This document analyzes and compares the performance of various classification algorithms (J48, Random Forest, Multilayer Perceptron, IB1, Decision Table) in predicting student performance using data from 260 students. Random Forest performed the best with 89.23% accuracy, taking the least time to build the model and having the lowest error rates compared to the other algorithms. Attributes like attendance, economic status, and parental education were found to be most important factors influencing student results. The analysis provides insight into how different factors impact student performance.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
IRJET- Student Placement Prediction using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
This document summarizes a research paper that evaluates the performance of decision tree and clustering techniques using the WEKA data mining tool. The paper uses student academic and performance data to apply decision tree and clustering algorithms and compare the results of each technique. Specifically, it uses WEKA to classify and cluster a dataset containing the marks and percentages of students from educational institutions. The paper aims to determine which technique (decision tree or clustering) provides more accurate and useful results for predicting student performance.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
Predicting students' performance using id3 and c4.5 classification algorithmsIJDKP
An educational institution needs to have an approximate prior knowledge of enrolled students to predict
their performance in future academics. This helps them to identify promising students and also provides
them an opportunity to pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of students from their previous
performances using concepts of data mining techniques under Classification. We have analyzed the data
set containing information about students, such as gender, marks scored in the board examinations of
classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch
of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data,
we have predicted the general and individual performance of freshly admitted students in future
examinations.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
The document discusses a proposed students' performance prediction system using multi-agent data mining techniques. It aims to predict student performance with high accuracy and help low-performing students. The system uses ensemble classifiers like Adaboost.M1 and LogitBoost and compares their prediction accuracy to the single classifier C4.5 decision tree. Experimental results showed SAMME boosting improved prediction accuracy over C4.5 and LogitBoost.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
IRJET- Student Placement Prediction using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
This document summarizes a research paper that evaluates the performance of decision tree and clustering techniques using the WEKA data mining tool. The paper uses student academic and performance data to apply decision tree and clustering algorithms and compare the results of each technique. Specifically, it uses WEKA to classify and cluster a dataset containing the marks and percentages of students from educational institutions. The paper aims to determine which technique (decision tree or clustering) provides more accurate and useful results for predicting student performance.
Evaluating the efficiency of rule techniques for file classificationeSAT Journals
Abstract Text mining refers to the process of deriving high quality information from text. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. It is used in various areas such as information retrieval, marketing, information extraction, natural language processing, document similarity, and so on. Document Similarity is one of the important techniques in text mining. In document similarity, the first and foremost step is to classify the files based on their category. In this research work, various classification rule techniques are used to classify the computer files based on their extensions. For example, the extension of computer files may be pdf, doc, ppt, xls, and so on. There are several algorithms for rule classifier such as decision table, JRip, Ridor, DTNB, NNge, PART, OneR and ZeroR. In this research work, three classification algorithms namely decision table, DTNB and OneR classifiers are used for performing classification of computer files based on their extension. The results produced by these algorithms are analyzed by using the performance factors classification accuracy and error rate. From the experimental results, DTNB proves to be more efficient than other two techniques. Index Terms: Data mining, Text mining, Classification, Decision table, DTNB, OneR
Role of education is very critical for the development of any country. So it is the responsibility of each and every person to do something for the betterment of education. Taking this fact into consideration we start working on the education system. Education system ranging from basic to higher education. Now a day education system generates a lots of data related to student. If we cannot analyze that data properly then that data is useless. With the help of data mining techniques we can find the hidden information from the data collected for the different educational setting. With the help of that information we can review our educational process or make improvement in our education system. Here in this article we are considering a case of an engineering college student and try to predict the final result in advance. The result of the prediction provides timely help to those students who are on risk of failure in the final examination. There are different techniques of data mining are available and we are using J48, RandomForest, and ADTree to predict the performance of the student in their final examination. On the basis of this predication we can make a decision whether the student will be promoted to next year or not. We the help of the result we can improve the performance of the student who are on risk of fail or promoted. After the declaration of the final result of the student, result is fed into the system and hence the result will analysed for the next semester. The comparative result shows that, prediction help in the improvement of overall result of the weaker students.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Prediction of student performance has become an essential issue for improving the educational system. However, this has turned to be a challenging task due to the huge quantity of data in the educational environment. Educational data mining is an emerging field that aims to develop techniques to manipulate and explore the sizable educational data. Classification is one of the primary approaches of the educational data mining methods that is the most widely used for predicting student performance and characteristics. In this work, three linear classification techniques; logistic regression, support vector machines (SVM), and stochastic gradient descent (SGD), and three nonlinear classification methods; decision tree, random forest and adaptive boosting (AdaBoost) are explored and evaluated on a dataset of ASSISTment system. A k-fold cross validation method is used to evaluate the implemented techniques. The results demonstrate that decision tree algorithm outperforms the other techniques, with an average accuracy of 0.7254, an average sensitivity of 0.8036 and an average specificity of 0.901. Furthermore, the importance of the utilized features is obtained and the system performance is computed using the most significant features. The results reveal that the best performance is reached using the first 80 important features with accuracy, sensitivity and specificity of 0.7252, 0.8042 and 0.9016, respectively.
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document summarizes a research project that uses data mining classification techniques to analyze a trajectory dataset in order to predict a user's mode of transportation.
2) Several classification algorithms (decision tree, naive Bayes, Bayesian network, neural network, support vector machines) were evaluated using metrics like accuracy, recall, precision, and kappa. The results showed that decision trees and Bayesian networks performed best.
