Dr. Vivek Kumar Singh

About

Dr. Vivek Kumar Singh is working as an Assistant Professor in the Department of Computer Science and Engineering, School of Engineering and Technology at Sharda University, Greater Noida. He received Ph.D. in Computer Science and Engineering from National Institute of Technology, Uttarakhand, M.Tech in Software Engineering from Motilal Nehru National Institute of Technology Allahabad and B. E. in Computer Science and Engineering from  Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. He has more than 12 years of industry, teaching and research experience in various reputed organizations. He has published good research papers in reputed international journals and conferences.  His areas of interest are Computer Vision, Artificial Intelligence, Machine Learning and Visual Attention Modeling.

Experience

• 8 Years (Teaching) + 3 Years (Research) + 1 Year (Industry)

Qualification

• Ph.D.
• M. Tech
• B.E.

Award & Recognition

• Awarded best paper In International Conference on Robotics, Control and Computer Vision (ICRCCV 2022), National Institute of Technology, Uttarakhand, India in association with Eötvös Loránd University, Hungary (Savaria Institute of Technology), 2022.
• Awarded best paper In International Conference on Evolving Technologies for Computing, Communication and Smart World (ETCCS-2020), CDAC Noida (NOIDA), India (2020).

Research

Journal:

• Singh, V. K., & Kumar, N. (2022). CoBRa: convex hull based random walks for salient object detection. Multimedia Tools and Applications, 1-21, Springer, (SCI-E, Impact factor: 2.757).
• Singh, V. K., & Kumar, N. (2021). SOFT: salient object detection based on feature combination using teaching-learning-based optimization. Signal, Image and Video Processing, 1-8, Springer, (SCI-E, Impact factor: 2.157).
• Singh, V. K., & Kumar, N. (2021). CHELM: Convex Hull based Extreme Learning Machine for Salient Object Detection. Multimedia Tools and Applications, 80(9)13535-13558, Springer, (SCI-E, Impact factor: 2.757).
• Singh, V. K., Kumar, N., & Singh, N. (2020). A hybrid approach using color spatial variance and novel object position prior for salient object detection. Multimedia Tools and Applications, 79(39), 30045-30067, Springer, (SCI-E, Impact factor: 2.757).
• Singh, V. K., Kumar, N., & Madhavan, S. (2019). Saliency Boosting: a novel framework to refine salient object detection. Artificial Intelligence Review, 1-42, Springer, (SCI, Impact factor: 8.139).
• Singh, V. K., & Kumar, N. (2019). Saliency bagging: a novel framework for robust salient object detection. The Visual Computer, 1-19, Springer, (SCI-E, Impact factor: 2.601).

Conference:

• Singh, V. K., & Kumar, N (2022). Challenges and Opportunity for Salient Object Detection in COVID-19 Era: A Study. (Presented) in International Conference on Robotics, Control and Computer Vision, Springer, (Scopus Indexed).
• Singh, V. K., & Kumar, N (2020). A Novel Fusion Framework for Salient Object Detection Based on Support Vector Regression. In Evolving Technologies for Computing, Communication and Smart World (pp. 437-450). Springer, Singapore, (Scopus Indexed).
• Singh, V. K., & Kumar, N. (2019). U-fin: unsupervised feature integration approach for salient object detection. In Advances in Communication and Computational Technology (pp. 1173-1188). Springer, Singapore, (Scopus Indexed).
• Singh, V. K., Kumar, R., & Sahana, S. (2017). To enhance the reliability and energy efficiency of WSN using new clustering approach. In 2017 International Conference on Computing, Communication and Automation (ICCCA) (pp. 488-493). IEEE.

Area of Interest

• Computer Vision
• Artificial Intelligence
• Machine Learning and Visual Attention Modeling