Predictive models are quasi experimental structures used to determine the future
patterns in data. These meaningful data patterns form the building block of any
decision support system. Researchers all over the world have built many prediction
models for major industries. Research works in the educational sector has increased
steeply. This steep increase may be due to the high availability of data in the
educational domain. This survey tries to comprehend a few literary works on
academic performance prediction of engineering students with the focus on grade
predictions. Meaningful interpretations have been made and inferences are presented
at the end of this paper
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.
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.
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
Correlation based feature selection (cfs) technique to predict student perfro...IJCNCJournal
Education data mining is an emerging stream which h
elps in mining academic data for solving various
types of problems. One of the problems is the selec
tion of a proper academic track. The admission of a
student in engineering college depends on many fact
ors. In this paper we have tried to implement a
classification technique to assist students in pred
icting their success in admission in an engineering
stream.We have analyzed the data set containing inf
ormation about student’s academic as well as socio-
demographic variables, with attributes such as fami
ly pressure, interest, gender, XII marks and CET ra
nk
in entrance examinations and historical data of pre
vious batch of students. Feature selection is a pro
cess
for removing irrelevant and redundant features whic
h will help improve the predictive accuracy of
classifiers. In this paper first we have used featu
re selection attribute algorithms Chi-square.InfoGa
in, and
GainRatio to predict the relevant features. Then we
have applied fast correlation base filter on given
features. Later classification is done using NBTree
, MultilayerPerceptron, NaiveBayes and Instance bas
ed
–K- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
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.
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.
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
Correlation based feature selection (cfs) technique to predict student perfro...IJCNCJournal
Education data mining is an emerging stream which h
elps in mining academic data for solving various
types of problems. One of the problems is the selec
tion of a proper academic track. The admission of a
student in engineering college depends on many fact
ors. In this paper we have tried to implement a
classification technique to assist students in pred
icting their success in admission in an engineering
stream.We have analyzed the data set containing inf
ormation about student’s academic as well as socio-
demographic variables, with attributes such as fami
ly pressure, interest, gender, XII marks and CET ra
nk
in entrance examinations and historical data of pre
vious batch of students. Feature selection is a pro
cess
for removing irrelevant and redundant features whic
h will help improve the predictive accuracy of
classifiers. In this paper first we have used featu
re selection attribute algorithms Chi-square.InfoGa
in, and
GainRatio to predict the relevant features. Then we
have applied fast correlation base filter on given
features. Later classification is done using NBTree
, MultilayerPerceptron, NaiveBayes and Instance bas
ed
–K- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
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.
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.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance IJECEIAES
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
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.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
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.
The journal publishes original works with practical significance and academic value. Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various
types of problems. One of the problems is the selection of a proper academic track. The admission of a
student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank
in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
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.
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.
Fuzzy Association Rule Mining based Model to Predict Students’ Performance IJECEIAES
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
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.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
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.
The journal publishes original works with practical significance and academic value. Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various
types of problems. One of the problems is the selection of a proper academic track. The admission of a
student in engineering college depends on many factors. In this paper we have tried to implement a
classification technique to assist students in predicting their success in admission in an engineering
stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank
in entrance examinations and historical data of previous batch of students. Feature selection is a process
for removing irrelevant and redundant features which will help improve the predictive accuracy of
classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and
GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given
features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based
–K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Education data mining is an emerging stream which helps in mining academic data for solving various types of problems. One of the problems is the selection of a proper academic track. The admission of a student in engineering college depends on many factors. In this paper we have tried to implement a classification technique to assist students in predicting their success in admission in an engineering stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank in entrance examinations and historical data of previous batch of students. Feature selection is a process for removing irrelevant and redundant features which will help improve the predictive accuracy of classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based –K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...indexPub
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
Performance Assessment of Faculties of Management Discipline From Student Per...Waqas Tariq
This paper deals with Faculty Performance Assessment from student perspective using Data Analysis and Mining techniques .Performance of a faculty depends on a number of parameters (77 parameters as identified) and the performance assessment of a faculty/faculties are broadly carried out by the Management Body ,the Student Community ,Self and Peer faculties of the organization .The parameters act as performance indicators for an individual and group and subsequently can impact on the decision making of the stakeholders. The idea proposed in this paper is to perform an analysis of faculty performance considering student feedback which can directly or indirectly impact management’s decision, teaching standards and norms set by the educational institute, understand certain patterns of faculty motivation, satisfaction, growth and decline in future. The analysis depends on many factors, encompassing student’s feedback, organizational feedback, institutional support in terms of finance, administration, research activity etc. The data analysis and mining methodology used for extracting useful patterns from the institutional database has been used to extract certain trends in faculty performance when assessed on student feedback.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Performance Evaluation of Feature Selection Algorithms in Educational Data Mi...IIRindia
Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be
Title: A Machine Learning Approach to Performance and Dropout prediction in Computer Science: Bangladesh Perspective
Reference: Reference: Ahmed, S. A., Billah, M. A., & Khan, S. I. (2020, July). A Machine Learning Approach to Performance and Dropout prediction in Computer Science: Bangladesh Perspective. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
2. V. Sathya Durga and J. Thangakumar
http://www.iaeme.com/IJCIET/index.asp 49 editor@iaeme.com
1.1. Predicting Students Academic performance
Predicting students performance is a significant research work in the educational sectors. Few
Years back, students waited till the end of the semester to know their GPA. With the advent
of machine learning, we can predict all semesters grades easily and we have models which
predict the likelihood of a student being placed or not.
