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Vimal K. Shrivastava
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2020 – today
- 2023
- [j20]Dawit Kiros Redie
, Abdulhakim Edao Sirko, Tensaie Melkamu Demissie, Semagn Sisay Teferi, Vimal K. Shrivastava
Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model. Evol. Intell. 16(3): 729-738 (2023) - [j19]Gayatri Pattnaik, Vimal K. Shrivastava, K. Parvathi:
Improved performance on tomato pest classification via transfer learning-based deep convolutional neural network with regularisation techniques. Int. J. Comput. Sci. Eng. 26(4): 397-405 (2023) - [j18]Ayan Mondal
A fine tuning approach using modified DenseNet model for skin cancer classification. Int. J. Medical Eng. Informatics 15(4): 323-335 (2023) - 2022
- [j17]Ayan Mondal
A novel Parametric Flatten-p Mish activation function based deep CNN model for brain tumor classification. Comput. Biol. Medicine 150: 106183 (2022) - [j16]Vimal K. Shrivastava, Monoj K. Pradhan:
Diagnosis of COVID-19 based on chest X-ray images using pre-trained deep convolutional neural networks. Intell. Decis. Technol. 16(1): 169-180 (2022) - 2021
- [j15]Gayatri Pattnaik
Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning. Intell. Decis. Technol. 15(3): 433-442 (2021) - 2020
- [j14]Gayatri Pattnaik
Transfer Learning-Based Framework for Classification of Pest in Tomato Plants. Appl. Artif. Intell. 34(13): 981-993 (2020) - [j13]Manoranjan Dash, Narendra D. Londhe
Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis. Comput. Biol. Chem. 86: 107247 (2020) - [j12]Somenath Bera
Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images. IET Image Process. 14(3): 480-486 (2020)
2010 – 2019
- 2019
- [j11]Monoj K. Pradhan, Sonajharia Minz, Vimal K. Shrivastava
Fast active learning for hyperspectral image classification using extreme learning machine. IET Image Process. 13(4): 549-555 (2019) - 2018
- [c1]Monoj K. Pradhan, Sonajharia Minz, Vimal K. Shrivastava
Fisher discriminant ratio based multiview active learning for the classification of remote sensing images. RAIT 2018: 1-6 - 2017
- [j10]Tadashi Araki, Pankaj Kumar Jain
Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm. Comput. Biol. Medicine 80: 77-96 (2017) - [j9]Vimal K. Shrivastava
A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput. Methods Programs Biomed. 150: 9-22 (2017) - 2016
- [j8]Vimal K. Shrivastava
A novel approach to multiclass psoriasis disease risk stratification: Machine learning paradigm. Biomed. Signal Process. Control. 28: 27-40 (2016) - [j7]Tadashi Araki, Nobutaka Ikeda, Devarshi Shukla
A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. Comput. Methods Programs Biomed. 124: 161-179 (2016) - [j6]Vimal K. Shrivastava
Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind. Comput. Methods Programs Biomed. 126: 98-109 (2016) - [j5]Tadashi Araki, Nobutaka Ikeda, Devarshi Shukla
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. Comput. Methods Programs Biomed. 128: 137-158 (2016) - [j4]Vimal K. Shrivastava
Reliability analysis of psoriasis decision support system in principal component analysis framework. Data Knowl. Eng. 106: 1-17 (2016) - 2015
- [j3]Vimal K. Shrivastava
First review on psoriasis severity risk stratification: An engineering perspective. Comput. Biol. Medicine 63: 52-63 (2015) - [j2]Vimal K. Shrivastava
Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm. Comput. Biol. Medicine 65: 54-68 (2015) - [j1]Vimal K. Shrivastava
Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. Expert Syst. Appl. 42(15-16): 6184-6195 (2015)
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