Publications

SCI/SCIE INDEXED JOURNAL PUBLICATIONS

1. T. Prasannavenkatesan, Sahana DS. RG-SVM: Recursive gaussian support vector machine-based feature selection algorithm for liver disease classification. Multimedia Tools and Applications, 2023, Springer, IF: 3.6, ISSN: 1468-0394, DOI: https://doi.org/10.1007/s11042-023-17825-1, Q1, Indexed in SCIE.

2. T. Prasannavenkatesan, Ruby, A.U. & George Chellin Chandran, J. Prediction and classification of minerals using deep residual neural network. Neural Computing &Application, 2023, Springer, IF: 6.0, ISSN: 1433-3058, https://doi.org/10.1007/s00521-023-09141-4, Q1, Indexed in SCIE.

3. T. Prasannavenkatesan, Ruby, A.U., Chaithanya, B.N., Renuka R Patil & Swasthika Jain. D-Resnet: deep residual neural network for exploration, identification, and classification of beach sand minerals. Multimedia Tools and Applications, 2023, Springer, IF: 3.6, ISSN: 1468-0394, DOI: https://doi.org/10.1007/s11042-023-16085-3, Q1, Indexed in SCIE.

4. T. Prasannavenkatesan, Usha Ruby, Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends, Multimedia Tools and Applications, 2023, Springer, IF: 3.6, ISSN: 1468-0394, DOI: https://doi.org/10.1007/s11042-023-14819-x, Q1, Indexed in SCIE.

5.     T. Prasannavenkatesan, “Stress Emotion Recognition with Discrepancy Reduction using Transfer Learning", Multimedia Tools and Applications, 2022, Springer, IF: 3.6, ISSN: 1468-0394, DOI: https://doi.org/10.1007/s11042-022-13593-6, Q1, Indexed in SCIE.

6. Prasannavenkatesan T, Vidya J., “Cardiovascular Disease Prediction using Recursive Feature Elimination and Gradient Boosting Classification Techniques.”  Expert Systems, 2022, Wiley, IF: 3.3, ISSN: 1468-0394, DOI: http://doi.org/10.1111/exsy.13064, Q2, Indexed in SCIE.

7. T. Prasannavenkatesan, A. Usha Ruby, “RFFS: Recursive Random Forest Feature Selection Based Ensemble Algorithm for Chronic Kidney Disease Prediction”, Expert Systems, 2022, Wiley, IF: 3.3, ISSN: 1468-0394, DOI: http://doi.org/10.1111/exsy.13048, Q2, Indexed in SCIE.

8. T. Prasannavenkatesan, “Mobility Prediction for Random Walk Mobility Model in Mobile Ad hoc Networks Using ARIMA" The Journal of Supercomputing, 2022. Springer, IF: 3.3, ISSN: 1573-0484, DOI: http://doi.org/10.1007/s11227-022-04503-6, Q2, Indexed in SCI.

9. Prasannavenkatesan T and Gopala Krishnan C, “Vehicular Multi-hop Intelligent Transportation Framework for Effective Communication in VANETs”, Concurrency and Computation: Practice and Experience: e6833, 2022. Wiley, IF: 2.0, ISSN: 1532-0634, DOI: http://doi.org/10.1002/cpe.6833, Q3, Indexed in SCIE.

10.  T. Prasannavenkatesan. "ReCoMM: resource-aware cooperation modelling using Markov process for effective routing in mobile ad hoc networks." Sadhana, vol. 46, no. 4 (2021): 1-14. Springer, IF: 1.6, ISSN: 0973-7677, DOI: https://doi.org/10.1007/s12046-021-01743-9, Q2, Indexed in SCIE.

11.  T. Prasannavenkatesan, and T. Menakadevi, "Mobility speed prediction using ARIMA and RNN for random walk mobility model in mobile ad hoc networks." Concurrency and Computation: Practice and Experience: e6625, 2021. Wiley, IF: 2.0, ISSN: 1532-0634, DOI: https://doi.org/10.1002/cpe.6625, Q3, Indexed in SCIE.

12.  T. Prasannavenkatesan, “FUCEM: futuristic cooperation evaluation model using Markov process for evaluating node reliability and link stability in mobile ad hoc network." Wireless Networks, vol. 26, no. 6, pp. 4173-4188, 2020. Springer, IF: 3.0, ISSN: 1572-8196, DOI: https://doi.org/10.1007/s11276-020-02326-y, Q2, Indexed in SCI.

