International Journal of Education and Management Engineering 

About: International Journal of Education and Management Engineering is an academic journal published by MECS Publisher. The journal publishes majorly in the area(s): Computer science & Cloud computing. It has an ISSN identifier of 2305-3623. Over the lifetime, 251 publications have been published receiving 1341 citations. The journal is also known as: IJEME.

A Study on Malware and Malware Detection Techniques

Abstract: The impact of malicious software are getting worse day by day. Malicious software or malwares are programs that are created to harm, interrupt or damage computers, networks and other resources associated with it. Malwares are transferred in computers without the knowledge of its owner. Mostly the medium used to spread malwares are networks and portable devices. Malwares are always been a threat to digital world but with a rapid increase in the use of internet, the impacts of the malwares become severe and cannot be ignored. A lot of malware detectors have been created, the effectiveness of these detectors depend upon the techniques being used. Although researchers are developing latest technologies for the timely detection of malwares but still malware creators always stay one step ahead. In this paper, a detailed review of malwares types are provided, malware analysis and detection techniques are studied and compared. Furthermore, malware obfuscation techniques have also been presented.

Analysis and Design of University Teaching Evaluation System Based on JSP Platform

Abstract: High quality of teaching is fundamental purpose and basic task of a university, as well as a foothold in the university. We introduce in this paper a university teaching evaluation. This system is used by students and experts via Servlet+JavaBean+ORACLE on campus network with the foundation of the system published by the teaching affairs bureau of university. The target system is divided into student evaluation, expert evaluation and management modules. The evaluation system is divided into two subsystems, namely, expert evaluation and student evaluation of courses. Database is the core of the whole system. It serves all the information processing modules. The implementation of the system can fully improve the quality control of teaching and lower the cost. A teaching evaluation system is analyzed and designed in this paper.

A Decision Tree Approach for Predicting Students Academic Performance

Abstract: This research is on the use of a decision tree approach for predicting students’ academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Abstract: One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

Literature Survey on Educational Dropout Prediction

Abstract: Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.