3) Future work proposed applying density-based clustering to identify dense regions and build prediction models for public vs. personal transportation use in those areas based on historical data.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
This document discusses machine learning algorithms and their applications. It begins with an abstract discussing supervised, unsupervised, and reinforcement learning techniques. It then discusses machine learning in more detail, explaining that machine learning algorithms represent data instances with a set of features and classify instances based on their labels. The main focus is on supervised and unsupervised learning techniques and their performance parameters. It provides an overview of support vector machines, neural networks, and other machine learning algorithms. In summary, the document provides a survey of different machine learning techniques, how they work, and their applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Empirical Study of the Applications of Classification Techniques in Studen...IJERA Editor
University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
This document discusses testing and loss measurement techniques in optical fiber communication systems. It describes how optical time domain reflectometry (OTDR) is commonly used to characterize optical fibers and locate faults by measuring backscattered light. OTDR can measure splice and connector losses, fiber quality, and reflectance. Other techniques mentioned include using a light source and power meter to check continuity and measure insertion loss. The document provides a table comparing the capabilities of different fiber testing equipment and concludes that both OTDR and optical loss test sets provide important loss measurement information to ensure healthy optical fiber communication.
1) A new converter topology for closed loop speed control of a switched reluctance motor is proposed, consisting of half-bridge IGBT modules and SCRs.
2) The proposed converter topology aims to improve upon the conventional asymmetric bridge converter by enhancing utilization of switching devices.
3) Simulation results in MATLAB/Simulink validate the operation of the proposed converter topology in both open loop and closed loop configurations for driving a switched reluctance motor.
This document proposes an efficient and secure distributed group key management scheme for mobile ad hoc networks (MANETs). The key points are:
1) The network is divided into clusters, with each cluster having a cluster head and members. The cluster head is responsible for generating and distributing the group key within the cluster.
2) When nodes join or leave a cluster, the cluster head regenerates the group key to ensure forward and backward secrecy.
3) A separate group key is generated among cluster heads to secure communication between clusters. Session keys are then distributed from source to destination nodes across clusters.
4) The scheme aims to reduce computation and communication costs by limiting key updates within clusters instead of the whole
This paper proposes using fuzzy cognitive maps (FCM) to automatically detect a student's learning style in an adaptive e-learning system based on the Felder-Silverman learning style model. FCM is a soft computing technique that combines fuzzy logic and neural networks. The paper reviews related work on automatic detection of learning styles. It then explains how FCM would be used to model student behaviors and interactions to identify their learning style dimensions. The results of testing this approach are discussed. The overall goal is to personalize the e-learning experience based on a student's detected learning style.
This document discusses using bloodstain pattern evidence from crime scenes to predict the positions of victims, perpetrators, and bystanders through Bayesian networks. It begins by providing context on violent crime statistics and definitions. It then outlines the typical process of investigating and reconstructing a crime scene. This involves defining, processing, and collecting information from the scene. Specifically, it discusses using bloodstain patterns to sequence events, determine directions of movement, and infer positions. The research aims to add objectivity to crime scene reconstruction by documenting how stain patterns vary with impact angles, heights, and apertures using physics and fluid mechanics principles. The goal is probabilistic positional prediction through Bayesian reasoning.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Prediction of student performance has become an essential issue for improving the educational system. However, this has turned to be a challenging task due to the huge quantity of data in the educational environment. Educational data mining is an emerging field that aims to develop techniques to manipulate and explore the sizable educational data. Classification is one of the primary approaches of the educational data mining methods that is the most widely used for predicting student performance and characteristics. In this work, three linear classification techniques; logistic regression, support vector machines (SVM), and stochastic gradient descent (SGD), and three nonlinear classification methods; decision tree, random forest and adaptive boosting (AdaBoost) are explored and evaluated on a dataset of ASSISTment system. A k-fold cross validation method is used to evaluate the implemented techniques. The results demonstrate that decision tree algorithm outperforms the other techniques, with an average accuracy of 0.7254, an average sensitivity of 0.8036 and an average specificity of 0.901. Furthermore, the importance of the utilized features is obtained and the system performance is computed using the most significant features. The results reveal that the best performance is reached using the first 80 important features with accuracy, sensitivity and specificity of 0.7252, 0.8042 and 0.9016, respectively.
Educational Data Mining is used to predict the future learning behavior of the student. It is still a research topic for the researcher who wants do better result from the prediction of the student. The results of all these techniques help the teachers, management, and administrator to draft new rules and policy for the improvement of the educational standards and hence overall results and student retention. Taking this point in mind work has been done to find the slow learner in a High School class and then provide timely help to them for improving their overall result. There are lots of techniques of data mining are available for use but we are selecting only those techniques which are mostly used by different research for their result prediction like J48, REPTree, Naive Bayes, SMO, Multilayer Perceptron. On the collected dataset Multilayer Perception classification algorithm gives 87.43% accuracy when using whole dataset as training dataset and SMO and J48 gives 69.00% accuracy when using 10-fold cross validation algorithm.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
DATA MINING METHODOLOGIES TO STUDY STUDENT'S ACADEMIC PERFORMANCE USING THE...ijcsa
The study placed a particular emphasis on the so ca
lled data mining algorithms, but focuses the bulk o
f
attention on the C4.5 algorithm. Each educational i
nstitution, in general, aims to present a high qual
ity of
education. This depends upon predicting the student
s with poor results prior they entering in to final
examination. Data mining techniques give many tasks
that could be used to investigate the students'
performance. The main objective of this paper is to
build a classification model that can be used to i
mprove
the students' academic records in Faculty of Mathem
atical Science and Statistics. This model has been
done using the C4.5 algorithm as it is a well-known
, commonly used data mining technique. The
importance of this study is that predicting student
performance is useful in many different settings.
Data
from the previous students' academic records in the
faculty have been used to illustrate the considere
d
algorithm in order to build our classification mode
l.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
1) The document summarizes a research project that uses data mining classification techniques to analyze a trajectory dataset in order to predict a user's mode of transportation.
