Vimal K. Shrivastava
Vimal K. Shrivastava
Assistant Professor
Dr. Vimal K. Shrivastava has received his BE degree in Electronics and Telecommunication Engineering from the Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh in 2009 and MTech degree in Electronics Instrumentation Engineering from National Institute of Technology Warangal, Andhra Pradesh in 2011. Later, he received his PhD degree from National Institute of Technology, Raipur, India in 2016. He is currently working as an Assistant Professor in the School of Electronics Engineering of Kalinga Institute of Industrial Technology, Bhubaneswar, India. His area of research includes Image processing, soft computing techniques, machine learning, active learning and deep learning. He has published many papers in SCI and SCOPUS indexed journals and international conferences on the aforementioned research areas.
Profile Links
Email :
[email protected]
Google Scholar :
https://scholar.google.co.in/citations?user=1llpltMAAAAJ&hl=en&authuser=1
Social Links
Educational Qualification
PhD
Research Interests
Image Processing, Soft Computing, Machine Learning, Deep Learning, Active Learning.
PhD
Research Interests
Image Processing, Soft Computing, Machine Learning, Deep Learning, Active Learning.
Journals/Conferences :
Gayatri Pattnaik, Vimal K. Shrivastava, K. Parvathi, " Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning", Intelligent Decision Technologies, vol. 15, no. 3, pp. 433-442, 2021. 10.3233/IDT-200192.Link: https://content.iospress.com/articles/intelligent-decision-technologies/idt200192
BeraS., Shrivastava V. K., "Effect of Pooling Strategy on Convolutional Neural Network for Classification of Hyperspectral Remote Sensing Images", IET Image Processing, 2019. (Accepted)
Bera S., Shrivastava V. K., "Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification". International Journal of Remote Sensing, 41(7), 2664-2683, 2019.
Pradhan M. K., MinzS., Shrivastava V. K., "Fast Active Learning for Hyperspectral Image Classification using Extreme Learning Machine", IET Image Processing, 13(4), 549-555, 2019.
Pradhan M. K., Minz S.,Shrivastava V. K., "A Kernel-Based Extreme Learning Machine Framework for Classification of Hyperspectral Images using Active Learning", Journal of the Indian Society of Remote Sensing, 1-13, 2019.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2017). A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Computer methods and programs in biomedicine, 150, 9-22.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). A novel approach to multiclass psoriasis disease risk stratification: Machine learning paradigm. Biomedical Signal Processing and Control, 28, 27-40.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). Reliability analysis of psoriasis decision support system in principal component analysis framework. Data & Knowledge Engineering, 106, 1-17.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Computer methods and programs in biomedicine, 126, 98-109.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm. Computers in biology and medicine, 65, 54-68.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. Expert Systems with Applications, 42(15-16), 6184-6195.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). First review on psoriasis severity risk stratification: An engineering perspective. Computers in biology and medicine, 63, 52-63.
Shrivastava, V. K., & Londhe, N. D. (2015). Measurement of Psoriasis Area and Severity Index Area Score of Indian Psoriasis Patients. Journal of Medical Imaging and Health Informatics, 5(4), 675-682.
Araki, T., Jain, P. K., Suri, H. S., Londhe, N. D., Ikeda, N., El-Baz, A., Shrivastava, V. K.,...& Laird, J. R. (2017). Stroke risk stratification and its validation using ultrasonic Echolucent Carotid Wall plaque morphology: a machine learning paradigm. Computers in biology and medicine, 80, 77-96.
Araki, T., Ikeda, N., Shukla, D., Londhe, N. D., Shrivastava, V. K., Banchhor, S. K., ... & Suri, J. S. (2016). A new method for IVUS-based coronary artery disease risk stratification: a link between coronary & carotid ultrasound plaque burdens. Computer methods and programs in biomedicine, 124, 161-179.
Araki, T., Ikeda, N., Shukla, D., Jain, P. K., Londhe, N. D., Shrivastava, V. K., ... & Laird, J. R. (2016). PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. Computer methods and programs in biomedicine, 128, 137-158.
Conferences:
Shrivastava, V. K., Pradhan M. K., Minz S.and ThakurM. P., "Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network", Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 631-635, 2019. https://doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019.
