Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities

Al-Jabbar, Mohammed; Almansor, Ebtesam Hussain; Abdel‐Khalek, S.; Alkhalaf, Salem

doi:10.1016/j.aej.2023.05.049

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Cited by 5 publications

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DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

Nanjappan,

Pradeep,

Natesan

et al. 2024

Cluster Comput

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DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

Nanjappan,

Pradeep,

Natesan

et al. 2024

Cluster Comput

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Octonion-based transform moments for innovative stereo image classification with deep learning

Tahiri,

Boudaaoua,

Karmouni

et al. 2024

Complex Intell. Syst.

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Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.

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Landmark Scene Classification Using Vision Transformer

Sangeetha,

Sunil,

Shnain

et al. 2024

2024 International Conference on Data Science and Network Security (ICDSNS)

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Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities

Cited by 5 publications

References 20 publications

DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

DeepLG SecNet: utilizing deep LSTM and GRU with secure network for enhanced intrusion detection in IoT environments

Octonion-based transform moments for innovative stereo image classification with deep learning

Landmark Scene Classification Using Vision Transformer

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