2) Several classification algorithms (decision tree, naive Bayes, Bayesian network, neural network, support vector machines) were evaluated using metrics like accuracy, recall, precision, and kappa. The results showed that decision trees and Bayesian networks performed best.
3) Future work proposed applying density-based clustering to identify dense regions and build prediction models for public vs. personal transportation use in those areas based on historical data.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
This document discusses machine learning algorithms and their applications. It begins with an abstract discussing supervised, unsupervised, and reinforcement learning techniques. It then discusses machine learning in more detail, explaining that machine learning algorithms represent data instances with a set of features and classify instances based on their labels. The main focus is on supervised and unsupervised learning techniques and their performance parameters. It provides an overview of support vector machines, neural networks, and other machine learning algorithms. In summary, the document provides a survey of different machine learning techniques, how they work, and their applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Empirical Study of the Applications of Classification Techniques in Studen...IJERA Editor
University servers and databases store a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
Data Mining System and Applications: A Reviewijdpsjournal
In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information, generate knowledge and disseminate data, information and knowledge to every stakeholder. Due to vast use of computers and electronics devices and tremendous growth in computing power and storage capacity, there is explosive growth in data collection. The storing of the data in data warehouse enables entire enterprise to access a reliable current database. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Data Mining Application in Advertisement Management of Higher Educational Ins...ijcax
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to get enrollment in their institutes. To attract students educational institutes select a best advertisement method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data mining methods.
This document discusses testing and loss measurement techniques in optical fiber communication systems. It describes how optical time domain reflectometry (OTDR) is commonly used to characterize optical fibers and locate faults by measuring backscattered light. OTDR can measure splice and connector losses, fiber quality, and reflectance. Other techniques mentioned include using a light source and power meter to check continuity and measure insertion loss. The document provides a table comparing the capabilities of different fiber testing equipment and concludes that both OTDR and optical loss test sets provide important loss measurement information to ensure healthy optical fiber communication.
1) A new converter topology for closed loop speed control of a switched reluctance motor is proposed, consisting of half-bridge IGBT modules and SCRs.
2) The proposed converter topology aims to improve upon the conventional asymmetric bridge converter by enhancing utilization of switching devices.
3) Simulation results in MATLAB/Simulink validate the operation of the proposed converter topology in both open loop and closed loop configurations for driving a switched reluctance motor.
This document proposes an efficient and secure distributed group key management scheme for mobile ad hoc networks (MANETs). The key points are:
1) The network is divided into clusters, with each cluster having a cluster head and members. The cluster head is responsible for generating and distributing the group key within the cluster.
2) When nodes join or leave a cluster, the cluster head regenerates the group key to ensure forward and backward secrecy.
3) A separate group key is generated among cluster heads to secure communication between clusters. Session keys are then distributed from source to destination nodes across clusters.
4) The scheme aims to reduce computation and communication costs by limiting key updates within clusters instead of the whole
This paper proposes using fuzzy cognitive maps (FCM) to automatically detect a student's learning style in an adaptive e-learning system based on the Felder-Silverman learning style model. FCM is a soft computing technique that combines fuzzy logic and neural networks. The paper reviews related work on automatic detection of learning styles. It then explains how FCM would be used to model student behaviors and interactions to identify their learning style dimensions. The results of testing this approach are discussed. The overall goal is to personalize the e-learning experience based on a student's detected learning style.
This document discusses using bloodstain pattern evidence from crime scenes to predict the positions of victims, perpetrators, and bystanders through Bayesian networks. It begins by providing context on violent crime statistics and definitions. It then outlines the typical process of investigating and reconstructing a crime scene. This involves defining, processing, and collecting information from the scene. Specifically, it discusses using bloodstain patterns to sequence events, determine directions of movement, and infer positions. The research aims to add objectivity to crime scene reconstruction by documenting how stain patterns vary with impact angles, heights, and apertures using physics and fluid mechanics principles. The goal is probabilistic positional prediction through Bayesian reasoning.
This document summarizes a study on analyzing the impact of impulse noise on OFDM systems using three adaptive algorithms: LMS, NLMS, and RLS. It first describes OFDM systems and impulse noise modeling. It then provides details on the three algorithms - LMS uses a least mean square approach, NLMS is a normalized version of LMS, and RLS uses a recursive least squares approach. Simulation results show transmitted OFDM signals and spectra, as well as BER plots for the different algorithms under varying SNR levels. RLS is found to have the best performance with minimum BER, followed by NLMS, and then LMS. The document concludes RLS is the best algorithm to use for its sustainability to higher
This document discusses audio compression using multiple transformation techniques for audio applications. It compares the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) for compressing audio signals. The DCT and DWT are applied to audio signals to generate new data sets with smaller values, achieving compression. Performance is evaluated using metrics like compression ratio, peak signal-to-noise ratio, signal-to-noise ratio, and normalized root mean square error. The results show that DWT provides a lower compression ratio but higher performance metrics compared to DCT. Overall, the document examines using DCT and DWT transforms to compress audio signals and compares their performance.
The document describes an improved PIN entry method to prevent shoulder surfing attacks. The basic BW method divides numbers between two colored halves in each round. An observer can group entries over rounds to determine a PIN digit. The improved method displays four random colors across ten digit keys in each round. Users enter based on their PIN digit's color. This prevents grouping as colors randomly change, securing PINs even from skilled observers. The method uses sets and permutations to partition colors after each entry.
