Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises and less inconvenient for users. The number of authentication factors varies based on the security framework’s architecture and the required security level. Therefore, implementing a secured multi-factor authentication framework in a cloud platform is a challenging process. In this paper, we developed an adaptive multi-factor multi-layer authentication framework that embeds an access control and intrusion detection mechanisms with an automated selection of authentication methods. The core objective is to enhance a secured cloud platform with low false positive alarms that makes it more difficult for intruders to access the cloud system. To enhance the authentication mechanism and reduce false alarms, multiple authentication factors that include the length, validity, and value of the user factor is implemented with a user’s geolocation and user’s browser confirmation method that increase the identity verification of cloud users. An additional AES-based encryption component is applied to data, which are protected from being disclosed. The AES encryption mechanism is implemented to conceal the login information on the directory provider of the cloud. The proposed framework demonstrated excellent performance in identifying potentially malicious users and intruders, thereby effectively preventing any intentional attacks on the cloud services and data.

Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members’ to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members’ devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes’ assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students’ learning experiences.

Ensuring safe transportation requires a comprehensive understanding of driving behaviors and road safety to mitigate traffic crashes, reduce risks and enhance mobility. This study introduces an AI-driven machine learning (ML) framework for traffic crash severity prediction, utilizing a large-scale dataset of over 2.26 million records. By integrating human, crash-specific, and vehicle-related factors, the model improves predictive accuracy and reliability. The methodology incorporates feature engineering, clustering techniques such as K-Means and HDBSCAN, with oversampling methods such as RandomOverSampler, SMOTE, Borderline-SMOTE, and ADASYN to address class imbalance, along with Correlation-Based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for optimal feature selection. Among the evaluated classifiers, the Extra Trees (ET Classifier) ensemble model demonstrated superior performance, achieving 96.19% accuracy and an F1-score (macro) of 95.28%, ensuring a well-balanced prediction system. The proposed framework provides a scalable, AI-powered solution for traffic safety, offering actionable insights for intelligent transportation systems (ITS) and accident prevention strategies. By leveraging advanced ML and feature selection techniques, this approach enhances traffic risk assessment and enables data-driven decision-making.

A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.

Cardiovascular diseases are a global health challenge that necessitates improvements in diagnostic accuracy and efficiency. This study examines the potential of deep learning (DL) models for the classification of electrocardiogram (ECG) images to assist in the identification of various cardiac conditions. We initiated a two-tiered experimental framework to investigate the effectiveness of several neural network architectures in this medical application. In the first experiment, eight distinct neural network models were selected based on their top-5 accuracy on the ImageNet validation dataset and were fine-tuned using transfer learning techniques. These models were assessed using a cross-validation scheme, focusing on balanced accuracy, precision, recall, and the F1-score to evaluate their classification capabilities across four cardiac conditions: Myocardial Infarction (MI), abnormal heartbeat, historical MI, and normal ECG patterns. The second experiment extended our inquiry into the power of ensemble learning. By testing all possible combinations of the chosen models, we explored 120 ensemble configurations. The resulting analysis identified the best-performing ensemble set, which did not include the least effective model based on F1 score rankings. The most effective ensemble, composed of Inception, MobileNet, and NASNetLarge, achieved an F1 score of 0.9651 and a balanced accuracy of 0.9640, indicating a superior predictive performance. The ROC curve analysis yielded near-perfect Area Under the Curve (AUC) values for all classes, underscoring the ensemble’s proficiency in distinguishing between the specified cardiac conditions. The outcomes of this research highlight the synergistic benefit of ensembles in DL applications for medical imaging and suggest a promising approach for the early detection and diagnosis of cardiac diseases, potentially improving clinical outcomes and patient care.

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.