N Ram Kumar

@narayana medical college

Assistant Professor cum Statistician Department of Community Medicine
Assistant Professor cum Statistician

N Ram Kumar


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https://researchid.co/ram222u

EDUCATION

PhD Statistics Full Time

RESEARCH, TEACHING, or OTHER INTERESTS

Computational Theory and Mathematics, Statistics and Probability, Medicine, Computer Science Applications
3

Scopus Publications

24

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins
    M. Kavitha, B. Sankara Babu, B. Sumathy, T. Jackulin, N. Ramkumar, A. Manimaran, Ranjan Walia, S. Neelakandan
    Intelligent Automation and Soft Computing, 2022
    With the advancement of communication and computing technologies, multimedia technologies involving video and image applications have become an important part of the information society and have become inextricably linked to people's daily productivity and lives. Simultaneously, there is a growing interest in super-resolution (SR) video reconstruction techniques. At the moment, the design of digital twins in video computing and video reconstruction is based on a number of difficult issues. Although there are several SR reconstruction techniques available in the literature, most of the works have not considered the spatio-temporal relationship between the video frames. With this motivation in mind, this paper presents VDCNN-SS, a novel very deep convolutional neural networks (VDCNN) with spatiotemporal similarity (SS) model for video reconstruction in digital twins. The VDCNN-SS technique proposed here maps the relationship between interconnected low resolution (LR) and high resolution (HR) image blocks. It also considers the spatiotemporal non-local complementary and repetitive data among nearby low-resolution video frames. Furthermore, the VDCNN technique is used to learn the LR–HR correlation mapping learning process. A series of simulations were run to examine the improved performance of the VDCNN-SS model, and the experimental results demonstrated the superiority of the VDCNN-SS technique over recent techniques.
  • +Computing adjusted projection depth using GSO algorithm
    R. Muthukrishnan, N. Ramkumar
    Aip Conference Proceedings, 2020
    Projection depth is an important concept of non-parametric inference on multivariate data analysis and is the most widely used as statistical depth notion among all the existing depth procedures. This depth procedure was based on Stahel-Donoho multivariate location and scatter estimator and its outlyingness. Further, the adjusted outlyingness concept was used to compute projection depth and namely adjusted projection depth. In both the depth procedures, exact, fixed and random algorithms have been used to compute projection depth values. In this paper, the Gram-Schmidt Orthonormalization (GSO) algorithm is proposed to compute adjusted projection depth in order to improve the accuracy measure. The superiority of the GSO algorithm based adjusted projection depth over the exact, random and fixed algorithms has been demonstrated by applying it in the context of classification analysis by computing average misclassification error rate under real and simulation environments.
  • Robust classification using skew-adjusted projection depth
    International Journal of Scientific and Technology Research, 2019

