· Year 2022-2023
Review four research papers in June 2021 at GUGON IEEE Conference 2021
·Invertis Journal of Science and Technology.“Software defect prediction based on Multivariate Statistical Method and Machine Learning Techniques “by Mohammad Zubair Khan in Department of computer science, College of computer science and Engineering, Taibah University, Madinah, Saudi Arabia,2021
IJST-2020-2276-R1, Title: Weighted Mel Frequency Cepstral Coefficient based feature extraction for automatic assessment of stuttered speech using Bi-directional LSTM Submitted Date: 07 January 2021
“Internet of Things Based Flexible Architectures for Public Distribution System in Lockdown Situations” 9th IEEE international conference on systemmodeling& Advancement in Research trends on 04-05 Dec2020 SMART 2020 TMU
A Review paper on Data Security in Cloud Computing Environment” 9th IEEE international conference on system modeling & Advancement in Research trends on 04-05 Dec2020, SMART 2020 TMU
“Ecosystem Preparedness & Learning 4.0: A Case Study of Higher Education Scenario in Goa” 9th IEEE international conference on system modeling& Advancement in Research trends on 04-05 Dec2020 SMART 2020 TMU
·31-Jan-2019, ID Access-2019-02596 entitled "automatic classification method for software vulnerability based on deep neural network" with Dr. Wang as contact author, which has been submitted to IEEE Access.
23-Jan-2019, ID Access-2019-02445 entitled "An empirical study on the temporary file smell in Dockerfiles" with Dr. Xu as contact author, which has been submitted.
01-Apr-2019.ID Access-2019-08918, entitled “DESeeker: Detecting epistatic interactions using a two-stage differential evolution algorithm," .
19-Apr-2019’ ID Access-2019-11226, entitled "A Comprehensive and Flexible Big Data Mining Platform Integrating
User Identification over Digital Social Networks using Fingerprint Authenticationhttps://cmt3.research.microsoft.com/2017
Automatic ECG Signals Recognition based on Time Domain Features Extraction using fiducial mean square Algorithm https://cmt3.research.microsoft.com/2017