Assistant Professor (SG)

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Assistant Professor (SG)

Areas of Interest: Software Engineering, Software Product Management and Software Quality

Education: B.E, M.Tech, Ph.D.

E-mail: dinesh.verma@juet.ac.in

Contact No. : 8770511036 Ext. 175

Dr. Dinesh Kumar Verma

Dr. Dinesh Kumar Verma has done his Ph.D. in 2016 from Jaypee University of Engineering and Technology in the Department of Computer Science and Engineering. He earned his M. Tech. in Software Engineering  in 2006 from ABV-IIITM, Gwalior, and B.E. in Computer Science & Engineering in 2004 from Madhav Institute of Technology and Science (MITS), Gwalior. Dr. Verma has worked in the area of Software Engineering, Software Product Management and Software Quality. He has published various research papers and Book Chapters in national and international journals of repute. Dr. Verma has guided three Ph.D. students in the area of Software Engineering, Precision Agriculture Integration by Machine Learning, and Genome Sequence Analysis guiding two more students in the area of Machine Learning. Many students of UG and PG courses in Computer Science and IT have also done their projects and dissertations under his supervision. Dr. Verma is an active researcher with over 15 articles published in SCI/SCOPUS-indexed journals.

Academic Profiles:

Google Scholar: https://scholar.google.com/citations?hl=en&user=hL-Pny8AAAAJ

ORCID: https://orcid.org/0000-0002-9288-6819

Scopus: https://www.scopus.com/authid/detail.uri?authorId=56785299700

Vidwan/IRINS profile: https://juet.irins.org/profile/125425

LinkedIn: https://www.linkedin.com/in/dr-dinesh-kumar-verma-11496614

 

 

  1. P. Goyal, D. K. Verma, and S. Kumar, “Optimal Prediction and Prevention of Active Fruit Plant Disease Monitoring Using a Deep Learning Recommendation System for Smart Agriculture.” Journal of Phytopathology173, no. 6: e70211. https://doi.org/10.1111/jph.70211.
  2. S. Dubey, D. K. Verma,  and M. Kumar, “Identification of Infectious Disease Like COVID-19 Gene Biomarkers Using a Clear Artificial Intelligence Approach,” SN Computer Science, vol. 6, no. 4, 2025.
  3. B. K. Shrivash, D. K. Verma, and P. Pandey, “A Novel Framework for Text Preprocessing using NLP Approaches and Classification using Random Forest Grid Search Technique for Sentiment Analysis,” Economic Computation and Economic Cybernetics Studies and Research, vol. 59, no. 2, pp. 91–107, 2025.
  4. P. Goyal, D. K. Verma, and S. Kumar, “Detection and Monitoring for Enhanced Prevention of Grain Plant Disease Using Classification-Based Deep Ensemble Neural Networks in Smart Agriculture,” International Journal of Remote Sensing, vol. 46, no. 5, pp. 1992–2022, 2025.
  5. P. Goyal, D. K. Verma, and S. Kumar, “Plant Leaf Disease Detection Using an Optimized Evolutionary Gravitational Neocognitron Neural Network,” National Academy Science Letters, vol. 47, no. 4, pp. 347–354, 2024.
  6. S. Dubey, D. K. Verma,  and M. Kumar, “Severe Acute Respiratory Syndrome Coronavirus-2 GenoAnalyzer and Mutagenic Anomaly Detector Using FCMFI and NSCE,” International Journal of Biological Macromolecules, vol. 258, 2024.
  7. S. Dubey, D. K. Verma,  and M. Kumar, “Identification of Unique Genomic Signatures in Viral Immunogenic Syndrome Using FIMAR and FCSM Methods,” Economic Computation and Economic Cybernetics Studies and Research, vol. 58, no. 2, pp. 165–181, 2024.
  8. S. Dubey, D. K. Verma,  and M. Kumar, “Real-Time Infectious Disease Endurance Indicator System for Scientific Decisions using Machine Learning and Rapid Data Processing,” PeerJ Computer Science, vol. 10, 2024.
  9. B. K. Shrivash, D. K. Verma,  and P. Pandey, “An Effective Framework for Sentiment Analysis of Hindi Sentiments Using Deep Learning Technique,” Wireless Personal Communications, vol. 132, no. 3, pp. 2097–2110, 2023.
  10. P. Goyal, D. K. Verma,  and S. Kumar, “Diagnosis of Plant Leaf Diseases Using Image-Based Detection and Prediction Using Machine Learning Approach,” Economic Computation and Economic Cybernetics Studies and Research, vol. 57, no. 4, pp. 293–312, 2023.
  11. P. Rai, D. K. Verma,  and S. Kumar, “Prediction of Software Effort in the Early Stage of Software Development: A Hybrid Model,” IEEE Canadian Journal of Electrical and Computer Engineering, vol. 44, no. 3, pp. 376–383, 2021.
  12. D. K. Verma and S. Kumar, “Prediction of Defect Density for Open Source Software Using Repository Metrics,” Journal of Web Engineering, vol. 16, no. 3–4, pp. 294–311, 2017.
  13. D. K. Verma and S. Kumar, “An Improved Approach for Reduction of Defect Density Using Optimal Module Sizes,” Advances in Software Engineering, vol. 2014, Article ID 803530, pp. 1–7, Aug. 2014, doi: 10.1155/2014/803530.

