This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Ayala%20-%20Educational%20Data%20Mining%20-%202014.pdf

Review: Educational data mining: A survey and a data mining-based analysis of recent works

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Digital control system analysis and design

Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions/registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students' marks. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. Finally, a prototype version of software support tool for tutors has been constructed.

Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades

Visual perception refers to the process of organizing, identifying, and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this article, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this article also summarizes the current challenges of visual perception and predicts its future development trends.

/pdf/visual-perception-enabled-industry-intelligence-state-of-the-3v5dsw2daa.pdf

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

As a result of the increase in the services provided over the internet, it is seen that the network infrastructure is more exposed to cyber attacks. The most widely used of these attacks are Distributed Denial of Service (DDoS) attacks that easily disrupt services. The most important factor in the fight against DDoS attacks is the early detection and separation of network traffic. In this study, it is suggested to use the deep neural network (DNN) as a deep learning model that detects DDoS attacks on the sample of packets captured from network traffic. DNN model can work quickly and with high accuracy even in small samples because it contains feature extraction and classification processes in its structure and has layers that update itself as it is trained. As a result of the experiments carried out on the CICDDoS2019 dataset containing the current DDoS attack types created in 2019, it was observed that the attacks on network traffic were detected with 99.99% success and the attack types were classified with an accuracy rate of 94.57%. The high accuracy values obtained show that the deep learning model can be used effectively in combating DDoS attacks.

Detection of DDoS attacks with feed forward based deep neural network model

Data mining application on students’ data

https://cyberleninka.org/article/n/1126310.pdf

IPV4/IPV6 security and threat comparisons

In this study, fabric defects have been detected and classified from a video recording captured during the quality control process. Fabric quality control system prototype has been manufactured and...

A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm:

During the fabric production process, many defects can be occurred stemming from the unevenness in spinning, weaving, finishing processes, or from the raw materials. The fabric quality control process for the detection of these defects is carried out by specialist operators. In this paper, a new method based on the use of thermal camera for detecting these defects from the textile fabric images is presented. For identification process of defective area, fabric images were obtained by a thermal camera during the fabric flow in quality control machine that was specially designed for this experiment. Defective and defect-free regions on fabric surface were determined by thermal camera due to the thermal differences. The mentioned thermal defect detection system will eliminate the worker usage for fabric quality control process, thus it will provide a cost-effective and competitive manufacturing.

Ali Buldu

Papers

Detection of DDoS attacks with feed forward based deep neural network model

Data mining application on students’ data

IPV4/IPV6 security and threat comparisons

A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm:

A novel thermal-based fabric defect detection technique