Verified email at gmail.com
professor, computer science engineering
LENORA COLLLEGE OF ENGINEERING
I, myself, Raja SK Boddu (http://www.rajaboddu.com) highly motivated, self-driven, moderate educator and administrator with 17 years experience in Engineering Education and 6 years Engineering Departmental experience. Presently, working as a Professor, Faculty of Computer Science and Principal at Lenora College of Engineering (http://www.lce.ac.in). I have been ratified as PRINCIPAL by JNT University, Kakinada in 2013. 19 PG dissertations supervised, 27 peer-reviewed publications published and 4 International Conferences attended.
I am having memberships of high profile program committees, review boards such as a Fellow of IEI, as a Life Member of IETE, ISCA and CSI, as a Senior Member of IEEE and ACM and as a Reviewer for IEI-Springer Series-B Journals, SAI Organization journals and Springer’s Journal of Supercomputing.
2013- Ph.D (Computer Science and Systems Engineering) from Andhra University (A), Visakhapatnam, India
Major – Machine Learning, Big data
Thesis titled- Some studies on Personalized Recommendation Algorithms with Collaborative Filtering (February 2013)
2001 - M.Tech (Computer Science and Systems Engineering) from Andhra University (A), Visakhapatnam, India
1995 - B.E (Civil Engineering) from Andhra University (A), Visakhapatnam, India
Kamal Gulati, Raja Sarath Kumar Boddu, Dhiraj Kapila, Sunil L. Bangare, Neeraj Chandnani, and G. Saravanan
Materials Today: Proceedings, eISSN: 22147853, Pages: 161-165, Published: 2021 Elsevier BV
Bendi Venkata Ramana and Raja Sarath Kumar Boddu
2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, Pages: 140-145, Published: 12 March 2019 IEEE
This paper studies selected classification algorithms on medical datasets. The selected health datasets are Breast Cancer Data, Chronic Kidney Disease, Cryotherapy, Hepatitis, Immunotherapy, Indian Liver Patient Dataset (ILPD), Liver Disorders, and Liver disorders dataset. ILPD and Liver disorders, Pima diabetes, risk factors cervical cancer and Statlog (Heart) Data Set dataset are taken from the University of California at Irvine (UCI) repository. The classification algorithms considered in this study are Bagging, IBK, J48, JRip, Multilayer perceptron (MP) and Naive Bayes (NB) classifiers.
Raja Sarath Kumar Boddu
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Pages: 3954-3956, Published: 2016 IEEE
Collaborative Filtering (CF) Recommendation System is a prominent technology which is widely online. Large variety of available CF algorithms and the multitude of their possible parameters have a huge impact on quality of the outcome on ECommerce. Unfortunately, the literature on CF recommender system evaluation presents different evaluation metrics at different situation and could not provide any suggestion about the best. At the same time, it is a fact to accept that the accuracy measure should be different for each CF algorithm and depends upon the classification accuracy of that particular algorithm. As an initiative to address this problem in the present research paper, predictive accuracy metrics, classification accuracy metrics and rank accuracy metrics are considered as the classification accuracy metrics to know the overall competence for significant features of the chosen CF algorithms. At this juncture, normalization, a distinctive evaluation methodology, has been adopted to accomplish unique evaluation results of recommender systems. In this research paper, different accuracy metrics assessment would be brought into a common scale by taking into consideration of normalization process to evaluate metrics of the CF algorithms. A comprehensive comparative analysis is carried out and tabulated.
Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012, Pages: 914-919, Published: 2012
Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, Pages: 312-319, Published: 2011
Raja Sarath Kumar Boddu, “An Integrated Assessment Approach to different Collaborative Filtering Algorithms” IEEE BigData-2016 conference, held at Washington DC, USA, 5-8, December 2016, Year: 2016, Pages: 3954 - 3956, DOI: 10.1109/BigData.2016.7841073,IEEE