
Handbook of Neural Computation
Description
Key Features
- Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more
- Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing
- Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods
Readership
Professional engineers, research engineers, graduate students, PhD students
Table of Contents
1. Gravitational Search Algorithm With Chaos
Seyedali Mirjalili, Amir H. Gandomi
2. Textures and Rough Sets
Murat Diker
3. Hydrological time series forecasting using three different heuristic regression techniques
Ozgur Kisi, Jalal Shiri, Vahdettin Demir
4. A reflection on image classifications for forest ecology management: Towards landscape mapping and monitoring
Anusheema Chakraborty, Kamna Sachdeva, P.K. Joshi
5. An Intelligent Hybridization of ABC and LM Algorithms with Constraint Engineering Applications
Erdem Dilmen, Selim Yilmaz, Selami Beyhan
6. Network Intrusion Detection Model based on Fuzzy-Rough Classifiers
Ashalata Panigrahi, Manas Ranjan Patra
7. Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Models
S. Metya, T. Mukhopadhyay, S. Adhikari , G. Bhattacharya
8. Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks
Taiwo Adetiloye and Anjali Awasthi
9. Object Categorization Using Adaptive Graph-based Semi-supervised Learning
F. Dornaika, A. Bosaghzadeh, H. Salmane, and Y. Ruichek
10. Hemodynamic Model Inversion by Iterative Extended Kalman Smoother
Serdar Aslan
11. Improved Sparse Approximation Models for Stochastic Computations
Tanmoy Chatterjee, Rajib Chowdhury
12. Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization
Manish Mandloi, Vimal Bhatia
13. Application of particle swarm optimization to solve robotic assembly line balancing problems
J Mukund Nilakantan, S.G. Ponnambalam, Peter Nielsen
14. The cuckoo optimization algorithm and its applications
Mohamed Arezki Mellal, Edward J. Williams
15. Hybrid Intelligent Model Based on Least Squared Support Vector Regression and Artificial Bee Colony Optimization for Time Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam
Tien Bui, Kien-Trinh Thi Bui, Quang-Thanh Bui, Chinh Van Doan, Nhat-Duc Hoang
16. Modelling the axial capacity of bored piles using multi-objective feature selection, functional network and multivariate adaptive regression spline
Ranajeet Mohanty, Shakti Suman, Sarat Kumar Das
17. Transient stability constrained optimal power flow using chaotic whale optimization algorithm
Dharmbir Prasad, Aparajita Mukherjee, V. Mukherjee
18. Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine
Nhat-Duc Hoang, Dieu Tien Bui
19. Alternating Decision Trees
Melanie Po-Leen Ooi, Hong Kuan Sok, Ye Chow Kuang, Serge Demidenko
20. Scene Understanding Using Deep Learning
Farzad Husain, Babette Dellen, Carme Torras
21. Deep Learning for Coral Classification
Ammar Mahmood, Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, Renae Hovey, Gary Kendrick, Robert B. Fisher
22. A Deep Learning Framework for Classifying Mysticete Sounds
Stavros Ntalampiras
23. Unsupervised deep learning for data-driven reliability and risk analysis of engineered systems
Peng Jiang, Mojtaba Maghrebi, Alan Crosky, Serkan Saydam
24. Applying Machine Learning Algorithms in Landslide Susceptibility Assessments
Paraskevas Tsangaratos, Ioanna Ilia
25. MDHS-LPNN: A hybrid FOREX predictor model using a Legendre polynomial Neural Network with a Modified Differential Harmony Search technique
Rajashree Dash, Pradipta Kishore Dash
26. A Neural Model of Attention and Feedback for Computing Perceived Brightness in Vision
Ashish Bakshi, Kuntal Ghosh
27. Support Vector Machine: Principles, Parameters and Applications
Raoof Gholami, Nikoo Fakhari
28. Evolving Radial Basis Function Networks using Moth-Flame Optimizer
Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili
29. Application of Fuzzy Methods in Power system Problems
Sajad Madadi, Morteza Nazari-Heris, Behnam Mohammdi-Ivatloo
30. Application of Particle Swarm Optimization Algorithm in Power system Problems
Milad Zamani-Gargari, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo
31. Optimum Design of Composite Steel-Concrete Floors Based on a Hybrid Genetic Algorithm
Mohamed G. Sahab, Vassili V. Toropov, Amir H. Gandomi
32. A Comparative Study of Image Segmentation Algorithms and Descriptors for Building Detection
F. Dornaika, A. Moujahid, Y. El Merabet, Y. Ruichek
33. Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery
Taskin Kavzoglu
Product details
- No. of pages: 658
- Language: English
- Copyright: © Academic Press 2017
- Published: July 18, 2017
- Imprint: Academic Press
- Paperback ISBN: 9780128113189
- eBook ISBN: 9780128113196
About the Editors
Pijush Samui
Affiliations and Expertise
Sanjiban Sekhar Roy
Affiliations and Expertise
Valentina Balas

Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
Avijit B. Sat Jun 26 2021
Great book
A great book for Civil, mechanical as well as applied science. An innovative process is mentioned in the image and speech recognition technique.
Pallavi M. Wed Jun 03 2020
Handbook of Neutral Computation
An interesting read about applications of neural networks across disciplines, garnering the attention of engineering academics interested in machine learning. The concepts are easy to understand.
Akansha S. Mon Mar 23 2020
Handbook of Neural Computation
In this book even complex techniques & method is very easy to understand. In a very interesting manner the book has presented the application of neural network & their use in machine learning .
Suhaila K. Mon Mar 09 2020
Easy to grasp-deep explanation
This book is a saviour from research student's point of view. It has the easiest way to demonstrate the concept and doesn't take much re-takes for grasping. Highly recommended.
Deepica Mon Mar 09 2020
Good one
Well compiled collection of high quality articles that provide insights about a variety of machine learning techniques
Anjali Wed Feb 26 2020
REVIEW
This book covers a wide range of machine learning applications in all engineering disciplines. The incorporation of soft computing that is neural computation into the arduous problems of engineering has reduced the complexity of the problems.
Nurcihan C. Thu Feb 20 2020
Dear Editor
The book is very successful and appeals to a wide audience. I will recommend it to my university's library soon.
Jagan Mon Feb 17 2020
Interesting
I would like to convey that this book is the compilation of more thought provoking topics. This book is not only the solution for the issues, but also inspiring the researchers to involve more into the scope of the future..
Dr. &. Mon Feb 17 2020
Review notes
Dear Editor, I would like to say that this book is very conceptual and helpful and needs to be read! Thanks for your and for authors kind efforts. Good luck