Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2019/01/01
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Indian UGC (journal)
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Citations9
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Princy Matlani
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Manish Shrivastava
Cite
Matlani, Princy, and Manish Shrivastava. “Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection”. Computer Modeling in Engineering &Amp; Sciences, vol. 119, no. 3, 2019, pp. 427-58, https://doi.org/10.32604/cmes.2019.04985.
Matlani, P., & Shrivastava, M. (2019). Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection. Computer Modeling in Engineering &Amp; Sciences, 119(3), 427-458. https://doi.org/10.32604/cmes.2019.04985
Matlani, Princy, and Manish Shrivastava. “Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection”. Computer Modeling in Engineering &Amp; Sciences 119, no. 3 (2019): 427-58. https://doi.org/10.32604/cmes.2019.04985.
Matlani P, Shrivastava M. Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection. Computer Modeling in Engineering & Sciences. 2019;119(3):427-58.
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Application of space invariant artificial neural networks for network image interaction design | Journal of Intelligent & Fuzzy Systems |
| 2024 | |
A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device | Journal of X-Ray Science and Technology |
| 2023 | |
| ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images | Computer Modeling in Engineering & Sciences | 4 | 2022 | |
| ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition | IEEE Access |
| 1 | 2021 |
A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model | Scientific Programming |
| 1 | 2021 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics 4
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Recognition of Voltage Sag Sources Based on Phase Space Reconstruction and Improved VGG Transfer Learning and was published in 2019. The most recent citation comes from a 2024 study titled Application of space invariant artificial neural networks for network image interaction design. This article reached its peak citation in 2021, with 2 citations. It has been cited in 8 different journals, 25% of which are open access. Among related journals, the IEEE Access cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.