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36 results on '"Panda, Nibedan"'

10. Data Classification by Ensemble Methods in Machine Learning

Author

Jagadeeswara Rao, G., Siva Prasad, A., Sai Srinivas, S., Sivaparvathi, K., Panda, Nibedan, 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, Mohanty, Mihir Narayan, editor, and Das, Swagatam, editor

Published
2022
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13. Effectiveness of Backpropagation Algorithm in Healthcare Data Classification

Author

Chandra Sekhar, Ch, Panda, Nibedan, Ramana, B. V., Maneesha, B., Vandana, S., 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, Sharma, Renu, editor, Mishra, Manohar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, and Pelusi, Danilo, editor

Published
2021
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14. Effectiveness of Swarm-Based Metaheuristic Algorithm in Data Classification Using Pi-Sigma Higher Order Neural Network

Author

Panda, Nibedan, Majhi, Santosh Kumar, 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, Panigrahi, Chhabi Rani, editor, Pati, Bibudhendu, editor, Mohapatra, Prasant, editor, Buyya, Rajkumar, editor, and Li, Kuan-Ching, editor

Published
2021
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19. How Effective is the Salp Swarm Algorithm in Data Classification

Author

Panda, Nibedan, Majhi, Santosh Kumar, 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, Das, Asit Kumar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, Pati, Soumen Kumar, editor, and Pelusi, Danilo, editor

Published
2020
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27. Application of deep convolutional neural network for facial appearance recognition.

Author

Panda, Nibedan, Mohanty, Prithviraj, Naidu, B. Ramesh, and Prasad, G. Kalivar

Subjects
*CONVOLUTIONAL neural networks, *DEEP learning, *NOSE, *LEARNING, *FACIAL expression
Abstract

Identification of facial appearance is nothing but passing of non-verbal messages between two or more persons. Facial appearance acknowledgement is an actively emerging field of research where the machine is used to recognize the human expressions. Feelings emitted by expression which others can make judge their state by the expression maintained by humans. To detect the expression of a human, first it is required to sense the facial features such as eye, lips, nose, mouth etc. and then classify them using suitable classifier. Convolutional Neural Network is a technique through which a machine can learn the expressions by extracting the facial features. By using CNN, we can train the data for the machine that can learn the expression of humans. For this research work, three models (MLP, simple CNN and deep CNN) are considered and FER-2013 dataset is used to train and test those models. Various experiments are conducted and the results attained endorses that the deep CNN model affords a better testing accuracy of 68.48% as compared to other two models. [ABSTRACT FROM AUTHOR]

Published
2023
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