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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Firas Abedi1, Hayder M. A. Ghanimi2, Abeer D. Algarni3, Naglaa F. Soliman3,*, Walid El-Shafai4,5, Ali Hashim Abbas6, Zahraa H. Kareem7, Hussein Muhi Hariz8, Ahmed Alkhayyat9

1 Department of Mathematics, College of Education, Al-Zahraa University for Women, Karbala, Iraq
2 Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia
5 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
6 College of Information Technology, Imam Jaafar Al-Sadiq University, Al-Muthanna, 66002, Iraq
7 Department of Medical Instrumentation Techniques Engineering, Al-Mustaqbal University College, Hillah, 51001, Iraq
8 Computer Engineering Department, Mazaya University College, Dhi Qar, Iraq
9 College of Technical Engineering, The Islamic University, Najaf, Iraq

* Corresponding Author: Naglaa F. Soliman. Email: email

Computer Systems Science and Engineering 2023, 47(3), 3127-3144. https://doi.org/10.32604/csse.2023.038959

Abstract

Computational intelligence (CI) is a group of nature-simulated computational models and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UAV) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment. The major purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels. The proposed MDLS-UAVIC model follows a two-stage process: encryption and image classification. The encryption technique for image encryption effectively encrypts the UAV images. Next, the image classification process involves an Xception-based deep convolutional neural network for the feature extraction process. Finally, shuffled shepherd optimization (SSO) with a recurrent neural network (RNN) model is applied for UAV image classification, showing the novelty of the work. The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset, and the outcomes are examined in various measures. It achieved a high accuracy of 98%.

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APA Style
Abedi, F., Ghanimi, H.M.A., Algarni, A.D., Soliman, N.F., El-Shafai, W. et al. (2023). Computational intelligence driven secure unmanned aerial vehicle image classification in smart city environment. Computer Systems Science and Engineering, 47(3), 3127-3144. https://doi.org/10.32604/csse.2023.038959
Vancouver Style
Abedi F, Ghanimi HMA, Algarni AD, Soliman NF, El-Shafai W, Abbas AH, et al. Computational intelligence driven secure unmanned aerial vehicle image classification in smart city environment. Comput Syst Sci Eng. 2023;47(3):3127-3144 https://doi.org/10.32604/csse.2023.038959
IEEE Style
F. Abedi et al., "Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 3127-3144. 2023. https://doi.org/10.32604/csse.2023.038959



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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