Handbook of Neural Computation
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Handbook of Neural Computation

1st Edition - July 18, 2017

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  • Editors: Pijush Samui, Sanjiban Sekhar Roy, Valentina Balas
  • Paperback ISBN: 9780128113189
  • eBook ISBN: 9780128113196

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Description

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.

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

Pijush Samui is working as an associate professor in civil engineering department at NIT Patna, India. He graduated in 2000, with a B.Tech. in Civil Engineering from Indian Institute of Engineering Science and Technology, Shibpur, India. He received his M.Sc. in Geotechnical Earthquake Engineering from Indian Institute of Science, Bangalore, India (2004). He holds a Ph.D. in Geotechnical Earthquake Engineering (2008) from Indian Institute of Science, Bangalore, India. He was a postdoctoral fellow at University of Pittsburgh (USA) (2008-2009) and Tampere University of Technology (Finland) (2009- 2010). In 2010, Dr. Pijush joined in the Center for Disaster Mitigation and Management at VIT University as an Associate Professor. He was promoted to full Professor in 2012. Dr. Pijush is the recipient of the prestigious CIMO fellowship (2009) from Finland, for his integrated research on the design of railway embankment. He was awarded Shamsher Prakash Research Award (2011) by IIT Roorkee for his innovative research on the application of Artificial Intelligence in designing civil engineering structure. He was selected as the recipient of IGS Sardar Resham Singh Memorial Award – 2013 for his innovative research on infrastructure project. He was elected Fellow of International Congress of Disaster Management in 2010. He served as a guest in disaster advance journal. He also serves as an editorial board member in several international journals. He has been selected as an adjunct professor at Ton Duc Thang University (Ho Chi Minh City, Vietnam). He has been Visiting Professor at Far East Federal University (Russia).

Affiliations and Expertise

Associate Professor, Department of Civil Engineering, NIT Patna, Bihar, India

Sanjiban Sekhar Roy

Sanjiban Sekhar Roy is an Associate Professor in the School of Computer Science and Engineering, Vellore Institute of Technology. He joined VIT in the year 2009 as an Asst. Professor. His research interests include Deep Learning and advanced machine learning. He has published around 50 articles in a reputed international journal (with SCI impact factors) and conferences. He also is editorial board members to a handful of international journals and reviewer to many highly reputed journals such as Neural processing letters, Springer , IEEE Access: The Multidisciplinary Open Access Journal, Computers & Security, Elsevier , International Journal of Advanced Intelligence Paradigms, Inderscience International publishers, International Journal of Artificial Intelligence and Soft Computing, Inderscience International publishers,Ad Hoc Networks, Elsevier, Evolutionary Intelligence, Springer, Journal of Ambient Intelligence and Humanized Computing, Springer, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Springer. He uses Deep Learning and machine learning techniques to solve many complex engineering problems, especially those are related to imagery. He is specialized in deep convolutional neural networks and generative adversarial network. Dr. Roy also has edited many books with reputed interntional publishers such as elsevier,springer and IGI Global. Very recently, Ministry of National Education, Romania in collaboration with "Aurel Vlaicu" University Arad Faculty of Engineers, Romania has awarded Dr. Roy with "Diploma of Excellence" as a sign of appreciation for the special achievements obtained in the scientific research activity in 2019.

Affiliations and Expertise

Associate Professor in School of Computer Science and Engineering, Vellore Institute of Technology

Valentina Balas

Valentina Balas
Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.

Affiliations and Expertise

Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania

Ratings and Reviews

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(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