Advances in Hybridization of Intelligent Methods: Models, Systems and Applications

Description

This book presents recent research on the hybridization of intelligent methods, which refers to combining methods to solve complex problems. It discusses hybrid approaches covering different areas of intelligent methods and technologies, such as neural networks, swarm intelligence, machine learning, reinforcement learning, deep learning, agent-based approaches, knowledge-based system and image processing. The book includes extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016. The book is intended for researchers and practitioners from academia and industry interested in using hybrid methods for solving complex problems. Front Matter ....Pages i-ix Deep Learning Approaches for Facial Emotion Recognition: A Case Study on FER-2013 (Panagiotis Giannopoulos, Isidoros Perikos, Ioannis Hatzilygeroudis)....Pages 1-16 Analysis of Biologically Inspired Swarm Communication Models (Musad Haque, Electa Baker, Christopher Ren, Douglas Kirkpatrick, Julie A. Adams)....Pages 17-38 Target-Dependent Sentiment Analysis of Tweets Using Bidirectional Gated Recurrent Neural Networks (Mohammed Jabreel, Fadi Hassan, Antonio Moreno)....Pages 39-55 Traffic Modelling, Visualisation and Prediction for Urban Mobility Management (Tomasz Maniak, Rahat Iqbal, Faiyaz Doctor)....Pages 57-70 Assurance in Reinforcement Learning Using Quantitative Verification (George Mason, Radu Calinescu, Daniel Kudenko, Alec Banks)....Pages 71-96 Distillation of Deep Learning Ensembles as a Regularisation Method (Alan Mosca, George D. Magoulas)....Pages 97-118 Heuristic Constraint Answer Set Programming for Manufacturing Problems (Erich C. Teppan, Gerhard Friedrich)....Pages 119-147