13.  T. Prasannavenkatesan, “Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques”. Concurrency and Computation: Practice and Experience. e6248, 2021, Wiley, IF: 2.0, ISSN: 1532-0634, DOI: https://doi.org/10.1002/cpe.6248, Q3, Indexed in SCIE.

14.  T. Prasannavenkatesan, “COFEE: Context-aware Futuristic Energy Estimation model for sensor nodes using Markov model and auto-regression”, International Journal of Communication System, e4248, 2019, Wiley, IF: 2.1, ISSN: 1099-1131, DOI: https://doi.org/10.1002/dac.4248, Q2, Indexed in SCIE.

15.  T. Prasannavenkatesan and T. Menakadevi, “Futuristic Speed Prediction Using Auto-Regression and Neural Networks for Mobile Ad hoc Networks”, International Journal of Communication System, vol. 32, no. 9, e3951, 2019, Wiley, IF: 2.1, ISSN: 1099-1131, DOI: https://doi.org/10.1002/dac.3951, Q2, Indexed in SCIE.

16.  Gopala Krishnan C, Nishan AH, Prasannavenkatesan Theerthagiri, “K-Means Clustering Based Energy and Trust Management Routing Algorithm For MANET”, International Journal of Communication System, e5138, 2022, Wiley, IF: 2.1, ISSN: 1099-1131, DOI: http://doi.org/10.1002/dac.5138, Q2, Indexed in SCIE.

SCOPUS/WEB OF SCIENCE INDEXED JOURNAL PUBLICATIONS

1. Prasannavenkatesan T, Usha Ruby A, Vidya J. “Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms”, SN Computer Science, vol.4, no.72, 2023, Springer, ISSN: 2661-8907, DOI : http://doi.org/10.1007/s42979-022-01485-3, Q2, Indexed in SCOPUS .

2. Prasannavenkatesan T, “Predictive Analysis of Cardiovascular Disease using Gradient Boosting based Learning and Recursive Feature Elimination Technique”, Intelligent Systems with Applications, Elsevier, 2022, ISSN: 2667-3053, DOI: https://doi.org/10.1016/j.iswa.2022.200121, Q1, Indexed in SCOPUS.

3. Prasannavenkatesan T, “Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model”, EAI Endorsed Transactions on Pervasive Health and Technology, vol. 7, no. 26, e3, 2021, ISSN: 2411-7145, DOI: https://doi.org/10.4108/eai.3-2-2021.168601, Q3, Indexed in SCOPUS.

4. Prasannavenkatesan Theerthagiri, Gopala Krishnan C and Nishan AH, “Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique”, EAI Endorsed Transactions on Pervasive Health and Technology, vol. 7, no. 26, e5, 2021, ISSN: 2411-7145, DOI: https://doi.org/10.4108/eai.17-3-2021.169032, Q3, Indexed in SCOPUS.

5. Prasannavenkatesan T, Jeena Jacob, I., Usha Ruby, A. and Yendapalli, V., 2021. Prediction of COVID-19 Possibilities using K-Nearest Neighbour Classification Algorithm. International Journal of Current Research and Review, vol, 13, no. 06, pp.156. ISSN: 2231-2196, DOI: http://dx.doi.org/10.31782/IJCRR.2021.SP173, Indexed in SCOPUS.

6. Prasannavenkatesan T, Vamsidhar Yi, "COVID-19: spreading possibilities of human–animal–human and preventive measures", Journal of Health Research, vol, 32, no. 04, 2021, doi: https://doi.org/10.1108/JHR-09-2020-0390, Q3, Indexed in SCOPUS.

7. T. Prasannavenkatesan, “Imposing Packet Relaying for Mobile Adhoc Networks using Genetic Algorithm”, International Journal of Sensors, Wireless Communications and Control, vol. 10, no. 4, pp. 617-624. 2020, ISSN: 2210-3287, DOI: https://doi.org/10.2174/2210327910 666200320090234, Q4, Indexed in SCOPUS.

8. T. Prasannavenkatesan and T. Menakadevi, “Resource-based Routing Protocol for Mobile Adhoc Networks”, Songklanakarin Journal of Science and Technology, vol. 42, no. 4, pp. 889-896, 2020, ISSN: 2408-1779, https://doi.org/10.14456/sjst-psu.2020.114, Q3, Indexed in SCOPUS.

9. T. Prasannavenkatesan and T. Menakadevi, “Elephant Intrusion warning System using IoT and 6LoWPAN”, International Journal of Sensors, Wireless Communications and Control, vol. 10, no. 4, pp. 605 – 616, 2020, DOI: https://doi.org/10.2174/2210327909666191129092006, ISSN: 2210-3287, Q4, Indexed in SCOPUS, (Funded by AICTE).

