An Improved Convolutional Neural Network Model for DNA Classification

Soliman, Naglaa F.; Abd-Alhalem, Samia M.; El‐Shafai, Walid; Abdulrahman, Salah Eldin S. E.; Ismaiel, N.; El‐Rabaie, El‐Sayed M.; Algarni, Abeer D.; El‐Samie, Fathi E. Abd

doi:10.32604/cmc.2022.018860

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Recently, the security of the IoT has become perilous; therefore, different studies are related to this area [7][8][9][10][11][12][13][14][15][16][17][18][19]. Several works have been proposed for detecting intrusion using intelligent approaches like classification and detection [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…In [17], they introduced an optimal feature selection algorithm to help in detecting network intrusion. In [18], they provided an online attack detection model to collect evidence related to the attack that can be used in computer forensics. In [19], they presented an intrusion detection approach using 10% of the knowledge discovery and data mining (KDD) cup'99 dataset to compare the attack types and the protocol used by the attackers.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

Alshathri¹,

El‐Sayed²,

El‐Shafai³

et al. 2023

Computer Systems Science and Engineering

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Recently, the Internet of Things (IoT) has been used in various applications such as manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and productivity via an intelligent management console remotely. With the increased use of Industrial IoT (IIoT) applications, the risk of brutal cyber-attacks also increased. This leads researchers worldwide to work on developing effective Intrusion Detection Systems (IDS) for IoT infrastructure against any malicious activities. Therefore, this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure. A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is introduced. The data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT applications. Correspondingly, this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT network. This is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient fashion. Therefore, several machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (NB), Classification and Regression Tree (CART), Random Forest (RF), and AdaBoost (AB) are used for the detection intent on thirteen different intrusion datasets. Several performance metrics like accuracy, precision, recall, and F1-score are used to estimate the proposed framework. The experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00 for effectively detecting the intrusion activities on the IoT/ IIoT infrastructure on most of the employed datasets. In addition, the proposed work accomplishes high performance compared to other recent related works in terms of different security and detection evaluation parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

Alshathri¹,

El‐Sayed²,

El‐Shafai³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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“…In the first method, pixel value estimation is based on autoregression, such as PixelCNN [13] utilizing traceable density or the autoencoder method [14] applying approximate density. Likewise, GAN is based on implicit density and is considered a new way to generate various data from images, audio, and video [15]. The authors of [12] submitted the GAN model, which consists of two deep networks.…”

Section: Super-resolution (Sr) Gan Phasementioning

confidence: 99%

Hybrid Single Image Super-Resolution Algorithm for Medical Images

El‐Shafai¹,

Mohamed²,

Zeghid³

et al. 2022

Computers, Materials &Amp; Continua

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High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to generate high-resolution images with higher quality and finer details to reduce blurring. The proposed framework comprises five GAN models: Enhanced SR Generative Adversarial Networks (ESRGAN), Enhanced deep SR GAN (EDSRGAN), Sub-Pixel-GAN, SRGAN, and Efficient Wider Activation-B GAN (WDSR-b-GAN). To train the proposed models, we have employed images from the famous BreakHis dataset and enlarged them by 4× and 16× upscale factors with the ground truth of the size of 256 × 256 × 3. Moreover, several evaluation metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale Structural Similarity Index (MS-SSIM), and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency, training time, and storage space. The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

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“…It is decomposed of an input layer, convolutional layer, pooling layer, fully connected layer, and a classification layer. It comprises a feature extraction and classification network [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

El‐Shafai¹,

El-Hag²,

Sedik³

et al. 2023

Computers, Materials &Amp; Continua

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Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type , and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.

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An Improved Convolutional Neural Network Model for DNA Classification

Cited by 8 publications

References 22 publications

An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

Hybrid Single Image Super-Resolution Algorithm for Medical Images

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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