This document proposes and evaluates a context-based access control (CBAC) mechanism for Android systems. The CBAC mechanism allows users to set configuration policies over applications' usage of device resources and services based on the user's context. The proposed system uses context sensing and machine learning to classify contexts and then dynamically grants or revokes application privileges. Experiments show the CBAC mechanism incurs negligible energy overhead compared to the stock Android system. The CBAC framework provides improved privacy and security over existing location-based policy systems.
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive digital filters and their application in noise cancellation. An adaptive filter uses an algorithm to adjust its transfer function to minimize error and remove correlated noise from a measured signal. The document then discusses using a neural network approach with an exact random basis function for adaptive noise cancellation. It describes the radial basis function network architecture, which has a hidden layer of neurons that respond based on the distance between input and stored patterns. This neural network approach is used to minimize error and obtain an output signal that is closer to the desired input signal with noise removed. Simulation results are also mentioned to demonstrate reduced noise using this neural network algorithm.
1) The document presents two models for operating a wound rotor induction motor at constant maximum torque from standstill to a slip of 0.415 using feedback control of rotor speed and slip.
2) Both models use a diode bridge to rectify the AC output voltage of the motor, which is then fed to a parallel combination of an IGBT and external resistance. Feedback control varies the IGBT duty cycle to control the effective external resistance.
3) The first model directly controls the duty cycle, while the second adds an inductor for filtering. Simulation results in MATLAB show both models maintain constant maximum torque as intended.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
This document summarizes a study that used the Taguchi method to optimize electrical discharge machining (EDM) parameters for material removal rate (MRR) and electrode wear rate (EWR) in machining EN36 alloy steel. Experiments were conducted using an L9 orthogonal array with four parameters (pulse on time, pulse off time, current, and voltage) each at three levels. Analysis of variance showed current had the greatest effect on MRR while conformational tests identified the optimal parameters as pulse on time of 100 μs, pulse off time of 12 μs, current of 14 A, and voltage of 40 V. A similar process was followed to analyze EWR, with current also having the greatest impact based on ANOVA.
This document provides a review of previous research on thermoelectric generators. It begins with an introduction to solar energy and thermoelectric generation technologies. It then discusses the basic components of a thermoelectric module, including the thermocouple. The document reviews three previous works: 1) fabrication and testing of flat plate solar thermoelectric generators for near-earth orbits, which achieved power outputs of 3-3.3 watts; 2) development of a high efficiency thermoelectric power generator using bismuth telluride that achieved a maximum efficiency of 1.85%; 3) parametric analysis and modeling of a solar heat pipe thermoelectric generator unit comprising a thermoelectric module, finned heat pipe,
This document describes a novel fractal reconfigurable multiband antenna designed for cognitive radio applications. The antenna consists of a triangular patch with triangular slots and switches integrated along the slots. By turning the switches on and off, the electrical lengths and current paths are varied, changing the antenna's resonant frequencies. Simulation results show the antenna can achieve frequencies from 1.6-12 GHz across 8 switching states. Measured return loss results validated the reconfigurable multiband performance, making the antenna suitable for cognitive radio frequency switching capabilities. The antenna is compact, low cost, and provides multiband operation with frequency agility using a simple switching technique.
This document summarizes a study that investigated the performance and emissions of a diesel engine operating on apricot oil blended with methanol. The study found that using the biofuel blend can reduce emissions like hydrocarbons and carbon monoxide compared to diesel fuel alone. Experiments were conducted on a twin cylinder compression ignition engine to evaluate brake thermal efficiency, brake specific energy consumption, and exhaust emissions including hydrocarbons, carbon dioxide, nitrogen oxides, and smoke at varying engine loads. The results showed that the biofuel blend provided better performance characteristics than diesel in terms of emissions and thermal efficiency. Therefore, the document concludes that apricot oil blended with methanol is a suitable alternative fuel for diesel engines.
This document discusses cooperative spectrum sensing in cognitive radio networks to improve energy efficiency and throughput. It proposes deriving the optimal number of cooperating cognitive radios under two scenarios: 1) minimizing radios needed for a given detection performance to maximize energy efficiency, and 2) maximizing throughput by optimizing the reporting time given a detection constraint. Computer simulations show that an OR fusion rule outperforms AND in both scenarios using fewer radios.
1) The document derives the equations describing the interaction forces between two identical cylinders spinning around their stationary and parallel axes in an ideal fluid.
2) It finds the velocity field satisfies the boundary conditions of the fluid velocity matching the cylinders' rotation at the surface and being zero at infinity.
3) It then determines the pressure field from the velocity field using Bernoulli's equation and integrates the pressure around the cylinders' surfaces to obtain the forces acting on their axes.
This document presents a new segmentation technique for brain MRI images and compares it to existing techniques. The proposed technique is a two-stage brain extraction algorithm (2D-BEA) that first removes noise and enhances brain boundaries, then uses morphological operations to extract the brain region. It is shown to accurately extract the brain from MRI images. The technique is then compared to other segmentation methods like thresholding, edge detection, fuzzy c-means clustering, and k-means clustering. The results demonstrate that the 2D-BEA technique outperforms these other methods in effectively segmenting the brain region from MRI images.
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
The use of recent technology creates more impact in the teaching and learning process nowadays. Improvement of students’ knowledge by using the various technologies like smart class room environment, internet, mobile phones, television programs, use of iPods and etc. are play a very important role. Most of the education institutions used classroom teaching using advanced technologies such as smart class environment, visualization by power point projector and etc. This research work focusses on such technologies used for the improvement of student’s performance using some of the Data Mining (DM) techniques particularly classification and clustering. Information repositories (Educational Data Bases, Data Warehouses) are the source place for collecting study materials and use them for their learning purposes is the number one source for preparation of examinations. Particularly, this research work analyzes about the use of clustering and classification algorithms to enable the student’s performances and their learning capabilities using these modern technologies. During the study period, the student’s family background and their economic status are also play a very important role in their daily activities. These things are not considered in this survey work. A comparative study is carried out in this work by comparing students performance based on their results. The comparison is carried out based on the results of some of the classification and clustering algorithms. Finally, it states that the best algorithm for the improvement of students performance using these algorithms.
