Artificial Neural Networks in Finance and Manufacturing

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Description

Two of the most important factors contributing to national and international economy are processing of information for accurate financial forecasting and decision making as well as processing of information for efficient control of manufacturing systems for increased productivity. The associated problems are very complex and conventional methods often fail to produce acceptable solutions. Moreover, businesses and industries always look for superior solutions to boost profitability and productivity. In recent times, artificial neural networks have demonstrated promising results in solving many real-world problems in these domains, and these techniques are increasingly gaining business and industry acceptance among the practitioners.

Artificial Neural Networks in Finance and Manufacturing presents many state-of-the-art and diverse applications to finance and manufacturing, along with underlying neural network theories and architectures. It offers researchers and practitioners the opportunity to access exciting and cutting-edge research focusing on neural network applications, combining two aspects of economic domain in a single and consolidated volume.

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Author(s)/Editor(s) Biography

Joarder Kamruzzaman
Dr. Joarder Kamruzzaman received his B.Sc and M.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering & Technology, Dhaka, Bangladesh in 1986 & 1989 respectively and PhD in Information Systems Engineering from Muroran Institute of Technology, Japan in 1993. Currently he is a faculty member in the Faculty of Information Technology, Monash University, Australia. His research interest includes computational intelligence, computer networks and bioinformatics. He has published more than 90 refereed papers in international journals and conference proceedings. He is currently serving as a program committee member of a number of international conferences.

Rezaul Begg
Dr. Rezaul Begg received the B.Sc. and M.Sc. Eng degrees in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh and the Ph.D. degree in Biomedical Engineering from the University of Aberdeen, U.K. Currently he is a Faculty member at Victoria University, Melbourne, Australia. Previously, he worked with Deakin University and BUET. He researches in Biomedical Engineering, Biomechanics and Machine Learning areas and has published over 100 research papers in these areas. He is a regular reviewer for several international journals and was on the TPC for a number of major international conferences. He received several awards, including the BUET Gold medal and the Chancellor prize for academic excellence.

Ruhul Sarker
Ruhul Sarker received his Ph.D. in 1991 from DalTech, Dalhousie University, Halifax, Canada and is currently a Senior Lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia. Before joining at UNSW in February 1998, Dr Sarker worked with Monash University, Victoria and the Bangladesh University of Engineering and Technology, Dhaka. His main research interests are Evolutionary Optimization, Data Mining and Applied Operations Research. He is currently involved with four edited books either as editor or co-editor and has published more than 60 refereed papers in international journals and conference proceedings. He is also the editor of ASOR Bulletin, the national publication of the Australian Society for Operations Research.