Virtual learning environment is becoming an increasingly popular studyoption for students from diverse cultural and socioeconomic backgroundsaround the world. Although this learning environment is quite adaptable,improving student performance is difficult due to the online-only learningmethod. Therefore, it is essential to investigate students' participation andperformance in virtual learning in order to improve their performance. Usinga publicly available Open University learning analytics dataset, this studyexamines a variety of machine learning-based prediction algorithms todetermine the best method for predicting students' academic success, henceproviding additional alternatives for enhancing their academic achievement.Support vector machine, random forest, Nave Bayes, logical regression, anddecision trees are employed for the purpose of prediction using machinelearning methods. It is noticed that the random forest and logistic regressionapproach predict student performance with the highest average accuracyvalues compared to the alternatives. In a number of instances, the supportvector machine has been seen to outperform the other methods.

Intrusion detection system for imbalance ratio class using weighted XGBoost classifier

Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.

Several concerns concerning the acquisition of the Arabic language have been the subject of substantial discussion, one of which is the requirement to take into account a number of different elements. In order to address this issue, the current study conducted a qualitative investigation of the difficulties that primary school children face when attempting to learn Arabic, as well as the instructional methods that are utilised by primary school teachers. Interviews with a semi-structured format were carried out with 43 primary school teachers in Peninsular Malaysia who were selected through a purposeful sampling process. This guaranteeing a broad representation of experiences was achieved. Complex script, root word system, vowels, homographs, gender and quantity, and cultural context were identified as the six primary obstacles that were discovered from the findings that were gathered. In addition, eight different instructional methods were identified, including the utilisation of visual aids, learning environments that provide contextual information, iterative repetition, interactive game-based learning, targeted vocabulary exercises, narrative techniques, collaborative learning models, and the incorporation of technology. The findings of this study showed the significance of adopting a variety of flexible teaching methodologies in order to effectively cope with the complexity of language acquisition, particularly the Arabic language.