Yagyanath Rimal

Associate Professors
School of Engineering, Pokhara University
Email: [email protected]
Mobile: 9864897561

   

Ph.D. in Machine Learning and Model Optimization, IIS (Deemed University), Jaipur, India – 2025
Master of Science in Information Technology (M.Sc. IT), Sikkim Manipal University of Health, Medical & Technological Sciences, India – 2006
Master’s Degree in Rural Development (MRD), TU– 2004

Machine Learning & Artificial Intelligence, Data Analytics & Data Mining, Programming & Software Development, Health & Biomedical Data Analysis, Network Systems & Cybersecurity (Basic–Intermediate)

  1. Rimal, Y., Sharma, N., Alsadoon, A., & Abbas, S. K. (2025). A comparative analysis of ensemble AutoML machine learning prediction accuracy of STEM student grade prediction: A multi-class classification perspective. Multimedia Tools and Applications. https://link.springer.com/article/10.1007/s11042-024-20554-8
  2. Rimal, Y., & Sharma, N. (2025). Ensemble machine learning prediction accuracy: Local vs global precision, recall for multiclass grade performance of engineering students. Frontiers in Education. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1571133/full
  3. Rimal, Y., & Sharma, N. (2025). Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy. Scientific Reports. https://www.nature.com/articles/s41598-025-93675-1
  4. Rimal, Y., & Sharma, N. (2023). Hyperparameter optimization: A comparative machine learning model analysis for enhanced heart disease prediction accuracy. Multimedia Tools and Applications, 83(18), 55091–55107. https://link.springer.com/article/10.1007/s11042-023-17273-x
  5. Rimal, Y., Sharma, N., & Alsadoon, A. (2024). The accuracy of machine learning models relies on hyperparameter tuning: Student result classification using random forest, randomized search, grid search, Bayesian, genetic, and Optuna algorithms. Multimedia Tools and Applications, 83(30), 74349–74364. https://link.springer.com/article/10.1007/s11042-024-18426-2
  6. Rimal, Y., Paudel, S., Sharma, N., & Alsadoon, A. (2023). Machine learning model matters its accuracy: A comparative study of ensemble learning and AutoML using heart disease prediction. Multimedia Tools and Applications, 83(12), 35025–35042. https://link.springer.com/article/10.1007/s11042-023-16380-z
  7. Rawat, R., Rimal, Y. N., William, P., Dahima, S., Gupta, S., & Sankaran, K. S. (2022). Malware threat affecting financial organization analysis using machine learning approach. International Journal of Information Technology and Web Engineering. https://www.igi-global.com/article/malware-threat-affecting-financial-organization-analysis-using-machine-learning-approach/304051
  8. Rimal, Y., & Sharma, N. (2023, November 23). Ensemble machine learning model improves prediction accuracy for academic performance: A comparative study of default ML vs boosting algorithm. Proceedings of ICIMMI. https://dl.acm.org/doi/10.1145/3647444.3652473
  9. Rimal, Y., Sharma, N., & Rathore, V. S. (2024). Ensemble machine learning one-versus-rest multilevel grade classification and prediction. International Conference on Universal Threats in Expert Applications and Solutions. https://link.springer.com/chapter/10.1007/978-981-97-3810-6_40
  10. Rimal, Y., Rathore, V. S., Pageni, S., Samanta, D., Karuppiah, M., & Singh, D. (2022). Neural network-based student grade prediction model. 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS). https://ieeexplore.ieee.org/document/10076666
  11. Rimal, Y., Rai, B., Rathore, V. S., & Pageni, S. (2022). COVID cases analysis: Dynamic animated plots using R programming. In Artificial Intelligence Applications in a Pandemic. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003126218-6
  12. Rimal, Y., Arif, M., & Jain, A. (2022). Animated line, bar, and bubble plots for better COVID case analysis using R programming. In Biomedical Engineering Applications for People with Disabilities and the Elderly in the COVID-19 Pandemic and Beyond. https://www.sciencedirect.com/science/article/abs/pii/B9780323851749000236
  13. Rimal, Y., Pandit, P., Gocchait, S., Butt, S. A., & Obaid, A. J. (2021). Hyperparameter determines the best learning curve on single, multi-layer and deep neural networks of student grade prediction of Pokhara University Nepal. Journal of Physics: Conference Series. https://iopscience.iop.org/article/10.1088/1742-6596/1804/1/012054
  14. Rimal, Y., Gochhait, S., & Bisht, A. (2021). Data interpretation and visualization of COVID-19 cases using R programming. Informatics in Medicine Unlocked. https://pubmed.ncbi.nlm.nih.gov/34485681/
  15. Gochhait, S., Rimal, Y., & Pageni, S. (2021). The comparison of forward and backward neural network model – A study on the prediction of student grade. WSEAS Transactions on Systems. https://wseas.com/journals/articles.php?id=424
  16. Rimal, Y. (2021). Reproducible academic writing and interactive data visualization using R Markdown. Advances in Intelligent Systems and Computing. https://link.springer.com/chapter/10.1007/978-981-15-6014-9_73
  17. Dash, L., Pattanayak, B. K., Singh, D., Samanta, D., & Rimal, Y. (2023). Network lifetime enhancement by elimination of spatially and temporally correlated RFID surveillance data in WSNs. Wireless Communications and Mobile Computing. https://onlinelibrary.wiley.com/doi/10.1155/2022/2437578