raja s k Boddu
@lce.ac.in
professor, computer science engineering
LENORA COLLLEGE OF ENGINEERING
I, myself, Raja SK Boddu ( 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 (. 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.
EDUCATION
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
RESEARCH INTERESTS
machine learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Shashi Kant Gupta, Birajashis Pattnaik, Vineet Agrawal, Raja Sarath Kumar Boddu, Archana Srivastava, and Bramah Hazela
IEEE
The Internet of Things (IoT) is a network of computing devices that can transmit and obtain data across a network without human intervention. In the last couple of decades, software and communication technology have advanced tremendously, resulting in a considerable increase in IoT devices. The rapid expansion has raised security and privacy issues. Threats and malware attacks on IoT devices have increased dramatically recently. Hence, in this paper, we proposed a novel malware detection framework based on machine learning in IoT using a Genetic Cascaded Support Vector Machine (GC-SVM) classifier. We introduce the Chaotic Binary Coded Cuckoo Search Optimization Algorithm (CBC-CSOA) for optimizing the detection process. The performance of the proposed method is evaluated and compared with various conventional methodologies. The proposed method produced accurate outputs this approach may be used to forecast and identify malware in IoT-based systems, according to the study.
Raja Sarath Kumar Boddu, Partha Karmakar, Ankan Bhaumik, Vinay Kumar Nassa, Vandana, and Sumanta Bhattacharya
Elsevier BV
Raja Sarath Kumar Boddu, Ashwinkumar A. Santoki, Shopita Khurana, Poonam Vitthal Koli, Ravi Rai, and Abhishek Agrawal
Elsevier BV
Raja Sarath Kumar Boddu, Shahanawaj Ahamad, K.V. Pradeep Kumar, Mritha Ramalingam, Laxmi Kirana Pallathadka, and Fernan Peniero Tupas
Elsevier BV
Raja Sarath Kumar Boddu
IEEE
In early days, withdense population, Indian doctors have a look for novel solutions for constant monitoring on patient’s health check-up. In general, it needsnumerous visits to the hospital for doctor’s consultation, which involvesnot only money butintense time. Now, the technology has advanced by means of internet of things (IoT), which make all things interconnected and recognized as a progressive measure of technical revolt. Medical gadgets usagebecame efficient by permittingreal-time health observations uninterruptedly. It is possible to monitor patient historywhenever and where everrequired from anyplace by the specialist. These datasetsareused toknow thepatient’santiquity andconsequent analysis would be done by using machine learning algorithms. These datasets analyzed by using naive bayesian algorithm. The results are tabulated.
Kamal Gulati, S. Saravana Kumar, Raja Sarath Kumar Boddu, Ketan Sarvakar, Dilip Kumar Sharma, and M.Z.M. Nomani
Elsevier BV
S. Saravana Kumar, Dhiraj Kapila, Raja Sarath Kumar Boddu, Dilip Kumar Sharma, Mohit Tiwari, and Mohd Naved
IEEE
To understand function of proteins in living bodies we need to derive the protein sequences genome sequencing projects. For this purpose, we can use various tools or latest computational methods. These methods are related to the functions directly. Nuclear magnetic resonance (NMR) is helpful to make the 3 D protein structure. We’re using a unique method to determine the protein structures in this paper. 1491 proteins have been taken in consideration from BMRB - Biological Magnetic Resonance Bank. The structural categorization of proteins (SCOP) method was useful in locating a set of 119 traits divided into 5 separate types. After conducting study, we were able to determine the structural classes of proteins with an accuracy of 80%. taking help of using Matthew Correlation coefficient. Results conclude that we can use NMR-based method for protein structural class identification as a tool for low-resolution.
Dilip Kumar Sharma, Raja Sarath Kumar Boddu, Narinder Kumar Bhasin, S. Shajun Nisha, Vipin Jain, and Md. Khaja Mohiddin
IEEE
Cloud Computing is an Internet computing model in which client PCs access pooled resources, software, and data via web/cloud servers. Health IT refers to computer IT systems that handle the electronic health records of patients based on the electronic health records to make cloud computing easier for people, organizations, and companies; Large IT firms have already invested millions of dollars in infrastructure, services, tools, and apps. The healthcare industry is still so diversified, complex, and distinctive, it must be established that it is interesting to examine how cloud computing affects it. It poses several concerns, including safeguarding members’ health records while adhering to HIPAA regulation and Federal compliance requirements’ guidelines. In addition, the rising expense of healthcare solutions is a concern. Consumer expenses are being reduced, and IT will play a crucial part in attaining this goal and enhancing clinical and quality outcomes for patients. The way cloud computing addresses these problems in the healthcare sector and contributes to them is fascinating to see. This research seeks to study the current condition and emerging advances of cloud computing in health care.
Surabhi Saxena, Diwakar Yagyasen, Ch Naga Saranya, Raja Sarath Kumar Boddu, Amit Kumar Sharma, and Shashi Kant Gupta
IEEE
Computer without network connectivity is an exception today. Earlier everyone was using the computer to solve its individual problem. Now a days these problems related application is not available on the standalone desktop, these applications are working as a client server application. In this system, server part is available at some remote machine while the client part is available at user’s machine. Cloud computing is implemented using the four deployment models private cloud, public cloud, cloud hybrid and community cloud. Some of the user organization feels that the data can be misused by the company who owned the infrastructure. As a result, either they do not use the cloud services hosted on public cloud or they use only some limited services, so negate the use of cloud computing to a great extent. The solution to this is provided by the evolution of hybrid cloud. The corporate that feels security as one of the major issues in migration to cloud computing makes the use of hybrid cloud for their usage.
Kamal Gulati, Raja Sarath Kumar Boddu, Dhiraj Kapila, Sunil L. Bangare, Neeraj Chandnani, and G. Saravanan
Elsevier BV
V. Nagaraju, Abhisek Sethy, Raja Sarath Kumar Boddu, S. Balambigai, and K. Sakthisudhan
Elsevier BV
Bendi Venkata Ramana and Raja Sarath Kumar Boddu
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
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.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
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/