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This paper aims to address the detection of COVID-19 by developing an accurate and efficient diagnostic system using chest X-ray images. The research utilizes open-source Kaggle data comprising four categories: COVID-19, Lung-Opacity, Normal, and Viral Pneumonia. The proposed system employs convolutional neural networks (CNNs), including VGG19, RNN-LSTM, and inceptionv3. Results vary among the methodologies, with VGG19 achieving 26% accuracy, RNN-LSTM attaining 25% accuracy (28% with preprocessing), and inceptionv3 with histogram equalization achieving 83% accuracy. A CNN designed from scratch demonstrates the highest performance, with an accuracy of 93% (96% with histogram equalization). The findings emphasize the potential of AI techniques in enhancing disease diagnosis, particularly in distinguishing COVID-19 from other conditions, thereby facilitating timely and effective interventions. [ABSTRACT FROM AUTHOR]

Examining facial expressions is a crucial aspect that garners attention due to its significance in divulging emotional states. This study delves into employing a robust deep learning method for automatically analyzing and identifying facial emotions in images. The chosen technique revolves around the convolutional neural network (CNN) algorithm. A dataset containing images of individuals, each exhibiting distinct facial expressions, was curated. The emotions in these images were categorized into seven groups (angry, disgust, fear, happy, neutral, sad, surprise) based on the depicted emotional states. The approach comprises four primary steps: preprocessing the input facial images, utilizing image adjustments and data augmentation, employing the Viola and Jones technique for face detection and landmark localization, creating a numerical feature vector from the registered image for feature extraction, and inputting the extracted features into the CNN for classification. The proposed CNN model underwent application to classify facial emotions within the image dataset. Additionally, a pretrained VGG-16 model was incorporated into the classification process for facial images. A comparative analysis between the proposed CNN approach and the pretrained VGG-16 model revealed that the latter outperformed the former in terms of accuracy rate and loss function values when determining individuals’ facial expressions. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSN) such as Facebook, Instagram, Twitter, and others have seen rapid growth in recent years. Such applications provide attractive online social networks and communications with the opportunity to connect with relatives and acquaintances, meet new people, enter communities, talk, exchange photos, organize events, and network with others who are close to real-life; unfortunately, on the other hand, they also raise privacy and security issues. We identified OSN threats in this paper and recommended a digital face-processing authentication method as a double-factor authentication after entering the password using Matlab. After applying deep learning classification by attending to a real dataset from the live webcam to train the model, we achieved the best accuracy rate of 95%. However, such methods have yet to be deployed to all social networks, so we also mentioned the problem of fake accounts, which is one of the most significant problems in OSN. These are effective tools for executing spam campaigns and spreading malware and phishing attacks. Fake accounts could lead to the loss of money for businesses, loss of reputation, stealing information for malicious purposes, and much more. This study is related to detecting fake and legitimate profiles on OSN. For this purpose, we chose two datasets that contain fake and legitimate accounts on Facebook and Instagram. Each contains different features after applying machine learning using Naive Bayes, Logistic Regression, Support Vector Machines, K-Nearest Neighbour, Boosted Tree, Neural Networks, SVM Kernal, and Logistec Regression Kernal. SVM achieved the highest classification accuracy for the Fake Profiles detection datasets with 97.1%. [ABSTRACT FROM AUTHOR]

Non Orthogonal Multiple Access (NOMA) successfully drew attention to the deployment of 5th Generation (5G) wireless communication systems, and it is now considered a significant technology in 5G communications. The primary enhancement in 5G is the speed, which may be 100 times faster than 4G. Due to the rising number of internal or external attacks on the Network, wireless intrusion detection systems are a vital aspect of any system connected to the Internet, and 5G will demand considerable improvements in data rate and security. In this paper, we have built a simulator for NOMA and applied a dropping attack to extract a dataset from the simulation model. The accuracy for detecting dropping attacks using the extracted data after applying ML algorithms was 95.7% for LR. Furthermore, this work suggests a methodology for wireless cyberattack detection in 5G networks based on applying several ML and DL techniques such as Decision Trees, KNN, Multi-class Decision Jungle, Multi-class Decision Forest, and Multi-class Neural Network. The proposed work is implemented and tested using a comprehensive Wi-Fi network benchmark dataset. The conducted experiments resulted in an outstanding performance with an accuracy of 99% for the KNN algorithm and 93% for DF and Neural Network. [ABSTRACT FROM AUTHOR]

Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin's effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%. [ABSTRACT FROM AUTHOR]

Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets. [ABSTRACT FROM AUTHOR]

Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases' spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11042-022-13914-9 B Correction to: Multimedia Tools and Applications b https://doi.org/10.1007/s11042-022-13914-9 The original publication of this article contains incorrect affiliations of the author "Laith Abualigah". Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]