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 5 publications

(4 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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“…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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“…In determining the decision-making model based on the gray theory system of coal bursts whether they are explosions or not according to the visibility between the critical value of the index from the center of the explosion target or coal burst (Perez, 2003). Backpropagation (BP) is used as a model to predict the risk of coal bursts in work surface areas and mine areas that can pose occupational safety risks to employees (Al Shibli et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Coal Spray Rate Prediction Based on Factor Analysis and Neural Network (NN) Algorithm

Kasim

Yusuf

Haslinda

et al. 2023

JOSR

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Given the many factors that affect coal bursts as well as complex nonlinear relationships, this study analyzes the main factors that affect coal bursts in coal seams. Central Sulawesi Province. Using NN algorithms to predict coal bursts. This algorithm uses factor analysis to subtract the attributes of the original high-dimensional sample and obtains three common factors to maintain the correlation characteristics of 82.227% of the original data that had 10 sets. factors affecting coal burst rates can be used as training datasets as well as the last five datasets used as testing data and inputted into the rapid miner application to support NN algorithms to predict results. By comparing the prediction results where the NN algorithm has a non-explosion threshold value of 3.194% and an explosion threshold value of -3.230% and is more suitable for predicting coal burst explosions.

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

Cited by 5 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

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

Coal Spray Rate Prediction Based on Factor Analysis and Neural Network (NN) Algorithm

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