Jothilakshmi R
@mucollege.ac.in
Assistant Professor of Mathematics
Mazharul Uloom College
EDUCATION
M.Sc M.Phil M.Tech Ph.D
RESEARCH, TEACHING, or OTHER INTERESTS
Mathematics, Applied Mathematics, Computational Mathematics, Modeling and Simulation
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M. Parveen Banu, R. Jothilakshmi, S. Radha Rammohan, R. Vijay Anand, P. Anandan, and Moustafa H. Aly
Springer Science and Business Media LLC
T.S. Balaji Damodhar, P. Anandan, C. Nalini, M. Asha Jerlin, Akila Victor, K. Anusha, and R. Jothilakshmi
Elsevier BV
Rajagopal Kumar, Fadi Al-Turjman, L. N. B. Srinivas, M. Braveen, and Jothilakshmi Ramakrishnan
Springer Science and Business Media LLC
Nakeeb Noor alleema, Christalin Nelson Selvin, Vijayakumar Varadarajan, Anandan Panneerselvam, Ramakrishnan Jothilakshmi, and Santhosh kumar perumal
Elsevier BV
Geeitha Senthilkumar, Rajagopal Kumar, C. Nalini, V.R. Niveditha, and Jothilakshmi Ramakrishnan
Inderscience Publishers
Geeitha Senthilkumar, Fadi Al Turjman, Rajagopal Kumar, and Jothilakshmi Ramakrishnan
Inderscience Publishers
G. Vinu Priya and R. Jothilakshmi
AIP Publishing
M Parveen Banu and R Jothilakshmi
IOP Publishing
Abstract The relationship between difference equations with constant coefficients, obtained by establishing forward and backward differences in the filter design concepts of digital signal processing is explored in this paper. Moreover, thecorrelation is established between the input-output difference equations along with definite time invariant linear systemand the state spacedifference equationsrelated to filter design andthisleads to identify the efficient adaptive filterdesign and validate its function. Finally, the state space illustration of a system is provided, that is acceptable to construct and validate a new developed system between the difference equation and the digital signal processor.
Nandagopal Velusamy, Fadi Al-Turjman, Rajagopal Kumar, and Jothilakshmi Ramakrishnan
Elsevier BV
Geeitha Senthilkumar, Jothilakshmi Ramakrishnan, Jaroslav Frnda, Manikandan Ramachandran, Deepak Gupta, Prayag Tiwari, Mohammad Shorfuzzaman, and Mazin Abed Mohammed
IEEE Access Institute of Electrical and Electronics Engineers (IEEE)
IoT has facilitated predominant advancements in cancer research by incorporating Artificial intelligence (AI) that enables human decision-makers to achieve a better decision. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (HSDAFS). In the HSDA.FS algorithm, the diversity parameter is added based on the gene value, and their risk score of the lncRNAs is computed using the Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (ENSCF), is proposed based on recurrent neural networks. The prognostic factor is computed with a risk score of nine lncRNA signatures for 300 samples taken from GSE44001. The Chi-Square method has been used to obtain statistical results. The survival of the patient with recurrence cervical cancer is shown using the proposed model.
K. Ameenal Bibi, A. Lakshmi, and R. Jothilakshmi
Springer International Publishing
S. Elizabeth and R. Jothilakshmi
Academic Publications
In this paper, the convergence aspects of the Extended Kalman Filter, when used as a deterministic observer for a nonlinear discrete-time sys- tems, are addressed and analyzed. The conditions needed to ensure the bound- edness of the error covariances which are related to the observability properties of the nonlinear systems are identified through difference equations. Further- more, boundedness and stability conditions are provided in a noisy environment systems.
Sebastian Elizabeth and Ramakrishnan Jothilakshmi
Trans Tech Publications, Ltd.
This paper deals with quantification of the noise located in the digital signal by an innovative process. This process is associated with the filter that represents the novel information conveyed by the desired signal, residual interference and residual noise which are used to reduce the noise. A typical uniform quantization operation of a sampled signal is identified and interpreted with the framework of stochastic difference equation. A new theorem is proposed with all possible assumptions to support our result to signal noise ratio. AMS [200] Subject Classification: 39A10, 39A30, 39A60, 39B82, 39B99.
S Elizabeth. and R Jothilakshmi.
IEEE
In this paper the stability of discrete time Extended Kalman Filter (EKF) when applied to non linear system with state estimation constraints are discussed. The stochastic stability of the constrained extended Kalman filter is considered then the analysis is extended to the estimation error-based constrained extended Kalman filter. The estimation error of the EKF with known constraints on the states remains bounded when the initial error and noise terms are small, and the solution of the Riccati difference equation remains positive definite and bounded. This leads to convergence of the filter and its stability. It is very sensitive to initialization and filter divergence is inevitable if the arbitrary noise matrices have not chosen appropriately.