Artificial Intelligent Drone-Based Encrypted Machine Learning of Image Extraction Using Pretrained Convolutional Neural Network (CNN)

Shibli, Murad; Marques, Pascual; Spiridon, Elena

doi:10.1145/3293663.3297155

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…To extract the pattern information of pixels that are laid out in the document, we have used convolutional neural network (CNN) because it has shown a high performance in understanding images [14,15]. The used CNN model in this paper has four hidden layers with different number of nodes for each corresponding layer as shown in Table 3 which showed superior results compared to other models.…”

Section: Convolutional Neural Network (Cnn)-based Measurement Methodsmentioning

confidence: 99%

Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document

Shin¹,

Cho²,

Kang³

et al. 2020

Adv. sci. technol. eng. syst. j.

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As many digital documents are required in various environments, paper documents are digitized by scanner, fax, digital camera and specific software. In the case of a scanned document, we need to check whether the document is right sided or upside down because the orientation of the scanned document is determined by the orientation in which the paper document is placed. It is time-consuming for a person to check all the documents whether they are upside down. We propose an algorithm that can automatically determine upside down documents. The proposed artificial neural network-based method shows a high accuracy and efficiency in time for general documents. In addition, OCR-based method and CNN-based method were used to compare with the performance of the proposed method.

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Section: Convolutional Neural Network (Cnn)-based Measurement Methodsmentioning

confidence: 99%

Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document

Shin¹,

Cho²,

Kang³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…This technique can be recognized by a token where the UAV establishes relationships with the BSH. Shibli et al [12] introduced an AI drone-based encrypted ML of image classifier with a pertained CNN and image encrypt-decrypt using XOR-Secret-Key block cipher cryptology and singular value decomposition (SVD). Firstly, a pre-trained convolution neural network (CNN) is widely employed for extracting and classifying image features exploiting ML training tool features.…”

Section: Related Workmentioning

confidence: 99%

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

Abedi,

Ghanimi,

Algarni

et al. 2023

CSSE

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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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“…Nogay et al researched the detection of epileptic seizures using a deep learning CNN (pretrained) advocating for transfer learning [ 20 ]. Kumari et al examined the application of a pretrained CNN in forensics for offline signature detection [ 21 ], while Shibli et al investigated the implementation of pretrained CNN for artificial intelligent drone-based encrypted machine learning of image extraction [ 22 ]. In 2021, Rajadurai et al examined the detection of cracks in concrete surfaces through deep learning vision using AlexNet CNN [ 23 ], and Sharma et al evaluated the identification of vehicles using region-based CNN with an intelligent transportation focus [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Vasan

Sridharan

Sreelatha

et al. 2023

Sensors

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Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

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Artificial Intelligent Drone-Based Encrypted Machine Learning of Image Extraction Using Pretrained Convolutional Neural Network (CNN)

Cited by 7 publications

References 17 publications

Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document

Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document

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

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

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