10.  M. Sathishkumar, A. K. Reshmy, S. Sabaria, Prasannavenkatesan Theerthagiri, “An investigation of wine quality testing using machine learning techniques”, IAES International Journal of Artificial Intelligence, Vol. 12, No. 2, June 2023, pp. 747~754, ISSN: 2252-8938, DOI: https://doi.org/10.11591/ijai.v12.i2.pp747-754, Q2, Indexed in SCOPUS.

11.  Ruby, A.U., George Chellin Chandran, J., T. Prasannavenkatesan, Chaithanya, B.N., Renuka R Patil & Swasthika Jain. “Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model”, Optical Memory and Neural Networks, Springer, Vol. 33, No. 1, pp. 86–96, 2024, ISSN: 1060-992X, DOI: https://doi.org/10.3103/S1060992X24010107, Q3, Indexed in SCOPUS.

12. Husna Tabassum, and T. Prasannavenkatesan, “Review of image processing and artificial intelligence methodologies for apple leaf disease diagnosis”, IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 13, No. 3, September 2024, pp. 2459~2471, ISSN: 2252-8938, DOI: https://doi.org/10.11591/ijai.v13.i3.pp2459-2471, Q2, Indexed in SCOPUS. 

13. M. Sathishkumar, P. C. Kishoreraja, C. Joseph, Reji. M, Prasannavenkatesan Theerthagiri, “Hybrid Intrusion Detection Model for Hierarchical Wireless Sensor Network using Federated Learning”, IAES International Journal of Artificial Intelligence, Vol. 12, No. 2, June 2023, pp. 747~754, ISSN: 2252-8938, DOI: 10.11591/ijai.v99.i1.pp1-1x, Q2, Indexed in SCOPUS. 

SCOPUS/WEB OF SCIENCE INDEXED BOOK CHAPTER PUBLICATIONS

1.     Gopala Krishnan C., Nishan A.H., Prasannavenkatesan T., Jeena Jacob I., Komarasamy G, “Two Dimensional and Gesture Based Medical Visualization Interface and Image Processing Methodologies to Aid and Diagnose of Lung Cancer”, In: Nayak J., Favorskaya M.N., Jain S., Naik B., Mishra M. (eds) Advanced Machine Learning Approaches in Cancer Prognosis. Intelligent Systems Reference Library, vol. 204, no. 11, pp. 297-314, 2021. Springer, Cham. https://doi.org/10.1007/978-3-030-71975-3_11, Indexed in SCOPUS.

2. Gopala Krishnan C., Harold Robinson Y., Golden Julie E., Nishan A.H., Theerthagiri P., Mohan Gowda V. (2022) Machine Learning Techniques for Biometric Fingerprint Recognition Using the Magnitudes to Provide Privacy and Integrity. In: Kumar R., Sharma R., Pattnaik P.K. (eds) Multimedia Technologies in the Internet of Things Environment, Volume 2. Studies in Big Data, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-3828-2_11, Indexed in SCOPUS.

3. Theerthagiri P., Gopala Krishnan C. (2022) Extreme Learning-Based Intellectual Lung Cancer Classification Using Artificial Intelligence. In: Tyagi A.K., Abraham A., Kaklauskas A. (eds) Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-6542-4_19.

4. Theerthagiri, P. (2022). Social Sentiment Analysis Using Features Based Intelligent Learning Techniques. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_6 , Indexed in SCOPUS.

5. Saraswathi, S., Gopala Krishnan, C., Theerthagiri, P. (2023). Gesture-Based Smart-Assistive Device for Elderly and Disabled People Using IoT. In: Sindhwani, N., Anand, R., Niranjanamurthy, M., Chander Verma, D., Valentina, E.B. (eds) IoT Based Smart Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-04524-0_5, Indexed in SCOPUS.

6. Theerthagiri, P. (2022). Estimation of Bitcoin Price Trends Using Supervised Learning Approaches. In: Rajesh D, Vani R, SK Islam, B Balusamy, C Hsu. (Eds.) Quantum Blockchain: An Emerging Cryptographic Paradigm, Wiley, Scrivener Publishing, ISBN: 9781119836728, pp.341-355, https://doi.org/10.1002/9781119836728.ch14, Indexed in SCOPUS.

