Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area.

Article Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis” was published on January 1, 2024 in the journal Open Life Sciences (volume 19, issue 1).

Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data.

This study aimed to examine the relationship between electronic game addiction and sleep disorders among Jordanian adolescents. Sample of the study were 576 adolescents (325 males and 251 females) their age between 14 to 17 years. Lemmens., Valkenburg, and Peter scale for electronic games addiction and the Albana scale for adolescents' sleep disorder were used. The study considers the frequent links between severe internet addiction and the emergence of addictive-like traits in adolescents. The results suggested a higher level of electronic game addiction which was significantly associated with a higher level of sleep disorder. Results also showed that withdrawal, mood modification, and silence explained 69.30% of sleep disorders among Jordanian adolescents. Moreover, total electronic game addiction explained 40% of the variance in sleep disorder among Jordanian adolescents. Keywords: sleep disorder, electronic games, addiction, Jordanian adolescents. DOI: 10.7176/JEP/14-13-04 Publication date:May 31^st 2023