Technology Enabled Learning to Improve Student Performance: A SurveyIIRindia
This document discusses using data mining techniques like classification and clustering algorithms to analyze how technology can improve student performance. It provides an overview of several research papers on this topic, including how they selected data sets and technologies. Specifically, it examines the role of classification algorithms in learning data mining and discusses papers that used algorithms like Naive Bayes, J48, and support vector machines to analyze student performance data. It also discusses the use of clustering algorithms for grouping students and analyzing their learning. In general, the document analyzes how data mining can help evaluate the impact of technologies on student learning and performance.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
WEB-BASED DATA MINING TOOLS : PERFORMING FEEDBACK ANALYSIS AND ASSOCIATION RU...IJDKP
This document describes web-based data mining tools for performing association rule mining and feedback analysis on educational data. It presents two tools - one developed using ASP.NET for association rule mining to analyze elective course combinations, and one developed using PHP to collect and analyze student feedback on faculty performance and institutional infrastructure. The tools are intended to help educational institutions improve decision making, teaching effectiveness, and student outcomes by analyzing patterns in student feedback and course selection data. Sample outputs from applying the tools to student data demonstrate their ability to discover useful associations and evaluate performance.
Recommendation of Data Mining Technique in Higher Education Prof. Priya Thaka...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Extending the Student’s Performance via K-Means and Blended Learning IJEACS
In this paper, we use the clustering technique to monitor the status of students’ scholastic recital. This paper spotlights on upliftment the education system via K-means clustering. Clustering is the process of grouping the similar objects. Commonly in the academic, the performances of the students are grouped by their Graded Point (GP). We adopted K-means algorithm and implemented it on students’ mark data. This system is a promising index to screen the development of students and categorize the students by their academic performance. From the categories, we train the students based on their GP. It was implemented in MATLAB and obtained the clusters of students exactly.
Data Mining Techniques for School Failure and Dropout SystemKumar Goud
Abstract: Data mining techniques are applied to predict college failure and bum of the student. This is method uses real data on middle-school students for prediction of failure and drop out. It implements white-box classification strategies, like induction rules and decision trees or call trees. Call tree could be a call support tool that uses tree-like graph or a model of call and their possible consequences. A call tree is a flowchart-like structure in which internal node represents a "test" on an attribute. Attribute is the real information of students that is collected from college in middle or pedagogy, each branch represents the outcome of the test and each leaf node represents a class label. The paths from root to leaf represent classification rules and it consists of three kinds of nodes which incorporates call node, likelihood node and finish node. It is specifically used in call analysis. Using this technique to boost their correctness for predicting which students might fail or dropout (idler) by first, using all the accessible attributes next, choosing the most effective attributes. Attribute choice is done by using WEKA tool.
Keywords: dataset, classification, clustering.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
Prognostication of the placement of students applying machine learning algori...BIJIAM Journal
Placement is the process of connecting the selected candidate with the employer. Every student might have adream of having a job offer when he or she is about to complete her course. All educational institutions aim athaving their students well placed in good organizations. The reputation of any institution depends on the placementof its students. Hence, many institutions try hard to have a good placement cell. Classification using machinelearning may be utilized to retrieve data from the student-databases. A prediction model that can foretell theeligibility of the students based on their academic and extracurricular achievements is proposed. Related data wascollected from many institutions for which the placement-prediction is made. This paradigm is being weighed upwith the existing algorithms, and findings have been made regarding the accuracy of predictions. It was found thatthe proposed algorithm performed significantly better and yielded good results.
This document provides a systematic review of educational data mining (EDM) techniques and their applications. It discusses how EDM can be used to extract hidden information from large student data repositories using clustering, classification, prediction, and recommendation algorithms. These algorithms help group similar students, categorize students, predict student outcomes, and suggest courses. The document also reviews literature applying these EDM techniques and outlines future work on semantic and opinion mining to improve adaptive learning systems.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
Association rule discovery for student performance prediction using metaheuri...csandit
According to the increase of using data mining tech
niques in improving educational systems
operations, Educational Data Mining has been introd
uced as a new and fast growing research
area. Educational Data Mining aims to analyze data
in educational environments in order to
solve educational research problems. In this paper
a new associative classification technique
has been proposed to predict students final perform
ance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative cla
ssifiers maintain interpretability along
with high accuracy. In this research work, we have
employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract associat
ion rule for student performance prediction
as a multi-objective classification problem. Result
s indicate that the proposed swarm based
algorithm outperforms well-known classification tec
hniques on student performance prediction
classification problem.
ASSOCIATION RULE DISCOVERY FOR STUDENT PERFORMANCE PREDICTION USING METAHEURI...cscpconf
According to the increase of using data mining techniques in improving educational systems
operations, Educational Data Mining has been introduced as a new and fast growing research
area. Educational Data Mining aims to analyze data in educational environments in order to
solve educational research problems. In this paper a new associative classification technique
has been proposed to predict students final performance. Despite of several machine learning
approaches such as ANNs, SVMs, etc. associative classifiers maintain interpretability along
with high accuracy. In this research work, we have employed Honeybee Colony Optimization
and Particle Swarm Optimization to extract association rule for student performance prediction
as a multi-objective classification problem. Results indicate that the proposed swarm based
algorithm outperforms well-known classification techniques on student performance prediction
classification problem.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
Data mining referred to extracting the hidden predictive information from huge amount of data set. Recently, there are number of private institution are came into existence and they put their efforts to get fruitful admissions. In this paper, the techniques of data mining are used to analyze the mind setup of student after matriculate. One of the best tools of data mining is known as WEKA (Waikato Environment Knowledge Analysis), is used to formulate the process of analysis.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
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1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 1, Ver. III (Jan. 2014), PP 63-69
www.iosrjournals.org
www.iosrjournals.org 63 | Page
An Analysis of students’ performance using classification
algorithms
Mrs. M.S. Mythili1
, Dr. A.R.Mohamed Shanavas2
1
Ph.D Research Scholar, Bharathidasan University & Assistant Professor,
Department of Computer Applications, Bishop Heber College, Tiruchirappalli 620 017, TamilNadu, India.
