Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

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Hybrid classification model to detect advanced intrusions using data mining techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.10031 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 10

Author(s):

V Mala ◽

K Meena

Keyword(s):

Data Mining ◽

Learning Algorithm ◽

Detection System ◽

Classification Model ◽

Detection Methods ◽

Data Mining Techniques ◽

Detection Systems ◽

Intruder Detection ◽

Hybrid Classification ◽

Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

Academic Journal of Nawroz University ◽

10.25007/ajnu.v10n3a875 ◽

2021 ◽

Vol 10 (3) ◽

pp. 121-127

Author(s):

Bareen Haval ◽

Karwan Jameel Abdulrahman ◽

Araz Rajab

Keyword(s):

Data Mining ◽

Decision Tree ◽

Student Performance ◽

Educational Data Mining ◽

Data Sets ◽

Decision Tree Classifier ◽

Data Mining Techniques ◽

Academic History ◽

Tree Classifier ◽

Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

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