Designation: Assistant Professor
Qualification: M.tech and PhD in Computer Science & Engineering MANIT, Bhopal, India
Area of Interest: Machine Learning, Imbalanced Learning, Data Science, Deep Learning
Phone No:
E-Mail:
DR. BHAGAT SINGH RAGHUWANSHI
- About Faculty: I did my Bachelor of Engineering (B.E.) degree in computer science and engineering from the Lakshmi Narain College of Technology (LNCT), Bhopal, India, and the Master of Technology (M.Tech) and Ph.D. (15 SCI Journals) degrees from the Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal respectively. I am Currently working as a Assistant Professor (Artificial Intelligence & Robotics) at the Madhav Institute Of Technology and Science, Gwalior Madhya Pradesh. My research interests include Extreme Learning Machine, Neural Networks, and Machine Learning.
- Education and Qualification:
BE (CSE) LNCT Bhopal, M.tech and PhD in Computer Science & Engineering MANIT, Bhopal, India
- Work Experience:
TEACHING EXPERIENCE (4.5 YEARS)
- Assistant Professor, Information Technology in Madhav Institute Of Technology and Science, Gwalior (M.P.) [Dec ’21 - till date]
- Lakshmi Narain College of Technology (LNCT), Bhopal Assistant Professor, Computer Science & Engineering [July ’12 - June ’16]
RESEARCH/INDUSTRY EXPERIENCE( 3 YEARS)
- Research Assistant, Maulana Azad National Institute of Technology, Bhopal [July ’16 -Jan ’19]
- Assistant Programmer Commercial Tax Department MP [Jan ’19 - Nov’ 21]
- Honors, Award & Recognition:
GATE qualified GATE-2010, 2015, 2016, 2017 and 2018 with 96.85, 92.10, 91.29, 91.62 and
94.95 percentiles respectively.
• Selected in MPPSC-IT exam- 2016.
• Best Faculty Award by state level organization SRIJAN.
• Completed 04 SWAYAM/NPTEL course with ELITE and FDP “ Python for Data Science,
Data Base Management System, Compiler Design, Computer networks and protocols” in
session Jan-June 2022
• Recognized as NPTEL believers and NPTEL discipline star in session Jan -June 2022.
(Received appreciation certificate)MOOCs – Online Certifications: Qualified First phase training (08 Modules) of AICTE
National Initiative for Training of Technical Teachers (NITTT)
Coordinating Institute: NITTTR Chennai
REVIEWER
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Emerging Topics in Computational Intelligence
• Information Fusion
• Knowledge-based Systems
• Information Sciences
• Big Data Research
• Expert Systems with Applications
• European journal of operational research
• Computer Methods and programs in biomedicine
• Journal of The Franklin Institute
• Computer Standards & Interfaces
• INFORMS Journal on Computing
• Arabian Journal for Science and Engineering
• PeerJ Computer Science
• ISA Transactions - Publications:
RESEARCH PAPERS
SCI Journals (15)
(1) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning” Pattern Recognition, vol. 119, pp. 108069, November 2021. (I.F. 8.52)
DOI: https://doi.org/10.1016/j.patcog.2021.108069
(2) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Class-specific extreme learning machine for handling binary class imbalance problem,” Neural Networks, vol. 105, pp. 206-217, September 2018. (I.F. 9.66)
DOI: https://doi.org/10.1016/j.neunet.2018.05.011
(3) Sanyam Shukla, Bhagat Singh Raghuwanshi, “Online sequential class-specific extreme learning machine for binary imbalanced learning,” Neural Networks, vol. 119, pp. 235- 248, November 2019. (I.F. 9.66)
DOI: https://doi.org/10.1016/j.neunet.2019.08.018
(4) Bhagat Singh Raghuwanshi, Sanyam Shukla, “SMOTE based class-specific extreme learning machine for handling class imbalance problem,” Knowledge-Based Systems, vol. 187, pp. 104814, January 2020. (I.F. 8.14)
DOI: https://doi.org/10.1016/j.knosys.2019.06.022
