Phishing is a type of social web-engineering attack in cyberspace where criminals steal valuable data or information from insensitive or uninformed users of the internet. Existing countermeasures in the form of anti-phishing software and computational methods for detecting phishing activities have proven to be effective. However, new methods are deployed by hackers to thwart these countermeasures. Due to the evolving nature of phishing attacks, the need for novel and efficient countermeasures becomes crucial as the effect of phishing attacks are often fatal and disastrous. Artificial Intelligence (AI) schemes have been the cornerstone of modern countermeasures used for mitigating phishing attacks. AI-based phishing countermeasures or methods possess their shortcomings particularly the high false alarm rate and the inability to interpret how most phishing methods perform their function. This study proposed four (4) meta-learner models (AdaBoost-Extra Tree (ABET), Bagging –Extra tree (BET), Rotation Forest – Extra Tree (RoFBET) and LogitBoost-Extra Tree (LBET)) developed using the extra-tree base classifier. The proposed AI-based meta-learners were fitted on phishing website datasets (currently with the newest features) and their performances were evaluated. The models achieved a detection accuracy not lower than 97% with a drastically low false-positive rate of not more 0.028. In addition, the proposed models outperform existing ML-based models in phishing attack detection. Hence, we recommend the adoption of meta-learners when building phishing attack detection models.

Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas.

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Visually impaired people seek social integration, yet their mobility is restricted. They need a personal navigation system that can provide privacy and increase their confidence for better life quality. In this paper, based on deep learning and neural architecture search (NAS), we propose an intelligent navigation assistance system for visually impaired people. The deep learning model has achieved significant success through well-designed architecture. Subsequently, NAS has proved to be a promising technique for automatically searching for the optimal architecture and reducing human efforts for architecture design. However, this new technique requires extensive computation, limiting its wide use. Due to its high computation requirement, NAS has been less investigated for computer vision tasks, especially object detection. Therefore, we propose a fast NAS to search for an object detection framework by considering efficiency. The NAS will be used to explore the feature pyramid network and the prediction stage for an anchor-free object detection model. The proposed NAS is based on a tailored reinforcement learning technique. The searched model was evaluated on a combination of the Coco dataset and the Indoor Object Detection and Recognition (IODR) dataset. The resulting model outperformed the original model by 2.6% in average precision (AP) with acceptable computation complexity. The achieved results proved the efficiency of the proposed NAS for custom object detection.

The Whale Optimization Algorithm (WOA) is a newly proposed metaheuristic optimization algorithm, which simulate humpback whales hunting behavior. Like other population-based algorithms, WOA generate its population randomly during the exploration and exploitation phases, which could generate values far from the optimum solution or stuck the exploration around local optima. In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). The OWOA use the Opposition-based method to enhance Whale Optimization Algorithm (WOA) performance. The OWOA looks for the solution in the opposite direction of suggested values to test if the opposite select has better solution. The OWOA is tested and compared with the original algorithm WOA and other metaheuristic methods. The benchmark results prove the efficiency of the OWOA being more efficient than WOA