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25 results on '"Morwal, Sudha"'

1. A Formal Study of Shot Boundary Detection Approaches—Comparative Analysis

Author

Nankani, Hanisha, Mahrishi, Mehul, Morwal, Sudha, Hiran, Kamal Kant, 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, Sharma, Tarun K., editor, Ahn, Chang Wook, editor, Verma, Om Prakash, editor, and Panigrahi, Bijaya Ketan, editor

Published
2022
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3. Index Point Detection and Semantic Indexing of Videos—A Comparative Review

Author

Mahrishi, Mehul, Morwal, Sudha, 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, Pant, Millie, editor, Kumar Sharma, Tarun, editor, Arya, Rajeev, editor, Sahana, B.C., editor, and Zolfagharinia, Hossein, editor

Published
2020
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12. Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text

Author

Agarwal, Basant, Sharma, Richa, and Morwal, Sudha

Abstract

This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are promoted to a proposed model with the integration of CNN and conditional random field (CRF) layers. After a comparative analysis of results, it is verified that the performance of the proposed model (i.e., Bi-LSTM-CNN-CRF) is impressive. The proposed system achieves 61% precision, 56% recall, and 58% F-measure.

Published
2021
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