RECENT SCHOLAR PUBLICATIONS

  • Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins.
    M Kavitha, BS Babu, B Sumathy, T Jackulin, N Ramkumar, A Manimaran, ...
    Intelligent Automation & Soft Computing 34 (3) , 2022
    2022
    Citations: 14
  • Deep learning based Supply Chain and Waste management using Internet of Things
    DAA SaurabhDahiya, N RamKumar, S Hemavathi
    NeuroQuantology 20 (10), 4034-4042 , 2022
    2022
  • Data Depth based Discriminant Classification Analysis
    N Ramkumar, N Wahid, Y TL, K KS
    JOURNAL OF ALGEBRAIC STATISTICS 13 (3), 1995-2015 , 2022
    2022
  • Computing adjusted projection depth using GSO algorithm
    R Muthukrishnan, N Ramkumar
    AIP Conference Proceedings 2261 (1), 030066 , 2020
    2020
  • Adjusted Projection Depth Based Expected Maximization Algorithm and Its Application in Image Segmentation
    R Muthukrishnan, N Ramkumar
    TEST Engineering and Management 83 (March- April 2020), 19198 - 19207 , 2020
    2020
  • Robust Approaches on the Estimation of Correlation
    N Ramkumar, R Muthukrishnan, K Thangamalar
    INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR) 6 (1), 77-83 , 2019
    2019
  • Robust Classification using Skew-Adjusted Projection Depth
    R Muthukrishnan, N Ramkumar
    International Journal of Scientific & Technology Research 8 (12), 2999-3003 , 2019
    2019
    Citations: 1
  • EXPECTED MAXIMIZATION ALGORITHM: PROJECTION DEPTH APPROACH
    N Ramkumar, R Muthukrishnan, M Vadivel
    Far East Journal of Theoretical Statistics 56 (1), 35-46 , 2019
    2019
  • Measure of Location using Data Depth Procedures
    R Muthukrishnan, D Gowri, N Ramkumar
    Internatinal Journal Science Reserch in Mathematical and Statistical … , 2018
    2018
    Citations: 5
  • Projection Based Data Depth Procedure with Application in Discriminant Analysis
    N Ramkumar, R Muthukrishnan, M Vadivel
    International Journal of Research in Advent Technology 6 (5), 824-832 , 2018
    2018
    Citations: 3
  • IJMTT Call for Paper September-2022
    R Muthukrishnan, M Vadivel, N Ramkumar
    2017
  • Gram-Schmidt Orthonormalization based Projection Depth
    R Muthukrishnan, M Vadivel, N Ramkumar
    International Journal of Mathematics Trends and Technology 52 (6), 430-434 , 2017
    2017
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins.
    M Kavitha, BS Babu, B Sumathy, T Jackulin, N Ramkumar, A Manimaran, ...
    Intelligent Automation & Soft Computing 34 (3) , 2022
    2022
    Citations: 14
  • Measure of Location using Data Depth Procedures
    R Muthukrishnan, D Gowri, N Ramkumar
    Internatinal Journal Science Reserch in Mathematical and Statistical … , 2018
    2018
    Citations: 5
  • Projection Based Data Depth Procedure with Application in Discriminant Analysis
    N Ramkumar, R Muthukrishnan, M Vadivel
    International Journal of Research in Advent Technology 6 (5), 824-832 , 2018
    2018
    Citations: 3
  • Robust Classification using Skew-Adjusted Projection Depth
    R Muthukrishnan, N Ramkumar
    International Journal of Scientific & Technology Research 8 (12), 2999-3003 , 2019
    2019
    Citations: 1
  • Gram-Schmidt Orthonormalization based Projection Depth
    R Muthukrishnan, M Vadivel, N Ramkumar
    International Journal of Mathematics Trends and Technology 52 (6), 430-434 , 2017
    2017
    Citations: 1
  • Deep learning based Supply Chain and Waste management using Internet of Things
    DAA SaurabhDahiya, N RamKumar, S Hemavathi
    NeuroQuantology 20 (10), 4034-4042 , 2022
    2022
  • Data Depth based Discriminant Classification Analysis
    N Ramkumar, N Wahid, Y TL, K KS
    JOURNAL OF ALGEBRAIC STATISTICS 13 (3), 1995-2015 , 2022
    2022
  • Computing adjusted projection depth using GSO algorithm
    R Muthukrishnan, N Ramkumar
    AIP Conference Proceedings 2261 (1), 030066 , 2020
    2020
  • Adjusted Projection Depth Based Expected Maximization Algorithm and Its Application in Image Segmentation
    R Muthukrishnan, N Ramkumar
    TEST Engineering and Management 83 (March- April 2020), 19198 - 19207 , 2020
    2020
  • Robust Approaches on the Estimation of Correlation
    N Ramkumar, R Muthukrishnan, K Thangamalar
    INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR) 6 (1), 77-83 , 2019
    2019
  • EXPECTED MAXIMIZATION ALGORITHM: PROJECTION DEPTH APPROACH
    N Ramkumar, R Muthukrishnan, M Vadivel
    Far East Journal of Theoretical Statistics 56 (1), 35-46 , 2019
    2019
  • IJMTT Call for Paper September-2022
    R Muthukrishnan, M Vadivel, N Ramkumar
    2017