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Academic Experts

Dr. MEENAL JAIN

Biography

Highly ambitious researcher with a background in machine learning. Expertise in data analysis and prediction. Published research papers of the same domain in reputed journals and conferences. During my teaching career, I have taught subjects like OOPS, Computer Networks, Data Structure, Python, Computer Graphics.

Research Highlights

Research work integrates machine learning, distributed computing, and real-time analytics to design resilient, adaptive, and scalable solutions for cybersecurity, cloud computing, and IoT-driven networks.

Areas of Interest
  • Machine Learning
  • Computer Networks Data Structure
Publications
  1. M. Jain, G. Kaur (Deceased), and V. Saxena, “A K-Means Clustering and SVM based Hybrid Concept Drift Detection Technique for Network Anomaly Detection,” Expert Systems with Applications, 2022, p.116510, doi: 10.1016/j.eswa.2022.116510. (Indexing: SCI, H Index: 207, Impact Factor: 6.954)
  2. G. Kaur, and M. Jain, “A Comparison of Two Blending-Based Ensemble Techniques for Network Anomaly Detection in Spark Distributed Environment,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 35, no. 2, pp. 71-83, 2020, doi: 10.1504/IJAHUC.2020.109794. (Indexing: SCIE, H Index: 24, SJR: 0.22, Impact Factor: 0.714)
  3. M.Jain,andV.Saxena,“AnECOSVSbasedSVMforNetworkAnomalyDetection.,”InternationalJournal of Data Analysis Techniques and Strategies, 2022, doi: https://doi.org/10.1504/ijdats.2022.121513. (Indexing: Scopus, H Index: 15, SJR:0.25)
  4. M.Jain,andG.Kaur,“DistributedAnomalyDetectionusingConceptDriftDetectionbasedHybrid EnsembleTechniquesinStreamedNetworkData,”ClusterComputing,2021,doi:10.1007/s10586-021-03249-9. (Indexing: SCIE, H Index: 50, SJR: 0.41, Impact Factor:3.458)
  5. M. Jain, and G. Kaur, “A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection,” Journal of Computing and Information Technology, vol. 27, no. 4, pp. 1-16, 2019, doi: 10.20532/cit.2019.1004591. (Indexing: Scopus, H Index: 27, SJR: 0.21)