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A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)

Cevat Rahebi

Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effectively identifying fake pages. To address this challenge, we propose an image-based coding approach for detecting phishing attacks using a CNN-LSTM hybrid model. This approach combines SMOTE, an enhanced GAN based on the Autoencoder network, and swar ...Daha fazlası

Erişime Açık

Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

Cevat Rahebi

This paper discusses the momentum gradient dependent on the convolutional neural organization’s strong point. It is a new methodology introduced to detect evenness in the data set of faces. The proposed face recognition framework was created for various purposes. Through Gabor wavelet change, facial evenness was extracted from the face-preparing information. After that, we applied a profound learning process to carry out verification. After applying the proposed method to YALE and ORL data sets, we simulated them using MATLAB 2021a. Before this, similar trials were directly applied through Har ...Daha fazlası

Süresiz Ambargo

Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse

Metin Zontul

In this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and conve ...Daha fazlası

Erişime Açık

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Cevat Rahebi

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the ext ...Daha fazlası

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