2
Associate Professor, Dept. of Computer Science, Jamal Mohamed College,
Tiruchirappalli 620 020,TamilNadu, India.
Abstract: In recent years, the analysis and evaluation of students‟ performance and retaining the standard of
education is a very important problem in all the educational institutions. The most important goal of the paper
is to analyze and evaluate the school students‟ performance by applying data mining classification algorithms in
weka tool. The data mining tool has been generally accepted as a decision making tool to facilitate better
resource utilization in terms of students‟ performance. The various classification algorithms could be
specifically mentioned as J48, Random Forest, Multilayer Perceptron, IB1 and Decision Table are used. The
results of such classification model deals with accuracy level, confusion matrices and also the execution time.
Therefore conclusion could be reached that the Random Forest performance is better than that of different
algorithms.
Keywords: Decision Table, IB1, J48, Multilayer Perceptron, Random Forest
I. Introduction
Data Mining could be a promising and flourishing frontier in analysis of data and additionally the result
of analysis has many applications. Data Mining can also be referred as Knowledge Discovery from Data
(KDD).This system functions as the machine-driven or convenient extraction of patterns representing
knowledge implicitly keep or captured in huge databases, data warehouses, the Web, data repositories, and
information streams. Data Mining is a multidisciplinary field, encompassing areas like information technology,
machine learning, statistics, pattern recognition, data retrieval, neural networks, information based systems,
artificial intelligence and data visualization.
The application of data mining is widely prevalent in education system. Educational data mining is an
emerging field which can be effectively applied in the field of education. The educational data mining uses
several ideas and concepts such as Association rule mining, classification and clustering. The knowledge that
emerges can be used to better understand students’ promotion rate, students’ retention rate, students’ transition
rate and the students’ success. The data mining system is pivotal and crucial to measure the students’
performance improvement. The classification algorithms can be used to classify and analyze the students’ data
set in accurate manner. The students’ academic performance is influenced by various factors like parents’
education, locality, economic status, attendance, gender and result.
The main objective of the paper is to use data mining methodologies to study and analyze the school
students’ performance. Data mining provides many tasks that could be used to study the students’ performance.
In this paper, the classification task is employed to gauge students’ performance and deals with the accuracy,
confusion matrices and the execution time taken by the various classification data mining algorithms.
This paper is catalogued as follows. Section 2 enumerates a related work. Section 3 presents the idea
of Classification and discusses the aspects of classification algorithm. Section 4 elaborates a Data Preprocessing.
Section 5 explains the Implementation of model construction. Section 6 describes the results and discussions.
Section 7 provides the conclusion.
II. Related Work
Alaael-Halees 2009 suggested that Data Mining is an emerging methodology used in educational field
to enhance the understanding of learning process. The application of Data mining is widely spread in Higher
Education system. In Education domain many researchers and authors have explored and discussed various
applications of data mining in higher education. The authors had gone through the survey of the literature to
understand the importance of data mining applications. In the year 2001 Luan al. suggested a powerful decision
support tool called data mining. Data Mining is a powerful tool for academic purposes Alumni, Institutional
effectiveness, marketing and enrollment can benefit from the use of data mining Data Mining is the most suited
technology that can be used by lecturer, student, alumnus, manager and other educational staff and is a useful
tool for decision making on their educational activities
2. An Analysis of students‟ performance using classification algorithms
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Delmater et.al. Placed stress on underlying predictive modeling which is a mixture of mathematics,
computer science and domain expertise Qasem et.al. Started an attempt to use data mining functions to analyze
and evaluate student academic data and to enhance the quality of the higher educational system. The higher
managements can use such classification model to enhance the courses outcome according to the extracted
knowledge. Such knowledge can be used to give a deeper understanding of student’s enrollment pattern in the
course under study, and the faculty and managerial decision maker in order to utilize the necessary actions
needed to provide extra basic course skill classes and academic counseling. On the other hand, using such
knowledge the management system can improve their policies, enhance their strategy, and improve the quality
of management system.
III. Classification
This process is employed to classify data into predefined categorical class labels. Classification can be
a two step process consisting of training and testing. In the first step, a model is constructed by analyzing the
data tuples from training data having a collection of attributes. For every tuple in the training data, the worth of
class label attribute is understood. Classification rule is applied on training data to form the model. In the second
step of classification, test data is employed to examine the accuracy of the model. If the accuracy of the model is
appropriate then the model can be used to classify the unknown data tuples. The fundamental techniques for
classification are decision tree classifier, neural networks, rule based classifier and Lazy based classifier.
3.1. Classification Algorithms
This research paper contains a rule based classifier (Decision Table), a common decision tree classifier
C4.5 (J48), Random Forest, a neural network (Multilayer Perceptron) and a Lazy based classifier (IB1).The
classifiers are mentioned in brief.
