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The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

This paper presents and compares three feature reduction techniques that were applied to Arabic text. The techniques include stemming, light stemming, and word clusters. The effects of the aforementioned techniques were studied and analyzed on the K-nearest-neighbor classifier. Stemming reduces words to their stems. Light stemming, by comparison, removes common affixes from words without reducing them to their stems. Word clusters group synonymous words into clusters and each cluster is represented by a single word. The purpose of employing the previous methods is to reduce the size of document vectors without affecting the accuracy of the classifiers. The comparison metric includes size of document vectors, classification time, and accuracy (in terms of precision and recall). Several experiments were carried out using four different representations of the same corpus: the first version uses stem-vectors, the second uses light stem-vectors, the third uses word clusters, and the fourth uses the original words (without any transformation) as representatives of documents. The corpus consists of 15,000 documents that fall into three categories: sports, economics, and politics. In terms of vector sizes and classification time, the stemmed vectors consumed the smallest size and the least time necessary to classify a testing dataset that consists of 6,000 documents. The light stemmed vectors superseded the other three representations in terms of classification accuracy.

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.

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Malicious JavaScript code has been actively and recently utilized as a vehicle for Web-based security attacks. By exploiting vulnerabilities such as cross-site scripting (XSS), attackers are able to spread worms, conduct Phishing attacks, and do Web page redirection to "typically" porn Web sites. These attacks can be preemptively prevented if the malicious code is detected before executing. Based on the fact that a malignant code will exhibit certain features, we propose a novel classification-based detection approach that will identify Web pages containing infected code. Using datasets of trusted and malicious Web sites, we analyze the behavior and properties of JavaScript code to point out its key features. These features form the basis of our identification system and are used to properly train the various classifiers on malicious and benign data. Performance evaluation results show that our approach achieves a 95% or higher detection accuracy, with very small (less than 3%) false positive and false negative ratios. Our solution surpasses the performance of the comparable literature.

Protecting agricultural crops is essential for preserving food sources. The health of the plants plays a major role in impacting the yield of the agricultural output and results in significant economic loss. This is especially important in small-scale and hobby farming products such as fruits. Grapes are an important and widely cultivated plant especially in the Mediterranean region, with over $189 billion global market value. They are consumed as fruits as well as in other manufactured forms (e.g., drinks and sweet food products). However, much like other plants, grapes are prone to a wide range of diseases that require the application of immediate remedies. Misidentifying these diseases can result in poor disease control and great losses (i.e., 5–80% crop loss). Existing computer-based solutions may suffer from low accuracy, may require high overhead, are poorly deployable, and are prone to changes in image quality. The work in this paper aimed at utilizing ubiquitous technology to help farmers combat plant diseases. Particularly, deep learning artificial intelligence image-based applications were used to classify three common grape diseases—black measles, black rot, and isariopsis leaf spot. In addition, a fourth healthy class was included. A dataset of 3639 grape leaf images (1383 black measles, 1180 black rot, 1076 isariopsis leaf spot, and 423 healthy) was used. These images were used to customize and retrain eleven convolutional network models to classify the four classes. Thorough performance evaluation revealed that it is possible to design pilot and commercial applications that have accuracies that satisfy field requirements. The models achieved consistently high performance values (>99.1%).