[1] D. K. Verma, S. Singh, S. Dubey, and K. Raghuwanshi, “Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning,” in Communications in Computer and Information Science (CCIS), vol. 2194, pp. 334–345, 2025.

[2] D. K. Verma, S. Dubey, and M. Kumar, “Uncovering AI Potential Techniques for Infectious Disease,” in CCIS, vol. 2050, pp. 167–177, 2024.

[3] D. K. Verma, S. Dubey, and M. Kumar, “Exploring the Molecular Diversity of SARS, Ebola, MERS, and SARS-CoV-2 Viruses Using ViroGen,” in CCIS, vol. 2049, pp. 164–172, 2024.

[4] D. K. Verma et al., “Generic Framework of New Era Artificial Intelligence and Its Applications,” in CCIS, vol. 2049, pp. 149–163, 2024.

[5] D. K. Verma et al., “Revealing a State-of-the-Art Machine Learning Architecture for Hepatitis Disease,” in PICET 2024, 2024.

[6] D. K. Verma, B. K. Shrivash, and P. Pandey, “An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches,” in CCIS, vol. 1848, pp. 340–350, 2023.

[7] D. K. Verma et al., “Natural Language Processing to Improve Optimal Customized Treatment in Clinical Decision Support Systems,” in IEEE ICTBIG 2023, 2023.

[8] D. K. Verma et al., “Why Big Data and Data Analytics for Smart City,” in IEEE CVMI 2023, 2023.

[9] D. K. Verma, “An Analysis on Machine Learning Approaches for Sentiment Analysis,” in Smart Innovation Systems and Technologies, vol. 235, pp. 499–513, 2022.

[10] D. K. Verma et al., “Machine Learning Approaches in Deal with the COVID-19, Comprehensive Study,” in ECS Transactions, vol. 107, no. 1, pp. 17815–17827, 2022.

[11] D. K. Verma, P. Rai, and S. Kumar, “Prediction of Effort Required to Design Software for Smart City Applications,” in Journal of Physics: Conference Series, vol. 1714, 2021.

[12] D. K. Verma, P. Rai, and S. Kumar, “A Hybrid Machine Learning Framework for Prediction of Software Effort,” in CCIS, vol. 1244, pp. 187–200, 2020.

[13] D. K. Verma, P. Rai, and S. Kumar, “Prediction of Software Effort Using Design Metrics: An Empirical Investigation,” in Lecture Notes in Networks and Systems, vol. 100, pp. 627–637, 2020.

[14] D. K. Verma et al., “Empirical Study of Defects Dependency on Software Metrics Using Clustering Approach,” in IEEE UPCON, 2016.

[15] D. K. Verma, N. Mandhan, and S. Kumar, “Analysis of Approach for Predicting Software Defect Density Using Static Metrics,” in ICCCA, pp. 880–886, 2015.

[1] D. K. Verma, P. Rai, and S. Kumar, “Prediction of Software Effort in the Early Stage of Software Development: A Hybrid Model,” IEEE Canadian Journal of Electrical and Computer Engineering, vol. 44, no. 3, pp. 376–383, 2021.

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