This work is designed to assist researchers and interested learners in comprehending and putting deep machine learning classification approaches into practice. It aimed to simplify, facilitate, and advance classification methodology skills. To make it easier for the users to understand, it employed a methodical approach. The categorization assessment measures seek to give the fundamentals of these measures and demonstrate how they operate to function as a comprehensive resource for academics interested in this area. Intrusion detection and threat analysis (IDAT) is a particularly unpleasant cybersecurity issue. In this study, IDAT is identified as a case study, and a real-sample dataset that was used for institutional and community awareness was generated by the researchers. This review shows that, to solve a classification problem, it is crucial to use the output of classification in terms of performance measurements, encompassing both conventional criteria and contemporary metrics. This study focused on addressing the dynamic of classification assessment capabilities for using both scalars and visual metrics, and to fix imbalanced dataset difficulties. In conclusion, this review is a useful tool for researchers, especially when they are working on big data preprocessing, handling imbalanced data for multiclass assessment, and ML classification.

Nowadays, many technology-based applications and systems embedded with cybercrimes threats and risks. Our communities lack the basic skills to secure against a smart cybercrime threats. This study conducted to test and evaluate the current cybercrime risks and awareness in Alnamas area, a district in the Southern part of Saudi Arabia. The study targeted a set of users who are highly familiar in using modern technology applications and services. All subjects are students from Bisha University, College of Science and Arts, Al-Namas. The study used 16 different parameters; each parameter covers a different cybercrime issue. The outputs showed that the reasons behind a cybercrime can be list in order as: sexual, financial, political, cultural, and fame. However, a social communication network is the most widely gateway for a cybercrime to occur (rate 69.6%). Generally, an anti-virus program is the only tool used as an anti-cybercrime technique. The results showed that, among the most common cybercrime occurred, is a sexual crime (registered the highest risk rate 57%). The study noticed that there is a lack of awareness in a cybercrime knowledge, and a strong interest for receiving an anti-cybercrime training modules (rate 80.7%).

Important elements had an impact on how traditional learning was implemented and motivated researchers to develop Interactive Video Learning Effectiveness (IVL-E). These variables range from price to learning-environment to learner perspective, among others. This paper’s major objectives are to: (i) assess the effectiveness of Interactive Video Learning (IVL-E) using classification techniques and considering graduate students’ viewpoints, (ii) establish appropriate classification parameters to choose the optimum classifier model, and (iii) review prior works pertaining to IVL-E assessment. The study dataset is a sample of 63 datapoints randomly chosen by a survey performed at the College of Education, University of Bisha, Bisha, Saudi Arabia. A total of 123 registered postgraduate students made up the study population when using Google’s online questionnaire method, after all the respondents voluntarily agreed to fill out and submit the questionnaire. This study develops a reliable machine learning classifier’s model for classifying IVL-E. The created models use a backpropagation algorithm and are a type of multilayer classification perceptron. The best classification output was “interactive video learning performance measure”, which provided the highest results under: 1) support vector machine-based classifier (SVC), 2) decision tree (DT), and 3) light gradient-boosting machine classifier (lgb.LGBMClassifier). Regarding classification measures like balanced accuracy (high BCCR = 0.875), balanced error rate (low BER = 0.125), and optimization precision (highest OP = 0.999), our models performed extremely well according to the literature review.

Authors Dept. of Information Systems, College of Science & Arts-Alnamas, University of Bisha, Saudi Arabia https://orcid.org/0000-0003-2375-6911 ayatmostafi@yahoo.com Dept. of Science and Lab. Technology, College of Science and Technology, Jigawa State Polytechnic Dutse, Nigeria Dept. of Information Systems, College of Science & Arts-Alnamas, University of Bisha, Saudi Arabia Dept. of Computer Science, College of Computer Science… اقرأ المزيد »Exploiting the Capabilities of Classifiers to Examine a Website Defacement Data Set