7. Theerthagiri, P. (2022). Genomics and neural networks in electrical load forecasting with computational intelligence. In: AK Tyagi, A Abraham. (Eds.) Data Science for Genomics, Elsevier, Academic Press, ISBN: 9780323983525, pp.1-10, https://doi.org/10.​1016/​B978-0-323-98352-5.​00009-4, Indexed in SCOPUS.

8. Theerthagiri P., (2023) Defective and Failure Sensor Detection and Removal in a Wireless Sensor Network. In: Chandra, S., Rathishchandra, R, G., K.V.S. Sairam, Ashish S., (Eds.). Modelling And Optimization of Optical Communication Networks, Wiley, Scrivener Publishing, ISBN: 9781119839200, https://doi.org/10.1002/9781119839569.ch4, Indexed in SCOPUS.

SCOPUS/WEB OF SCIENCE INDEXED INT. CONFERENCE PUBLICATIONS

1. Theerthagiri, P., Ghose Basha. Deepfake Face Detection Using Deep InceptionNet Learning Algorithm. In 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE, 18-19 February 2023, Bhopal, India, DOI: https://doi.org/10.1109/SCEECS57921.2023.10063128, Indexed in SCOPUS.

2. Husna Tabassum, and T. Prasannavenkatesan, “Performance Analysis of AI-based Learning Models on Leaf Disease Prediction”, 4th International Conference on Circuits, Control, Communication and Computing, IEEE, 21st to 23rd December 2022, Bengaluru, India, DOI: https://doi.org/10.1109/I4C57141.2022.10057794, Indexed in SCOPUS.

3. T. Prasannavenkatesan and T. Menakadevi, “Significance of Scalability for On-Demand Routing Protocols in MANETs”, proceedings of IEEE Conference on Emerging Devices & Smart Systems, Namakkal, March 4-5, pp. 76-82, 2016, https://doi.org/10.1109 /ICEDSS.2016.7587794, Indexed in SCOPUS.

4. T. Prasannavenkatesan, R. Raja, and P. Ganeshkumar, “PDA-Misbehaving Node Detection & Prevention for MANETs,” proceedings of IEEE Int. Cof. Communication and Signal Processing, Melmaruvathur, pp. 1808–1812, April 2014, https://doi.org/10.1109/ICCSP.2014.6950037, Indexed in SCOPUS.

5. T. Prasannavenkatesan, P. Rajakumar, and A. Pitchaikkannu, “Overview of Proactive Routing Protocols in MANET,” proceedings of IEEE Int. Conference on Communication Systems & Network Technologies, Bhopal, pp. 173-177, April 2014. https://doi.org/10.1109/CSNT.2014.42, Indexed in SCOPUS.

6. P. Rajakumar, T. Prasannavenkatesan, and A. Pitchaikkannu, “Security Attacks and Detection Schemes in MANET,” proceedings of IEEE Int. Conference on Electronics and Communication Systems, Coimbatore, vol. 2, pp. 363–367, Feb 2014, https://doi.org/10.1109/ECS.2014.6892808, Indexed in SCOPUS.

BOOK CHAPTER PUBLICATIONS

1. Kommina L., Theerthagiri P., Payyavula Y., Vemula P.S., Reddy G.D. (2021) Post-Stroke Readmission Prediction Model Using Machine Learning Algorithms. In: Mathur R., Gupta C.P., Katewa V., Jat D.S., Yadav N. (eds) Emerging Trends in Data Driven Computing and Communications. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3915-9_4.

2. Theerthagiri P., Gopala Krishnan C. (2022) Finger Vein Authentication Using Convolutional Neural Networks and Feature Extraction. In: Mohan, N., Gupta, S., & Liu, C. M. (Eds.). Society 5.0 and the Future of Emerging Computational Technologies: Practical Solutions, Examples, and Case Studies. Taylor & Francis, CRC Press, ISBN: ‎978-1032026039, https://doi.org/10.1201/9781003184140-14.

3. C. Gopala Krishnan, A.H. Nishan., Theerthagiri P., (2022) Cancerous or Non-Cancerous Cell Detection on a Field-Programmable Gate Array Medical Image Segmentation Using Xilinx System. In: Mohan, N., Gupta, S., & Liu, C. M. (Eds.). Society 5.0 and the Future of Emerging Computational Technologies: Practical Solutions, Examples, and Case Studies. Taylor & Francis, CRC Press, ISBN: 978-1032026039, https://doi.org/10.1201/9781003184140-10.

