Abstract

The Car manufacturing sector represents a major focus in the development of the automotive industry. In this research paper, a proposed data mining application for the automotive manufacturing sector is explained and tested. The dataset was retrieved from the machine learning repository at the University of California, Irvine. This research paper aims to create a more reliable classifier for future object classification. Classification is an important technique in data mining. It is a supervised learning process that involves classifying an object into one of the predefined classes based on its attributes. In this paper, we use a large database containing 7 attributes and 1,728 instances. We compare the

results of a simple classification technique (using the J48 decision tree inference algorithm and MONK) with results based on different parameters using WEKA (Waikato Environment Knowledge Analysis), a data mining tool. The results of the experiment show a comparison between three algorithms to see which is the best and least error-prone algorithm. The physical characteristics of a car viz . Engine location ,price, how many doors, stroke, city fuel consumption, and other factors determine a vehicle's performance. Therefore, developing such a classification, although a huge undertaking, is absolutely essential in the car industry. Machine learning techniques can help integrate computer-based systems to predict vehicle quality and improve system efficiency. Classification models were trained using 214 datasets. The predicted values of the classifiers were evaluated using 10-fold cross-validation, and the results were compared.

Keywords: Data mining, Machine learning techniques, J48, decision trees, Car market, WEKA classification.

Abstract

This research investigates how Information and Communication Technology (ICT) is incorporated at the Higher Institute of Science and Technology Badr, with a specific focus on faculty opinions and encounters. The study included a survey of 20 faculty members, along with semi-structured interviews and focus group discussions, using a combination of quantitative and qualitative approaches. The results show a mostly favourable outlook on ICT, as 75% of participants acknowledge its advantages for education. Nevertheless, there are on-going notable obstacles, such as out-dated infrastructure (40%), inadequate training (47%), and reluctance to embrace change (20%). Although 53% of faculty feel ready to integrate ICT, only 20% claim to be highly prepared. The research emphasizes the need for thorough faculty training programs and technology assistance to improve digital skills and successful ICT incorporation. In addition, it is crucial to align ICT projects with organizational goals in order to overcome obstacles and create a supportive atmosphere for integrating technology. The findings from this study help in comprehending the challenges of integrating ICT in higher education, offering beneficial suggestions for improving teaching standards with the use of technology.

Keywords: Information and Communication Technology (ICT), Faculty member perspective, Quality of teaching.

Abstract

In this paper, I discuss how artificial intelligence has become central to the formulation of ethical policies concerning data privacy. With the advancement of technology, customers’ privacy and information security concerns have come into focus and require stringent measures for the usage of data. This paper aims to analyze how AI improves data protection mechanisms for security risks pertaining to personally identifiable information while advocating for the transparency of handling personal information. Drawing evidence from the current global issues involving data privacy, the practical use of AI in cyber defense, and the social issues arising from AI technologies, this paper establishes that while developing ethical frameworks, there exists the need to continuously assess and update the ethical standards. Finally, the study confirms how AI should be incorporated within the context of data privacy to build confidence and ensure better accountability in the growing data era.

This paper explores the role of fuzzy logic in the assessment of student performance in local high school, Badr, Libya, positing that it offers a more flexible and accurate alternative to traditional categorization methods of academic achievement. Conventional assessment tools, such as standardized test scores and GPAs, often yield a distorted and overly simplistic view of student performance. Utilizing a mixed-methods research design, this study compares the performance, attendance, and self-reported satisfaction of students assessed through fuzzy logic with those evaluated by traditional methods. The results demonstrate that students assessed using fuzzy logic achieved higher average scores and expressed greater satisfaction with the assessment process. Additionally, educators employing fuzzy logic provided more detailed and constructive feedback, enriching the learning experience. Thus, this research indicates that fuzzy logic can effectively mitigate the shortcomings of traditional assessment methods, enhancing the recognition of diverse student abilities. The findings advocate for the integration of fuzzy logic in educational assessment practices to promote inclusivity and improved learning outcomes.