JadhavA. R., GhontaleA. G. and Shrivastava, V. K., "Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM", In Computational Intelligence: Theories, Applications and Future Directions, Singapore, vol. I, pp. 97-108, Springer, 2019.
Pradhan, M. K., Minz, S., &Shrivastava, V. K. (2018, March). Fisher discriminant ratio based multiview active learning for the classification of remote sensing images. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT) (pp. 1-6). IEEE.
Raj, G. N., Raju, U. N., Venkat, M. K., &Shrivastava, V. K. (2017, March). Comparative performance analysis of different classifiers on diagnosis of erythmato-squamous diseases. In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference on (pp. 1-6). IEEE.
Gayatri Pattnaik, Vimal K. Shrivastava, K. Parvathi, " Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning", Intelligent Decision Technologies, vol. 15, no. 3, pp. 433-442, 2021. 10.3233/IDT-200192.Link: https://content.iospress.com/articles/intelligent-decision-technologies/idt200192
BeraS., Shrivastava V. K., "Effect of Pooling Strategy on Convolutional Neural Network for Classification of Hyperspectral Remote Sensing Images", IET Image Processing, 2019. (Accepted)
Bera S., Shrivastava V. K., "Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification". International Journal of Remote Sensing, 41(7), 2664-2683, 2019.
Pradhan M. K., MinzS., Shrivastava V. K., "Fast Active Learning for Hyperspectral Image Classification using Extreme Learning Machine", IET Image Processing, 13(4), 549-555, 2019.
Pradhan M. K., Minz S.,Shrivastava V. K., "A Kernel-Based Extreme Learning Machine Framework for Classification of Hyperspectral Images using Active Learning", Journal of the Indian Society of Remote Sensing, 1-13, 2019.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2017). A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Computer methods and programs in biomedicine, 150, 9-22.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). A novel approach to multiclass psoriasis disease risk stratification: Machine learning paradigm. Biomedical Signal Processing and Control, 28, 27-40.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). Reliability analysis of psoriasis decision support system in principal component analysis framework. Data & Knowledge Engineering, 106, 1-17.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016). Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: a first comparative study of its kind. Computer methods and programs in biomedicine, 126, 98-109.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm. Computers in biology and medicine, 65, 54-68.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. Expert Systems with Applications, 42(15-16), 6184-6195.
Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2015). First review on psoriasis severity risk stratification: An engineering perspective. Computers in biology and medicine, 63, 52-63.
Shrivastava, V. K., & Londhe, N. D. (2015). Measurement of Psoriasis Area and Severity Index Area Score of Indian Psoriasis Patients. Journal of Medical Imaging and Health Informatics, 5(4), 675-682.
Araki, T., Jain, P. K., Suri, H. S., Londhe, N. D., Ikeda, N., El-Baz, A., Shrivastava, V. K.,...& Laird, J. R. (2017). Stroke risk stratification and its validation using ultrasonic Echolucent Carotid Wall plaque morphology: a machine learning paradigm. Computers in biology and medicine, 80, 77-96.
Araki, T., Ikeda, N., Shukla, D., Londhe, N. D., Shrivastava, V. K., Banchhor, S. K., ... & Suri, J. S. (2016). A new method for IVUS-based coronary artery disease risk stratification: a link between coronary & carotid ultrasound plaque burdens. Computer methods and programs in biomedicine, 124, 161-179.
Araki, T., Ikeda, N., Shukla, D., Jain, P. K., Londhe, N. D., Shrivastava, V. K., ... & Laird, J. R. (2016). PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. Computer methods and programs in biomedicine, 128, 137-158.
Conferences:
Shrivastava, V. K., Pradhan M. K., Minz S.and ThakurM. P., "Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network", Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 631-635, 2019. https://doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019.
JadhavA. R., GhontaleA. G. and Shrivastava, V. K., "Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM", In Computational Intelligence: Theories, Applications and Future Directions, Singapore, vol. I, pp. 97-108, Springer, 2019.
Pradhan, M. K., Minz, S., &Shrivastava, V. K. (2018, March). Fisher discriminant ratio based multiview active learning for the classification of remote sensing images. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT) (pp. 1-6). IEEE.
Raj, G. N., Raju, U. N., Venkat, M. K., &Shrivastava, V. K. (2017, March). Comparative performance analysis of different classifiers on diagnosis of erythmato-squamous diseases. In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference on (pp. 1-6). IEEE.