Yagyanath Rimal
Pokhara University, College of Information Technology & Engineering, IISU , Jaipur
0000-0003-1045-7728
About
Yagyanath Rimal (Ph. D) is an Assistant Professor in the Department of Computer Science and Engineering at Pokhara University, Nepal. His academic and research expertise lies in Machine Learning, Data Science, Artificial Intelligence, and Data Mining. He has extensive teaching experience in core programming and computing subjects, including C, C++, Java, and Web Application Development, and has successfully supervised undergraduate and postgraduate research in areas such as data analytics, bioinformatics, data mining, and computer engineering.
Dr. Rimal has authored and co-authored several international research articles and scholarly books in his areas of specialization. He actively engages in academic research and values interdisciplinary and international collaboration aimed at producing high-impact scientific publications. Currently, he is seeking a postdoctoral research position in Machine Learning and Artificial Intelligence, with a particular focus on Data Mining, Data Analytics, and Statistical Learning, and is open to collaborative research opportunities with global scholars and research institutions
Employment
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Pokhara University Assistant Professor2009 - Present
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College of Information Technology & Engineering Assistant Professors2006 - 2009
Education
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IISU , Jaipur PHD IN Computer Science2021 - 2024
Projects & Funding
Projects & funding information is unavailable.
Publications (19)
- Deep Learning and Pattern Recognition for Anomaly Detection in Multimodal Medical Imaging With Sensor‐Based Data Fusion Save
- Metaheuristic Optimization Algorithm for Vulnerability Detection in Web of Things Environment Save
- An Explainable and Carbon‐Aware Stacked Ensemble Model for Phishing and Impersonation Detection in Online Social Networks Save
- Cyber Laws and their Role in Safeguarding Children Online Save
- Screen Time and Safety Save
- Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy Save
- Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students Save
- A comparative analysis of ensemble autoML machine learning prediction accuracy of STEM student grade prediction: a multi-class classification prospective Save
- Review of Research Methodology and IT for Business and Threat Management Save
- Ensemble Machine Learning One-Versus-Rest Multilevel Grade Classification and Prediction Save