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Handbook of Neural Computation

  • ID: 4080974
  • Book
  • July 2017
  • 658 Pages
  • Elsevier Science and Technology

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.

  • 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

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1. Gravitational Search Algorithm With Chaos 2. Textures and Rough Sets 3. Hydrological time series forecasting using three different heuristic regression techniques 4. A reflection on image classifications for forest ecology management: Towards landscape mapping and monitoring 5. An Intelligent Hybridization of ABC and LM Algorithms with Constraint Engineering Applications 6. Network Intrusion Detection Model based on Fuzzy-Rough Classifiers 7. Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Models 8.  Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks 9. Object Categorization Using Adaptive Graph-based Semi-supervised Learning 10. Hemodynamic Model Inversion by Iterative Extended Kalman Smoother 11. Improved Sparse Approximation Models for Stochastic Computations 12. Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization 13. Application of particle swarm optimization to solve robotic assembly line balancing problems 14. The cuckoo optimization algorithm and its applications 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 16. Modelling the axial capacity of bored piles using multi-objective feature selection, functional network and multivariate adaptive regression spline 17. Transient stability constrained optimal power flow using chaotic whale optimization algorithm 18. Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine 19. Alternating Decision Trees 20. Scene Understanding Using Deep Learning 21. Deep Learning for Coral Classification 22. A Deep Learning Framework for Classifying Mysticete Sounds 23. Unsupervised deep learning for data-driven reliability and risk analysis of engineered systems 24. Applying Machine Learning Algorithms in Landslide Susceptibility Assessments 25. MDHS-LPNN: A hybrid FOREX predictor model using a Legendre polynomial Neural Network with a Modified Differential Harmony Search technique 26. A Neural Model of Attention and Feedback for Computing Perceived Brightness in Vision 27. Support Vector Machine: Principles, Parameters and Applications 28. Evolving Radial Basis Function Networks using Moth-Flame Optimizer 29. Application of Fuzzy Methods in Power system Problems 30. Application of Particle Swarm Optimization Algorithm in Power system Problems 31. Optimum Design of Composite Steel-Concrete Floors Based on a Hybrid Genetic Algorithm 32. A Comparative Study of Image Segmentation Algorithms and Descriptors for Building Detection 33. Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery

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Samui, Pijush
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).
Roy, Sanjiban Sekhar
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.
Balas, Valentina Emilia
Dr. Valentina E. Balas, is a full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, "Aurel Vlaicu” University of Arad, Romania. Dr. Balas is Editor-in Chief of the International Journal of Advanced Intelligence Paradigms (IJAIP) and International Journal of Computational Systems Engineering (IJCSysE).
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