4. Theerthagiri P., (2022) Machine Learning Algorithms for Bitcoin Price Prediction. In: Kumar T, Ajith A, A. Kaklauskas, N. Sreenath, Gillala R, Shaveta M. (Eds.). Security and Privacy-Preserving Techniques in Wireless Robotics. Taylor & Francis, CRC Press, ISBN: 9781003156406, https://doi.org/10.1201/9781003156406-15.

5. Theerthagiri P., (2022) An investigation on Cooperative Communication Techniques in Mobile Ad Hoc Networks. In: Suhel, A. H., Rajeev, K., (Eds.). Computational Intelligent Security in Wireless Communications. Taylor & Francis, CRC Press, ISBN: 9781003323426, http://dx.doi.org/10.1201/9781003323426-1.

6. Theerthagiri, P. (2023). Plant Leaf Disease Detection Using Supervised Machine Learning Algorithm. In: Swarnkar, S, Patra, JP., Tran, TA., Bharat, B., S, Biswas., (Eds.). Multimedia Data Processing and Computing. Taylor & Francis, CRC Press, (pp. 83-95), ISBN: 9781003391272, https://doi.org/10.1201/9781003391272-8.

7. Theerthagiri, P. (2023). Delay-sensitive and Energy-efficient Approach for Improving Longevity of Wireless Sensor Networks. In: Swarnkar, S, Patra, JP., Tran, TA., Bharat, B., S, Biswas., (Eds.). Multimedia Data Processing and Computing. Taylor & Francis, CRC Press, (pp. 43-55), ISBN: 9781003391272, https://doi.org/10.1201/9781003391272-4.

INTERNATIONAL/NATIONAL JOURNAL PUBLICATIONS (UGC-CARE INDEXED)

1. Usha Ruby, A., Prasannavenkatesan T., Jeena Jacob, I., Vamsidhar, Y., “Binary cross entropy with deep learning technique for image classification International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 4, pp. 5393-5397, 2020, https://doi.org/10.30534/ijatcse/2020/175942020.

2. I.Jeena Jacob, A. Usha Ruby, Prasanna Venkatesan, Vamsidhar Yendapalli, D.Sathya, "Classification of WBC dataset using supervised learning techniques." European Journal of Molecular & Clinical Medicine 8, no. 1 (2021): 1952-1955.

3. Bindupriya, Prasanna Venkatesan T, Vinay Kumar, Vishnuvardhan, Subash, "A Cost Effective Campus Automation System Using BOLT-IOT", International Journal of Control and Automation, vol 13, no 4, pp. 935 - 941, 2020, ISSN: 2005-4297, DOI: http://sersc.org/journals/index.php/IJCA /article/view/1889.

4. T. Menakadevi, and T. Prasannavenkatesan, “Routing Protocols for the Aircraft Ad hoc Networks”, International Journal of Recent Technology and Engineering, vol.8, no.4, pp.11177-83, 2019, ISSN: 2277-3878, DOI: https://doi.org/10.35940/ijrte.D8124.118419,(ARDB Funded).

5. T. Prasannavenkatesan and T. Menakadevi, “FMPM: Futuristic Mobility Prediction Model for Mobile Adhoc Networks Using Auto-Regressive Integrated Moving Average”, Acta Graphica, vol.29,no.2,pp.7-17,2018,ISSN:1848-3828, DOI: https://doi.org/10.25027/agj2017.28.v29i2.136.

6. T. Prasannavenkatesan, P. Rajakumar, and A. Pitchaikkannu, “An Effective Intrusion Detection System for MANETs,” International Journal of Computer Applications, vol. 3, pp. 29-34, March 2014, ISSN: 0975 – 8887, Indexed in DOAJ.

INTERNATIONAL/NATIONAL CONFERENCE PUBLICATIONS

1. T. Prasannavenkatesan, K. Udhayakumar, and R. Ramkumar “Security Attacks and Detection Techniques for MANET” proceedings of International Conference on Advances in Computer Engineering & Applications (ICACEA), vol. 15 (42), pp. 89-93, Ghaziabad, March 2014.

2. T. Prasannavenkatesan, K. Udhayakumar, T. Hervinpraison, and S. Ponmudi “Cognitive Radio Technology and Spectrum Sensing in MANETs,” proceedings of International Conference on Trends in Technology Convergence (TITCON), Salem, pp. 168-172, April 2014.

3. T. Prasannavenkatesan, and T. Viswanathan, “Detection and Prevention of Malicious Nodes in Mobile ad-hoc networks,” proceedings of National Conference on Recent Trends in Engineering and Technology, Trivandrum, pp. 1-6, April 2014.