Srikanta B. Patnaik

Work place: Department of CS & E, SOA University,Bhubaneshwar, Orissa,751030, India



Research Interests: Computer systems and computational processes, Computer Vision, Robotics


Prof. Srikanta Patnaik has graduated in Electronics and Telecommunication Engineering in 1989 and post graduated in Master of Business Administration in 1991 from Sambalpur University. He has received his Ph.D. in Engineering in the year 1999 from Jadavpur University, Calcutta. He is Professor and Associate Dean of Siksha "O" Anusandhan University, Bhubaneshwar. He has published more than 60 technical papers in International and National Journals of repute. He has been awarded with the MHRD Fellowship for the year 1995 and his name has been placed in the MARQUIS Who’s Who in the World for the 2004. He has been awarded as the International Educator of the Year 2005, by International Biographical Centre, Great Britain. He is the Editor-in-Chief of the International Journal of Information and Communication Technology and International Journal of Computational Vision and Robotics, published by Inderscience Publishing House, England. He is also Editor-in-Chief of Book Series on Modeling and Optimization in Science and Technology [MOST], published from Springer, Germany and other two series namely Advances in Computer and Electrical Engineering (ACEE) and Advances in Medical Technologies and Clinical Practice (AMTCP) published from IGI-Global, USA.

Author Articles
Machine Learning and Software Quality Prediction: As an Expert System

By Ekbal A. Rashid Srikanta B. Patnaik Vandana C. Bhattacherjee

DOI:, Pub. Date: 8 Apr. 2014

To improve the software quality the number of errors from the software must be removed. The research paper presents a study towards machine learning and software quality prediction as an expert system. The purpose of this paper is to apply the machine learning approaches, such as case-based reasoning, to predict software quality. The main objective of this research is to minimize software costs. Predict the error in software module correctly and use the results in future estimation. The novel idea behind this system is that Knowledge base (KBS) building is an important task in CBR and the knowledge base can be built based on world new problems along with world new solutions. Second, reducing the maintenance cost by removing the duplicate record set from the KBS. Third, error prediction with the help of similarity functions. In this research four similarity functions have been used and these are Euclidean, Manhattan, Canberra, and Exponential. We feel that case-based models are particularly useful when it is difficult to define actual rules about a problem domain. For this purpose we have developed a case-based reasoning model and have validated it upon student data. It was observed that, Euclidean and Exponential both are good for error calculation in comparison to Manhattan and Canberra after performing five experiments. In order to obtain a result we have used indigenous tool. For finding the mean and standard deviation, SPSS version 16 and for generating graphs MATLAB 7.10.0 version have been used as an analyzing tool.

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