Binary cross entropy with deep learning technique for image classification Y Usha Ruby, A., Theerthagiri, P., Jeena Jacob, I., Vamsidhar International Journal of Advanced Trends in Computer Science and Engineering …, 2020 | 392 | 2020 |
Prediction of COVID-19 possibilities using KNN classification algorithm P Theerthagiri, IJ Jacob, AU Ruby, Y Vamsidhar | 50* | 2020 |
Overview of Proactive Routing protocols in MANET TP Venkatesan, P Rajakumar, A Pitchaikkannu IEEE Fourth International Conference on Communication Systems and Network …, 2014 | 38 | 2014 |
Security attacks and detection schemes in MANET P Rajakumar, VT Prasanna, A Pitchaikkannu IEEE International Conference on Electronics and Communication Systems …, 2014 | 31 | 2014 |
FUCEM: futuristic cooperation evaluation model using Markov process for evaluating node reliability and link stability in mobile ad hoc network P Theerthagiri Wireless Networks, 2020 | 30 | 2020 |
Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques P Theerthagiri, J Vidya Expert systems 39 (9), e13064, 2022 | 26 | 2022 |
Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique P Theerthagiri Intelligent Systems with Applications 16, 200121, 2022 | 24 | 2022 |
Diagnosis and classification of the diabetes using machine learning algorithms P Theerthagiri, AU Ruby, J Vidya SN Computer Science 4 (1), 72, 2022 | 23 | 2022 |
Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques P Theerthagiri Concurrency and Computation: Practice and Experience, e6248, 2021 | 23 | 2021 |
CoFEE: Context‐aware futuristic energy estimation model for sensor nodes using Markov model and autoregression T Prasannavenkatesan International Journal of Communication Systems, e4248, 2019 | 20* | 2019 |
Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks T Prasannavenkatesan, T Menakadevi International Journal of Communication Systems, e3951, 2019 | 18* | 2019 |
Probable forecasting of epidemic COVID-19 in using COCUDE model. T Prasannavenkatesan EAI endorsed transactions on pervasive health and technology 7 (26), e3, 2021 | 16* | 2021 |
PDA-misbehaving node detection & prevention for MANETs T Prasannavenkatesan, R Raja, P Ganeshkumar IEEE International Conference on Communication and Signal Processing, 1163-1167, 2014 | 16 | 2014 |
Stress emotion recognition with discrepancy reduction using transfer learning P Theerthagiri Multimedia Tools and Applications 82 (4), 5949-5963, 2023 | 12 | 2023 |
Prognostic analysis of hyponatremia for diseased patients using multilayer perceptron classification technique P Theerthagiri, AH Nishan EAI Endorsed Transactions on Pervasive Health and Technology 7 (26), e5-e5, 2021 | 11 | 2021 |
An Effective Intrusion Detection System for MANETs TP Venkatesan, P Rajakumar, A Pitchaikkannu International Journal of Computer Applications 3, 29-34, 2014 | 11 | 2014 |
FMPM: Futuristic mobility prediction model for mobile adhoc networks using auto-regressive integrated moving average T Prasannavenkatesan, T Menakadevi Acta graphica: znanstveni časopis za tiskarstvo i grafičke komunikacije 29 …, 2018 | 9 | 2018 |
Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends P Theerthagiri, AU Ruby Multimedia Tools and Applications 82 (16), 24485-24504, 2023 | 8 | 2023 |
Vehicular multihop intelligent transportation framework for effective communication in vehicular ad‐hoc networks P Theerthagiri, C Gopala Krishnan Concurrency and Computation: Practice and Experience 34 (10), e6833, 2022 | 8 | 2022 |
Significance of scalability for on-demand routing protocols in MANETs T Prasannavenkatesan, T Menakadevi IEEE Conference on Emerging Devices and Smart Systems (ICEDSS), 76-82, 2016 | 7 | 2016 |