3.2. Decision tree classifiers
A decision tree can be a flow chart resembling a tree structure, where every internal node is denoted by
rectangles and the leaf nodes are denoted by ovals. This is often used algorithm because of easy implementation
and easier to understand compared to different classification algorithms. Decision tree starts with a root node
that helps the users to take required actions. From this node, users split every node recursively according to
decision tree learning algorithm. The ultimate result is a decision tree in which each branch represents an
outcome.
3.2.1. C4.5 (J48)
This algorithm can be a successor to ID3 developed by Quinlan Ross. It is additionally supported the
Hunt’s algorithm.C4.5 handles each categorical and continuous attributes to create a decision tree, so as to
handle continuous attributes. C4.5 splits the attribute values into 2 partitions based on the chosen threshold. It
additionally handles missing attribute values. C4.5 has the concept of Gain Ratio as an attribute selection
measure to create a decision tree. It prunes the biasness of information gain once there are many outcome
values of an attribute. At first, calculate the gain ratio of every attribute. The root nodes are the attribute whose
gain ratio is maximum. C4.5 uses pessimistic pruning to get rid of unessential branches with in the decision tree
to enhance the accuracy of classification.
3.2.2. Random Forest
Random Forests is a bagging tool that leverages the ability of multiple varied analyses, organization
strategies, and ensemble learning to supply correct models, perceptive variable importance ranking, and laser-
sharp coverage on a record-by-record basis for deep data understanding. Its strengths are recognizing outliers
and anomalies in knowledgeable data, displaying proximity clusters, predicting future outcomes, characteristic
necessary predictions, discovering data patterns, exchange missing values with imputations, and providing
perceptive graphics.
3.3. Neural Network
Multilayer Perceptron (MLP) algorithm is one of the most widely used and common neural networks.
Multilayer Perceptron (MLP) is a feed forward artificial neural network model that maps sets of input data onto
a collection of acceptable output. An MLP consists of multiple layers of nodes in an exceedingly directed graph,
with every layer totally connected to the consequent one. Their current output depends solely on the present
input instance. It trains victimization back propagation.
3. An Analysis of students‟ performance using classification algorithms
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3.4. IB1
IBI is nearest neighbor classifier. It uses normalized Euclidean distance to search out the training
instance nearest to the given test instance, and predicts the identical category as this training instance. If many
instances have the smallest distance to the test instance, the primary one obtained is employed. Nearest neighbor
methodology is one of the effortless and uncomplicated learning/classification algorithms, and has been
effectively applied to a broad variety of issues.
3.5. Decision Table
Decision Tables are classification models elicited by machine learning algorithms and are used for
creating predictions. A decision table consists of a hierarchical table within which entry in a higher level table
gets broken down by the values of a pair of additional attributes to make another table.
IV. Data Preprocessing
Datasets utilized within the classification algorithm ought to be clear and can be preprocessed for
handling missing or redundant attributes. The data are to be handled with efficiency to induce the best outcome
from the Data Mining process.
4.1. Attribute Identification
Dataset collected from student database consists of
Attributes Description Possible values
Gender Gender (male, female) M,f
Locality Living locality Urban, Rural
paredu Parental education Edu,unedu
Eco Economic status High, Low
Attendance Class attendance High, Low
Result Students’ result First,Second,Third,Fail
V. Implementation of Model Construction
Weka is open source software system that implements a large collection of machine learning
algorithms and is widely utilized in data mining applications. From the above data, student.arff file was created.
This file was loaded into a WEKA explorer. The students’ academic performance is influenced by various
factors like parents’ education, locality, economic status, attendance, gender and result from the different school
students.260 samples were taken for the implementation. The classify panel permits the user to use classification
algorithms to the dataset, to estimate the accuracy of the resulting predictive model, and to visualize the model.
The decision tree classifier C4.5 (J48), Random Forest, Neural Network (Multilayer Perceptron) and Lazy
based classifier (IB1) Rule based classifier (Decision Table) were enforced in weka. Under the “Test options”,
the 10 fold cross validation is chosen.
VI. Results and Discussion
The analysis and interpretation of classification is time consuming process that needs a deep
understanding of statistics. The process needs a large amount of time to finish and expert analysis to look at the
classification and relationships within the data.
TABLE 1: Attributes Ranking using information gain and gain ratio
S.No Attribute Information Gain Gain Ratio
Value Rank Value Rank
1. Gender 0.0286 5 0.035 5
2. Locality 0.0544 4 0.0544 4
3. P.ed 0.1016 3 0.1193 3
4. Attendance 0.6429 1 0.6592 1
5. Eco 0.579 2 0.582 2
This section presents the results generated from the study. The attributes were ranked in order of its
importance using information gain and gain ratio measures. The ranking of each Attribute evaluators was done
using ranker search method.
4. An Analysis of students‟ performance using classification algorithms
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Figure 1: information gain of the attributes
Figure 2: gain ratio of the attributes
The Figure 1 and Figure 2 clearly identify the Attribute ranking according to information gain and gain
ratio of the attributes.
Figure 3: Visual image of generated decision trees
The attendance of the students’ is
taken as root node from that economical status and parents’ education support taken as branch node and so on.
The knowledge represented by decision tree can be extracted within the form of IF-THEN rules.
1. IF attendance=”high” AND eco=”high” THEN result=”first”
2. IF attendance=”high” AND eco=”low” THEN result=”second”
3. IF attendance=”low” AND eco=”high” AND p.ed=edu AND locality=urban THEN result=”third”
4. IF attendance=”low” AND eco=”high” AND p.ed=edu AND locality=rural AND gender=”m” THEN
result=”second”
5. IF attendance=”low” AND eco=”high” AND p.ed=edu AND locality=rural AND gender=”f” THEN
result=”third”
6. IF attendance=”low” AND eco=”high” AND p.ed=unedu THEN result=”third”
7. IF attendance=”low” AND eco=”low” AND gender=m THEN result=”fail”
8. IF attendance=”low” AND eco=”low” AND gender=f AND p.ed=edu THEN result=”fail”
9. IF attendance=”low” AND eco=”low” AND gender=f AND p.ed=unedu THEN result=”first”
5. An Analysis of students‟ performance using classification algorithms
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From the above set of rules an inescapable conclusion emerges the attendance is considerably related with
student performance. From the rule set it was found that parent education, locality, gender, Economic Status,
and different factors are of high potential variable that have an effect on students’ performance for getting good
performance in examination result.
