Vimalkumar B. Vaghela
Government Engineering College, L. D. College of Engineering
About
Desire a challenging position in the area of Academic, which will utilize my Interdisciplinary, Technical, Management Skills and Software Skills in a productive, enthusiastic in an educational organization.
• Having nearly 17 Years and 07 Months of teaching experience.
• Fast & self-directed learner, work effectively independently as well as team player.
• Excellent interpersonal and enterprising skills.
• Biography has been published in the Who's Who in Science and Engineering – 11thEdition, 2010-11, 2013-14, 2016-17.
• Published 30 International Research Papers in field of Ensemble Data Mining, Relational Data Mining, Mobile Augmentation, Computer Network.
• Written and Published a Book with the titled “Ensemble Classifier in Data Mining” in LAMBERT Academic Publisher at Germany.
• Written a Book with title “Operating System” in Dreamtech Publisher.
• Senior Professional Members of Association for Computing Machinery (ACM), Life time Professional Member of Computer Society of India (CSI) & Professional Member of International Association of Engineers (IAENG).
• Research Area includes Data Mining, Data Fusion, Multiple Classifier System, Classification, Relational Data Mining, Rule Induction, Mobile Ad-hoc Network
Employment
-
Government Engineering College Associate Professor2025 - Present
-
L. D. College of Engineering Assistant Professor2011 - 2025
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (6)
- An algorithmic approach for recommendation of movie under a new user cold start problem Save
- Optimization of the neighbor parameter of k-Nearest neighbor algorithm for collaborative filtering Save
- MR-MNBC: MaxRel based feature selection for the multi-relational Naïve Bayesian Classifier Save
- Role of mobile augmentation in mobile application development Save
- Applied Taxonomy Techniques Intended for Strenuous Random Forest Robustness Save
- Boost a weak learner to a strong learner using ensemble system approach Save