The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities. Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily. A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate. This research provides a prediction method for the early identification of COVID-19 patients outcome based on patients characteristics monitored at home, while in quarantine. The study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia. The data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). Initially, the data were preprocessed using several preprocessing techniques. Furthermore, 10-k cross-validation was applied for data partitioning and SMOTE for alleviating the data imbalance. Experiments were performed using twenty clinical features, identified as significant for predicting the survival versus the deceased COVID-19 patients. The results showed that RF outperformed the other classifiers with an accuracy of 0.95 and area under curve (AUC) of 0.99. The proposed model can assist the decision-making and health care professional by early identification of at-risk COVID-19 patients effectively.

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

Detection of minor leaks in oil or gas pipelines is a critical and persistent problem in the oil and gas industry. Many organisations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialisation and technological advancements, innovative engineering technologies that are cost-effective, faster, and easier to implement are essential. Herein, machine learning-based anomaly detection models are proposed to solve the problem of oil and gas pipeline leakage. Five machine learning algorithms, namely, random forest, support vector machine, k-nearest neighbour, gradient boosting, and decision tree, were used to develop detection models for pipeline leaks. The support vector machine algorithm, with an accuracy of 97.4%, overperformed the other algorithms in detecting pipeline leakage and thus proved its efficiency as an accurate model for detecting leakage in oil and gas pipelines.

Current technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that is often utilized to launch cyber-attacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification and detection more challenging. New techniques that are different from conventional systems should be utilized for effectively combating new malware variants. Machine learning (ML) methods are ineffective in identifying all complex and new malware variants. The deep learning (DL) method can be a promising solution to detect all malware variants. This paper presents an Automated Android Malware Detection using Optimal Ensemble Learning Approach for Cybersecurity (AAMD-OELAC) technique. The major aim of the AAMD-OELAC technique lies in the automated classification and identification of Android malware. To achieve this, the AAMD-OELAC technique performs data preprocessing at the preliminary stage. For the Android malware detection process, the AAMD-OELAC technique follows an ensemble learning process using three ML models, namely Least Square Support Vector Machine (LS-SVM), kernel extreme learning machine (KELM), and Regularized random vector functional link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is exploited for the optimal parameter tuning of the three DL models, and it helps accomplish improved malware detection results. To denote the supremacy of the AAMD-OELAC method, a comprehensive experimental analysis is conducted. The simulation results portrayed the supremacy of the AAMD-OELAC technique over other existing approaches.

An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models.