(5) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Class-specific kernelized extreme learning machine for handling binary class imbalance problem,” Applied Soft Computing, vol. 73, pp. 1026-1038, December 2018. (I.F. 8.26)
DOI: https://doi.org/10.1016/j.asoc.2018.10.0112
(6) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning,” Expert Systems With Applications, vol. 121, pp. 244-255, 1 May 2019. (I.F. 8.67)
DOI: https://doi.org/10.1016/j.eswa.2018.12.024
(7) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Minimum class variance class-specific extreme learning machine for imbalanced classification”, Expert Systems with Applications,vol. 178, pp. 114994, 15 September 2021. (I.F. 8.67 )
DOI: https://doi.org/10.1016/j.eswa.2021.114994
(8) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Class imbalance learning using UnderBagging based kernelized extreme learning machine,” Neurocomputing, vol. 329, pp. 172-187, May 2019 .(I.F. 6.00)
DOI: https://doi.org/10.1016/j.neucom.2018.10.056
(9) Bhagat Singh Raghuwanshi, Sanyam Shukla, “UnderBagging based reduced kernelized weighted extreme learning machine for class imbalance learning,” Engineering Applications of Artificial Intelligence, vol. 74, pp. 252-270, September 2018. (I.F. 7.80)DOI: https://doi.org/10.1016/j.engappai.2018.07.002
(10) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Class-specific cost-sensitive boosting WELM for class imbalance learning”, Memetic Computing, vol. 11, issue 3, pp. 263–283 September 2019. (I.F. 4.70)
DOI: https://doi.org/10.1007/s12293-018-0267-4
(11) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Classifying Imbalanced Data using ensemble based reduced kernelized weighted extreme learning machine,” International Journal of Machine Learning and Cybernetics, vol. 10, issue 11, pp. 3071-3097, August 2019.(I.F. 5.60) DOI: https://doi.org/10.1007/s13042-019-01001-9
(12) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Classifying imbalanced data using SMOTE based class-specific kernelized ELM,” International Journal of Machine Learning and Cybernetics, vol. 12, issue 5, pp. 1255–1280, May 2021. (I.F. 5.60)
DOI: https://doi.org/10.1007/s13042-020-01232-1
(13) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Classifying imbalanced data using BalanceCascade based kernelized extreme learning machine,” Pattern Analysis and Applications, vol. 23, issue 3, pp. 1157-1182, August 2020. (I.F. 3.90)
DOI: https://doi.org/10.1007/s10044-019-00844-w
(14) Bhagat Singh Raghuwanshi, Akansha Mangal, Sanyam Shukla, “Universum based kernelized weighted extreme learning machine for imbalanced datasets” International Journal of Machine Learning and Cybernetics, vol. 13, issue 11, pp. 3387–3408, July 2022 (I.F. 5.60) DOI: https://doi.org/10.1007/s13042-022-01601-y
(15) Bhagat Singh Raghuwanshi, “Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem” Soft Computing, vol. 27, issue 8, pp. 4609 - 4626, Dec 2022 (I.F. 4.10) https://doi.org/10.1007/s00500-022-07705-5SCOPUS Journals
(1) Bhagat Singh Raghuwanshi, Sanyam Shukla, “Classifying multiclass imbalanced data using generalized class-specific extreme learning machine” Progress in Artificial Intelligence, vol. 10, issue 3, pp. 259–281, September 2021. (Q3(SCOPUS), H-18)
DOI: https://doi.org/10.1007/s13748-021-00236-4
IEEE International Conferences
(1) Sukirty Jain, Sanyam Shukla, Bhagat Singh Raghuwanshi, “Analysis of ordering based ensemble pruning techniques for Voting based Extreme Learning Machine”, 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS),
pages. 1-5, 2018
DOI: 10.1109/SCEECS.2018.8546952
(2) Mona Singhal, Sanyam Shukla, Bhagat Singh Raghuwanshi,“ Voting based Extreme learning Machine with Spectral Coefficient Pruning for binary Classification”, 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS),
pages. 1-6, 2018
DOI: 10.1109/SCEECS.2018.8546989