Table 2 Performance result for Classifiers
Evaluation
Criteria
Classifiers
J48
Random
Forest
Multilayer Perceptron IB1 Decision Table
Timing to build the
model(sec)
0.03 0 1.11 0.01 0.02
Correctly
classified instances
224 232 227 209 224
Incorrectly
classified instances
36 28 33 51 36
Accuracy (%) 86.15% 89.23% 87.30% 80.38% 86.15%
In Table 2 the time build by the Random Forest is less than the remaining classifier is shown and
therefore the percentage of correctly classified instances is usually referred as accuracy of the model. Hence
Random Forest classifier can be termed as more accurate than other classifiers.
Figure 4: Time taken to build the classifier algorithm
Figure 5: Accuracy of the classifier algorithm
The Figure 4 and Figure 5 shows that the graphical representation of time and accuracy results of
school students’ performance analysis based on students’ dataset. It clearly reveals that Random Forest is a very
best classifier for analyzing the school students’ performance result consuming less time coupled with good
accuracy.
Figure 6: Efficiency of different classifiers
6. An Analysis of students‟ performance using classification algorithms
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The Figure 6 explains the graphical representation of correctly classified instances of results of school
students’ performance analysis based mostly on student dataset. The highest percentage of correctly classified
instances is the Random Forest classifier.
Table 3: Error measurement for classifiers
Evaluation
Criteria
Classifiers
J48 Random Forest Multilayer Perceptron IB1 Decision Table
Kappa statistic 0.8116 0.8531 0.8278 0.7306 0.8118
Mean absolute error 0.0984 0.0836 0.0836 0.0981 0.1328
Root mean squared
error(RMSE)
0.2336 0.2128 0.23 0.3132 0.2384
Relative absolute error(RAE) 26.9378% 22.8844% 22.8995% 26.8611% 36.3805%
Root relative squared
error(RRSE)
54.6895% 49.8177% 53.8346% 73.3071% 55.8143%
In Table 3 it explains the time build by the Random Forest is less than the remaining classifier. Kappa
statistics is a measure of the degree of non random agreement between observers and measurement of a
particular categorical variable. The root mean square error and Mean absolute error of Random Forest are
minimum when compared to other classifiers. Therefore the Random Forest is that the efficient classification
technique among remaining classification technique.
Figure 5: Error rate of different classifiers
The Figure 5 compares errors among completely different classifiers (root mean square error and Mean
absolute error) Random Forest has lower error rate compared to different classifiers. Therefore the Random
Forest is the efficient classification technique among remaining classifiers.
Table 4: Class label accuracy for classifiers
classifier TP FP Precision Recall Class
J48
0.919 0.111 0.722 0.809 First
0.88 0.029 0.88 0.88 Fail
0.844 0.018 0.964 0.9 Second
0.808 0.024 0.894 0.848 Third
Random Forest
0.935 0.101 0.744 0.935 First
0.94 0.014 0.94 0.94 Fail
0.875 0.018 0.966 0.875 Second
0.827 0.01 0.956 0.827 Third
MLP
0.887 0.086 0.764 0.887 First
0.96 0.038 0.857 0.96 Fail
0.844 0.012 0.976 0.844 Second
0.827 0.029 0.878 0.827 Third
IB1
0.645 0.086 0.702 0.645 First
0.94 0.029 0.887 0.94 Fail
0.865 0.104 0.83 0.865 Second
0.75 0.053 0.78 0.75 Third
Decision Table
0.887 0.101 0.733 0.887 First
0.92 0.038 0.852 0.92 Fail
0.844 0.018 0.964 0.844 Second
0.808 0.024 0.894 0.808 Third
The Table 4 clearly shows the performance of every classifier based on the true positive rate (TP rate)
and false positive rate (FP rate), precision, recall and different measures. These measures are very helpful for
comparing the classifiers based on the accuracy. The Random Forest outperforms all different classifiers within
the students’ dataset.
7. An Analysis of students‟ performance using classification algorithms
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Table 5: Confusion Matrix
Classifier First FAIL Second Third Class
J48
57 4 1 0 First
6 44 0 0 Fail
10 0 81 5 Second
6 2 2 42 Third
Random Forest
58 2 2 0 First
3 47 0 0 Fail
10 0 84 2 Second
7 1 1 43 Third
MLP
55 6 1 0 First
1 48 0 1 Fail
10 0 81 5 Second
6 2 1 43 Third
IB1
40 4 10 8 First
2 47 1 0 Fail
10 0 83 3 Second
5 2 6 39 Third
Decision Table
55 6 1 0 First
4 46 0 0 Fail
10 0 81 5 Second
6 2 2 42 Third
The Table 5 reveals that the confusion matrices are very helpful for analyzing the classifiers.
VII. Conclusion
The work explores the potency of machine learning algorithms in deciding the influence of result,
parental education, gender, economy and the locality within the study and analyze of school
students’performance.It is discovered that Random Forest performance is best than that of different algorithms
employed in the study. This study is going to be terribly useful for the educational institutions. In future, it is
doable to increase the analysis by using different clustering techniques and association rule mining for the
students’ dataset.
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