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Your search keyword '"Vishwakarma, Virendra P."' showing total 181 results
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181 results on '"Vishwakarma, Virendra P."'

4. COVID-19 Detection in Chest X-Ray Images Using Non-iterative Deterministic Learning Classifier

Author

Husain, Arshi, Vishwakarma, Virendra P., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Roy, Satyabrata, editor, Sinwar, Deepak, editor, Dey, Nilanjan, editor, Perumal, Thinagaran, editor, and Tavares, João Manuel R. S., editor

Published
2023
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6. Automatic adaptive weighted fusion of features-based approach for plant disease identification

Author

Kirti, Rajpal Navin, and Vishwakarma Virendra P.

Subjects
central difference convolution network, deep neural networks, feature fusion, leaf images, resnet, plant disease identification, Science, Electronic computers. Computer science, QA75.5-76.95
Abstract

With the rapid expansion in plant disease detection, there has been a progressive increase in the demand for more accurate systems. In this work, we propose a new method combining color information, edge information, and textural information to identify diseases in 14 different plants. A novel 3-branch architecture is proposed containing the color information branch, an edge information branch, and a textural information branch extracting the textural information with the help of the central difference convolution network (CDCN). ResNet-18 was chosen as the base architecture of the deep neural network (DNN). Unlike the traditional DNNs, the weights adjust automatically during the training phase and provide the best of all the ratios. The experiments were performed to determine individual and combinational features’ contribution to the classification process. Experimental results of the PlantVillage database with 38 classes show that the proposed method has higher accuracy, i.e., 99.23%, than the existing feature fusion methods for plant disease identification.

Published
2023
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12. Nested Sparse Classification Method for Hierarchical Information Extraction

Author

Mishra, Gargi, Vishwakarma, Virendra P., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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13. Local Binary Pattern Based ELM for Face Identification

Author

Ahuja, Bhawna, Vishwakarma, Virendra P., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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14. Blockchain and Industrial Internet of Things: Applications for Industry 4.0

Author

Swami, Mahesh, Verma, Divya, Vishwakarma, Virendra P., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Poonam, editor, Tushir, Meena, editor, Balas, Valentina Emilia, editor, and Srivastava, Rajeev, editor

Published
2021
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20. A Hybridization of Fuzzy Logic and Deterministic Learning Machine for Face Recognition

Author

Vishwakarma, Virendra P., Yadav, Sudesh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Dutta, Debashis, editor, Kar, Haranath, editor, Kumar, Chiranjeev, editor, and Bhadauria, Vijaya, editor

Published
2020
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21. A Novel Approach for Compensation of Light Variation Effects with KELM Classification for Efficient Face Recognition

Author

Vishwakarma, Virendra P., Dalal, Sahil, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Dutta, Debashis, editor, Kar, Haranath, editor, Kumar, Chiranjeev, editor, and Bhadauria, Vijaya, editor

Published
2020
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22. PHT and KELM Based Face Recognition

Author

Dalal, Sahil, Vishwakarma, Virendra P., Kacprzyk, Janusz, Series Editor, Gunjan, Vinit Kumar, editor, Zurada, Jacek M., editor, Raman, Balasubramanian, editor, and Gangadharan, G. R., editor

Published
2020
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25. Secured Blockchain and Fractional Discrete Cosine Transform-based Framework for Medical Images.

Author

Yadav, Abhay Kumar and Vishwakarma, Virendra P.

Subjects
BLOCKCHAINS, DISCRETE cosine transforms, CLOUD computing, DIGITAL images, FEATURE extraction
Abstract

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and retrieving medical images by extracting feature maps from medical images using fctDCT, followed by encoding and storing a feature map on a decentralized cloud and linking it on a blockchain. The integration has been implemented using α angles, which are stored on the blockchain and need to be identical at the storage and retrieval stage, as only the authentic user would have access to unique α angles and the number of coefficients that have been used in storing their medical images. The proposed novel approach offers numerous benefits, including improved data sharing and collaboration, enhanced security, compression, and efficient retrieval and processing of medical image data. The performance of the proposed framework was evaluated in terms of image quality metrics such as mean square error, peak signal-to-noise ratio, structural similarity index measure (SSIM), and multiSSIM by employing it with correct and incorrect α values. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Applications of deep learning in acute leukemia detection-a review.

Author

Chand, Sunita and Vishwakarma, Virendra P.

Subjects
*DEEP learning, *ACUTE leukemia, *MACHINE learning, *CELL imaging, *DIAGNOSIS, *NOSOLOGY
Abstract

Leukemia falls in the category of life-threatening disease. Moreover, it is difficult to detect it in its initial stage, so its prognosis and treatment becomes even more difficult. Machine Learning has found its application in almost all domains of our life. One of the most crucial domains is medical diagnosis. It was found from various studies that computational leukemia detection has been performed on various types of datasets like clinical data, genomic data, micro-array dataset, peripheral blood smear images, and flow-cytometry data. In the present study, we limit the domain of study to the detection of leukemia from blood cell images only. This paper presents a thorough review of machine learning and deep learning algorithms in the diagnosis of leukemia from microscopic blood cell images. The paper also discusses the publicly available datasets used by various researchers in this domain and various algorithms used for disease detection and classification. This paper concludes that deep learning gives the best result when it comes to diagnosis of disease from peripheral blood smear images. [ABSTRACT FROM AUTHOR]

Published
2023
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