Bipin K. Tripathi

Work place: Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, India



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms


Bipin K. Tripathi completed his Ph.D in Computational Intelligence from IIT Kanpur, India and M. Tech in Computer Science and Engineering from IIT Delhi, India. He is currently serving as a Professor in the Department of Computer Science and Engineering of HBTU Kanpur, India. He is also leading the Nature-inspired Computational Intelligence Research Group (NCIRG) at HBTU. His areas of research include high-dimensional neurocomputing, computational neuroscience, intelligent system design, machine learning and computer vision focused on biometrics and 3D Imaging. He has published several research papers in these areas in many peer reviewed journals including IEEE Transaction, Elsevier, Springer and other international conferences. He has also contributed book chapters in different international publications and patent in his area. He is continuously serving as PC for many international conferences and as a reviewer of several international journals.

Author Articles
Development of Hybrid Learning Machine in Complex Domain for Human Identification

By Swati Srivastava Bipin K. Tripathi

DOI:, Pub. Date: 8 Jan. 2019

This paper presents a hybrid learning machine for human identification. It is a merger of eigenface with fisherface method, genetic fuzzy clustering and complex neural network. The non-linear aggregation based summation and radial basis function neural networks (NLA-SRBF NNs) are proposed as one of the functional component of the novel learning machine. The architecture of NLA-SRBF NNs incorporates hidden neurons, with summation and radial basis aggregation, and output neurons with only summation aggregation, along with complex resilient propagation (ČRPROP) learning procedure. The improved learning and speedy convergence of NLA-SRBF NN enables the hybrid machine to provide better recognition accuracy. The learning machine consists of feature extraction, unsupervised clustering and supervised classification module. The aim of our proposal is to enhance the performance of biometric based recognition system. The efficacy and potency of our hybrid learning machine demonstrated on three benchmark biometric datasets-extended Cohn-Kanade, FERET and AR face datasets to comprehend the motivation. The performance comparisons of different variations of hidden neuron and learning algorithm thoroughly presented the superiority of the proposed NN based hybrid learning machine.

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On the Root-Power Mean Aggregation Based Neuron in Quaternionic Domain

By Sushil Kumar Bipin K. Tripathi

DOI:, Pub. Date: 8 Jul. 2018

This paper illustrates the new structure of artificial neuron based on root-power means (RPM) for quaternionic-valued signals and also presented an efficient learning process of neural networks with quaternionic-valued root-power means neurons (ℍ-RPMN). The main aim of this neuron is to present the potential capability of a nonlinear aggregation operation on the quaternionic-valued signals in neuron cell. A wide spectrum of aggregation ability of RPM in between minima and maxima has a beautiful property of changing its degree of compensation in the natural way which emulates the various existing neuron models as its special cases. Further, the quaternionic resilient propagation algorithm (ℍ-RPROP) with error-dependent weight backtracking step significantly accelerates the training speed and exhibits better approximation accuracy. The wide spectrums of benchmark problems are considered to evaluate the performance of proposed quaternionic root-power mean neuron with ℍ-RPROP learning algorithm.

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