction of Wave Energy Potential in India: A Fuzzy-ANN Approach
7.Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures
8.Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison
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