Voruganti Naresh Kumar
@cmrtc.ac.in
Assistant Professor, Department of Computer Science and Engineering
CMR Technical Campus
10+ Academic Experience
1+ Industry Experience
EDUCATION
Ph.D, M.Tech, B.Tech
RESEARCH INTERESTS
Network Security, Machine Learning, Image Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Gurram Sunitha, J. Sasi Kiran, Kolluru Venkata Nagendra, Syeda Sumaiya Afreen, K. Reddy Madhavi, Nandini Kothapati, Voruganti Naresh Kumar, and Dosapati Hemachandu
Springer Nature Singapore
Md Mohammad Shareef, Gurram Sunitha, S. V. S. V. Prasad Sanaboina, Marri Sireesha, K. Reddy Madhavi, Ganapathi Antharam, and Voruganti Naresh Kumar
Springer Nature Singapore
J. Sasi Kiran, Gurram Sunitha, Marri Sireesha, U. Mahender, K. Reddy Madhavi, Swathi Rudra, and Voruganti Naresh Kumar
Springer Nature Singapore
M. Subba Rao, Guntoju Kalpana Devi, Suraya Mubeen, Badam Prashanth, Tazzeen Fatima, K. Reddy Madhavi, Voruganti Naresh Kumar, and Charan Yadav Chintalacheri
Springer Nature Singapore
M. Senthilkumar, K. Suthendran, S. V. Suji Aparna, Mahesh Kotha, S. Kirubakaran, Srinivasarao Dharmireddi, and Voruganti Naresh Kumar
Springer Nature Singapore
Suraya Mubeen, G.V. Ashritha, Sanjeev Bandru, Marri Sireesha, Nuthanakanti Bhaskar, and Voruganti Naresh Kumar
IEEE
Voruganti Naresh Kumar, Mahesh V Sonth, Arfa Mahvish, Vijaya kumar Koppula, B Anuradha, and L Chandrasekhar Reddy
IEEE
The primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. This study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. This study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. The broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. The five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. The correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional Neural Networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. This is clear from the complete classification and segmentation outcomes achieved by the analysis of a limited number of map segments. The image is categorized into three groups—low, bright, and high—depending on their specific characteristics obtained and luminosity, and the associated wealth index has been forecasted for each.
Tabeen Fatima, N. Purushotham, Bushra Tarannum, Raheem Unnisa, Reddy Madhavi K, R Sai Krishna, and Voruganti Naresh Kumar
IEEE
One of the most common illnesses that affect people on a broad scale is chronic kidney disease, or CKD, which is lethal since it does not manifest itself until a person's kidneys have sustained irreparable damage. The progression of CKD is linked to several serious side effects, such as an increased risk of different illnesses, anemia, hyperlipidemia, nerve damage, problems during pregnancy, and even total kidney failure. This illness claims the lives of millions of individuals each year. As no significant symptoms can be used as a baseline to diagnose the condition, diagnosing CKD is challenging. We have developed a machine-learning strategy to determine whether a patient has CKD. The application of machine learning techniques to CKD prediction has the potential to improve patient outcomes by facilitating earlier disease detection and more effective management of the condition. The logistic regression model and Random forest demonstrated the best performance and interpretability, making them useful tools for clinical practice. These findings need to be confirmed by additional studies in order to enhance how accurately machine learning systems predict CKD.
J. Tejaashwini Goud, Nuthanakanti Bhaskar, Voruganti Naresh Kumar, Suraya Mubeen, Jonnadula Narasimharao, and Raheem Unnisa
Springer Nature Singapore
J. Avanija, Banothu Ramji, A. Prabhu, K. Maheswari, R. Hitesh Sai Vittal, D. B. V. Jagannadham, and Voruganti Naresh Kumar
Springer Nature Singapore
Voruganti Naresh Kumar, Vootla Srisuma, Suraya Mubeen, Arfa Mahwish, Najeema Afrin, D. B. V. Jagannadham, and Jonnadula Narasimharao
Springer Nature Singapore
U. M. Fernandes Dimlo, Jonnadula Narasimharao, Bagam Laxmaiah, E. Srinath, D. Sandhya Rani, Sandhyarani, and Voruganti Naresh Kumar
Springer Nature Singapore
Kavitha Rani Balmuri, Srinivas Konda, Kola Thirupathaiah, Voruganti Naresh Kumar, and Jonnadula Narasimharao
Springer Singapore
Voruganti Naresh Kumar and Ganpat Joshi
Springer Singapore