Data Mining - Concepts and Techniques.

Currently the amount huge of data stored in educational database these database contain the useful information for predict of students performance. The most useful data mining techniques in educational database is classification. In this paper, the classification task is used to predict the final grade of students and as there are many approaches that are used for data classification, the decision tree (ID3) method is used here.

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Data Mining: A prediction for Student's Performance Using Classification Method

Objectives: Data mining techniques are implemented in many organizations as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated in the educational domain. Methods: The aim of this study presents an analysis of final year results of UG degree students using data mining technique, which carried out in three of the private colleges in Tamil Nadu state of India. The primary objective of this research work is to apply the classification techniques to the prediction of the performance of students in end semester university examinations. Particularly, the decision tree algorithm C4.5 (J48), Bayesian classifiers, k Nearest Neighbor algorithm and two rule learner’s algorithms namely OneR and JRip are used for classifying the performance of students as well as to develop a model of student performance predictors. Results: The result of this study reveals that overall accuracy of the tested classifiers is above 60%. In addition classification accuracy for the different classes reveals that the predictions are worst for distinction class and fairly good for the first class. The JRip produces highest classification accuracy for the Distinction. Classification of the students based on the attributes reveals that prediction rates are not uniform among the classification algorithms. Also shows that selected data attributes have found to be influenced the classification process. The results showed to be satisfactory. Improvements: The study can be extended to draw the performance of other classification techniques on an expanded data set with more distinct attributes to get more accurate results.

A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance

University and technical organizations are facing high competition and their challenge is in analyzing their performance. The major challenges are in admission, student placement and in the curriculum. The two most important process during which data's are collected and analyzed are admission and placement. The ranking of the university depends on academic performance and placement of the student. Apart from academic performance there are various other factors which help in understanding the overall performance of the student. In this research work, the data mining technique is used to understand the performance of student and group the students under various categories as a student need to consistently improve to compete in today's world.

Student performance analysis using clustering algorithm

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.,Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naive Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.,The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.,The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.,The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

Using educational data mining techniques to increase the prediction accuracy of student academic performance

Improving the Student’s Performance Using Educational Data Mining K.Shanmuga Priya Department of Computer Science, Hindusthan College of Arts & Science, Coimbatore-28 Email: priyasiva.0229@yahoo.com A.V.Senthil Kumar Director, Department of MCA, Hindusthan College, Coimbatore-28 Email: avsenthilkumar@yahoo.com -----------------------------------------------------------------ABSTRACT----------------------------------------------------------The main goal of educational data mining is to improve the student performance. The usage of data mining techniques which achieves the goal in an efficient manner. The discovery of knowledge that extract from the end semester [1] is one of the method for improving the quality of higher education. In the higher education, the analysis on enrolment of student’s performance in a particular course, the student talent, confidence, studies and ethic helps to get more knowledge. In this research, the data classification and decision tree [1] which helps to improve the student’s performance in a better way. But with the inclusion of extracurricular activities with the above data mining techniques makes quality of education in an easiest way. This type of approach gives high confidence to students in their studies. This method helps to identify the students who need special advising or counseling by the teacher which gives high quality of education.

Improving the Student's Performance Using Educational Data Mining

Automated video surveillance systems are highly required to facilitate wide range of applications in computer vision. The applications include form human activity understanding, object tracking, traffic monitoring to conservation of endangered species and so on. Though efficient object tracking was performed using Support Vector Machine and Graph models, with the growing size involving in multiple moving object the error and object tracking accuracy gets compromised. Bayes Particle is one solution to solve these limitations which aims to minimize the error by filtering out noisy data from a given training dataset. In this paper, Bayesian Particle Filter-based Median Enhanced Laplacian Thresholding (BPF-MELT) is introduced. The Bayes Sequential Estimation is constructed based on the posterior and prior function. To represent a comprehensive representation, Bayes Estimation algorithm discriminately trains the high quality video files with aiming at reducing the mean square tracking error. The Color Histogram-based Particle Filter measures the color histogram value using the likelihood function. The Histogram-based Particle Filter algorithm in BPF-MELT framework improves the object detection accuracy. Experiments have been carried out on video images obtained from Internet Archive 501(c) (3). An intensive and comparative study shows the efficiency of these enhancements and shows better performance in terms of mean square error, peak signal to noise ratio, object tracing accuracy and object tracking execution time on high quality video files, true positive rate, and false positive rate. Experimental analysis shows that BPF-MELT framework is able to reduce the Peak Signal to Noise Ratio by 7.79% and minimize the Mean Square Error by 29.89% when compared to the state-of-the-art works.

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An Effective Multiple Moving Objects Tracking using Bayesian Particle Filter-Based Median Enhanced Laplacian Thresholding

Currently, there is a strong need for integrating WLANs with third generation mobile network and develop heterogeneous mobile data networks, capable of ubiquitous data services and very high data rates in hotspots. Wireless third generation (3G) network is coming with many benefits and features for network users. 3G network main advantages are it provides more connectivity and reachability. In the other side, IEEE 802.11 Wireless LAN has it own advantages which are Broadband service with low cost and widespread technology. This paper evaluates the performance of two 3G/WLAN integration schemes: loose and open coupling, together with two mobility management schemes: Mobile IP and mobile stream control transmission protocol (mSCTP) for an airport as a typical example of a hot-spot location. In addition, the evaluation is carried out for a wide range of application mixes consisting of FTP, HTTP and multimedia Utilizing OPNET as the simulation platform and incorporating the required protocols. The handoff delay between the 3G and WLAN networks, the results indicate that a loose-couple integration solution together with Mobile IP provides the best performance. Index Terms — Mobile Networks, Mobile IP, mSCTP, open & loose coupling and Handover.

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Integrating of WLAN / UMTS Network in Hot-Spot Locations Using OPNET

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Enhanced Denoising Algorithm for Improving Quality of Objects in Frames for Video Surveillance

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A. Senthil Kumar

Papers

Improving the Student's Performance Using Educational Data Mining

An Effective Multiple Moving Objects Tracking using Bayesian Particle Filter-Based Median Enhanced Laplacian Thresholding

Integrating of WLAN / UMTS Network in Hot-Spot Locations Using OPNET

Enhanced Denoising Algorithm for Improving Quality of Objects in Frames for Video Surveillance

Agriculture based on Robot using Raspberry Pi