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Prasannavenkatesan Theerthagiri
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2020 – today
- 2024
- [j20]Prasannavenkatesan Theerthagiri, A. Usha Ruby, J. George Chellin Chandran, Tanvir Habib Sardar, Ahamed Shafeeq B. M.:
Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases. J. Big Data 11(1): 112 (2024) - [j19]Prasannavenkatesan Theerthagiri
, A. Usha Ruby
, B. N. Chaithanya, Renuka R. Patil, Swasthika Jain:
D-Resnet: deep residual neural network for exploration, identification, and classification of beach sand minerals. Multim. Tools Appl. 83(5): 14539-14563 (2024) - [j18]Prasannavenkatesan Theerthagiri
, Devarayapattana Siddalingaiah Sahana:
RG-SVM: Recursive gaussian support vector machine based feature selection algorithm for liver disease classification. Multim. Tools Appl. 83(20): 59021-59042 (2024) - [j17]Prasannavenkatesan Theerthagiri
, A. Usha Ruby, J. George Chellin Chandran:
Prediction and classification of minerals using deep residual neural network. Neural Comput. Appl. 36(4): 1539-1551 (2024) - 2023
- [j16]Prasannavenkatesan Theerthagiri
:
CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression. Int. J. Commun. Syst. 36(12) (2023) - [j15]Prasannavenkatesan Theerthagiri
:
Stress emotion recognition with discrepancy reduction using transfer learning. Multim. Tools Appl. 82(4): 5949-5963 (2023) - [j14]Prasannavenkatesan Theerthagiri
, A. Usha Ruby
:
Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends. Multim. Tools Appl. 82(16): 24485-24504 (2023) - [j13]Prasannavenkatesan Theerthagiri
, A. Usha Ruby
, Vidya J:
Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms. SN Comput. Sci. 4(1): 72 (2023) - 2022
- [j12]Prasannavenkatesan Theerthagiri
, Menakadevi Thangavelu
:
Mobility speed prediction using ARIMA and RNN for random walk mobility model in mobile ad hoc networks. Concurr. Comput. Pract. Exp. 34(4) (2022) - [j11]Prasannavenkatesan Theerthagiri
, Chandrasekaran Gopala Krishnan:
Vehicular multihop intelligent transportation framework for effective communication in vehicular ad-hoc networks. Concurr. Comput. Pract. Exp. 34(10) (2022) - [j10]Prasannavenkatesan Theerthagiri
, A. Usha Ruby
:
RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction. Expert Syst. J. Knowl. Eng. 39(9) (2022) - [j9]Prasannavenkatesan Theerthagiri
, Jyothiprakash Vidya:
Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques. Expert Syst. J. Knowl. Eng. 39(9) (2022) - [j8]C. Gopala Krishnan, Nishan A. H.
, Prasannavenkatesan Theerthagiri
:
k-means clustering based energy and trust management routing algorithm for mobile ad-hoc networks. Int. J. Commun. Syst. 35(9) (2022) - [j7]Prasannavenkatesan Theerthagiri
:
Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique. Intell. Syst. Appl. 16: 200121 (2022) - [j6]Prasannavenkatesan Theerthagiri
:
Mobility prediction for random walk mobility model using ARIMA in mobile ad hoc networks. J. Supercomput. 78(14): 16453-16484 (2022) - 2021
- [j5]Prasannavenkatesan Theerthagiri
:
Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques. Concurr. Comput. Pract. Exp. 33(16) (2021) - [j4]Prasannavenkatesan Theerthagiri
:
Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model. EAI Endorsed Trans. Pervasive Health Technol. 7(26): e3 (2021) - [j3]Prasannavenkatesan Theerthagiri
, C. Gopala Krishnan, Nishan A. H.
:
Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique. EAI Endorsed Trans. Pervasive Health Technol. 7(26): e5 (2021) - [i2]Prasannavenkatesan Theerthagiri
, Vidya J:
Cardiovascular Disease Prediction using Recursive Feature Elimination and Gradient Boosting Classification Techniques. CoRR abs/2106.08889 (2021) - 2020
- [j2]Prasannavenkatesan Theerthagiri
:
FUCEM: futuristic cooperation evaluation model using Markov process for evaluating node reliability and link stability in mobile ad hoc network. Wirel. Networks 26(6): 4173-4188 (2020) - [i1]Prasannavenkatesan Theerthagiri:
Forecasting Hyponatremia in hospitalized patients Using Multilayer Perceptron and Multivariate Linear Regression Techniques. CoRR abs/2007.15554 (2020)
2010 – 2019
- 2019
- [j1]Prasannavenkatesan Theerthagiri
, Menakadevi Thangavelu
:
Futuristic speed prediction using auto-regression and neural networks for mobile ad hoc networks. Int. J. Commun. Syst. 32(9) (2019)
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last updated on 2024-08-19 21:36 CEST by the dblp team
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