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Manu Pratap Singh
Manu Pratap Singh
Dr. B. R. Ambedkar University, India
Dr. Manu Pratap Singh received his Ph.D. in Computer science from Kumaun University Nanital, Uthrakhand, India, in 2001. He has completed his Master of Science in Computer Science from Allahabad University, Allahabad in 1994. Further he obtained the M. Tech. in Information technology from Mysore. He is currently asAssociate Professor in Department of Computer Science, Institute of Engineering and Technology, Dr. B.R. Ambedkar University, Agra, UP, India since 2008. He is engaged in teaching and research since last 16 years. He has more than 80 research papers in journals of international and national repute. His work has been recognized widely around the world in the form of citations of my research papers. He also has received the Young Scientist Award in computer science by international Academy of Physical sciences, Allahabad in year 2005. He has guided 18 students for their doctorate in computer science. He is also referee of various international and national journals like International Journal of Uncertainty, Fuzziness and Knowledge Based Systems by World scientific publishing cooperation Ltd, International Journal of Engineering, Iran, IEEE Transaction of fuzzy systems and European journal of operation research by Elsevier. He has developed a feed forward neural networks simulator for hand written character recognition of English alphabets. He has also developed a hybrid evolutionary algorithm for hand written character recognition of English as well as for Hindi language classification. In the hybrid approach the Genetic algorithm is incorporated with back propagation learning rule to train the feed forward neural networks. In this approach the genetic algorithm starts from the suboptimal solution and converges for the optimal solutions. There are more than one optimal solution has obtained. This approach leads for the multi objective optimization phenomena. Another hybrid approach of evolutionary algorithm is developed for the feedback neural network of Hopfield type for efficient recalling for the memorized patterns. Here also the randomness from the genetic algorithm is minimized by starting it from the suboptimal solution in the term of parent weight matrix for the global optimal solutions i.e. correct weight matrices for the network to consider it for efficient pattern recalling. His research interests are focused on neuro-computing, neuroscience, neuro-informatics, soft-computing, etc. He is a member of technical committee of IASTED, Canada since 2004. He is also the regular member of machine intelligence Research Labs (MIR Labs), scientific network for innovation and research excellence (SNIRE), Auburn, Washington, USE,, since 2012.
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