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8 results on '"Verma, Dinesh"'

2. Uncovering AI Potential Techniques for Infectious Disease: A Comprehensive Exploration of Surveying, Classifying, and Predicting Models

Author

Dubey, Shivendra, Verma, Dinesh Kumar, Kumar, Mahesh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Botto-Tobar, Miguel, editor, Zambrano Vizuete, Marcelo, editor, Montes León, Sergio, editor, Torres-Carrión, Pablo, editor, and Durakovic, Benjamin, editor

Published
2024
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6. A Hybrid Machine Learning Framework for Prediction of Software Effort at the Initial Phase of Software Development

Author

Rai, Prerana, Kumar, Shishir, Verma, Dinesh Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Gupta, P. K., editor, Tyagi, Vipin, editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Valentino, Gianluca, editor

Published
2020
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7. Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures.

Author

Wu, Rih‐Teng, Singla, Ankush, Jahanshahi, Mohammad R., Bertino, Elisa, Ko, Bong Jun, and Verma, Dinesh

Subjects
AUTONOMOUS robots, STRESS corrosion cracking, TAYLOR'S series, SURFACE defects, DEEP learning, EDGE computing
Abstract

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance. [ABSTRACT FROM AUTHOR]

Published
2019
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