1.2. Four Core Components of a Predictive Model
Building a predictive model is very challenging work. To Build a predictive model, we
require four important components. These components are listed below for easy reference.
The Methodology followed to deploy the model.
Techniques adopted to build the prediction model.
Input attributes used by the model and
Performance Metrics used to evaluate the model.
This survey tries to the unravel common methodology, techniques, attributes and metrics
used in predicting the performance of engineering students. Students considered for this study
are from branches of civil engineering, Mechanical, Computer Science and Information
Technology.
1.3. Research Question and Objectives of the survey
Every research work, be it descriptive or analytical, applied or fundamental, needs a set of
research questions to work on. The research question and objective of this study is:
To find out the common methodology adopted in building a predictive model.
To find the important techniques used in predicting the performance of students.
To identify the commonly used attributes for building the prediction models.
To list out the frequently used performance metrics.
1.4. Research Methodology
The methodology followed to conduct the survey is outlined below:
First popular databases like IEEE, Scopus, ACM libraries were searched and research papers
were collected.
Research works older than 2013 and works with accuracy percent less than 65% were
discarded and not considered for the survey.
The research papers selected was studied in detail. In-depth analysis was carried out. Many
important interpretations were made and Research questions were answered.
This paper title, 'A Complete survey on predicting performance ofengineering students' was
prepared and presented.
2. LITERATURE REVIEW
2.1. Performance Prediction using Naive Bayes Classification
Fahad et al. (2018) present a prediction model to predict GPA of students using Naive Bayes
technique and Kmeans clustering. The highest accuracy achieved in this work was 98.8%.
[3]. Srinivas and Ramaraju (2017) build a prediction model using a Naïve Bayes Classifier.
Dataset consisted of 257 academic records. Two feature selection technique namely
3. A Complete Survey on Predicting Performance of Engineering Students
http://www.iaeme.com/IJCIET/index.asp 50 editor@iaeme.com
cfsSubsetEval and GainRatioAttributeEval was used. Results reveal cfsSubsetEval perform
better with 84% accuracy. [4]
Figure 1 Accuracy of Naive Bayes Classification
2.2. Performance Prediction using Artificial Neural Network (ANN)
Bendangnuksung and Prabu (2018) implement Neural Network to predict students’
performance. Cost Function with Accuracy were the metrics used in this research work.. Their
Model attained 83.4 % accuracy [7]. Okbu et.al develop a prediction model based on
Recurrent Neural Network (RNN) . Grades were predicted from log data. Results reveals that
RNN works well with an accuracy of 90% [8].
2.4. Performance Prediction using Decision Tree
Karishma and Swati (2016) use ID3 and C4.5 algorithm to identify students who are probable
to fail. Experiments were done on 200 Engineering student’s dataset. Applying the Decision
Tree on students internal marks, their performance was predicted. The Accuracy of the
classifiers was calculated to be 98.5 % [9]. Sumitha and Vinothkumar (2016) present a
comparative study of classification techniques to predict academic performance. This research
work experiments data of 350 students. Out of the classifiers, it was proved J48 was highly
influential with 97% accuracy[10] .
2.3. Performance Performance Prediction using Support Vector Machine (SVM)
Eashwar and Venkatesan (2017) develop a prediction model using Support Vector Machine to
recognize postgraduate students for carrying out doctoral studies. Data were clustered and
SVM technique was applied. 96.7% accuracy was achieved [13]. Jamuna and Shoba (2017)
compare various machine learning algorithms. Students data from a private institute was
taken. Results unravel the Polynomial Kernel achieved 97.62 % accuracy [14].
98.8
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Fahad Razaque
2018 [3]
Srinivas A
2017 [4]
Ihsan A. Abu
Amra
2017 [5]
Azwa Abdual
Aziz
2014 [6]
AccuracyoftheWork
Author Name and Year of Publication
Accuracy of Naive Bayes Classification Works
Accuracy
4. V. Sathya Durga and J. Thangakumar
http://www.iaeme.com/IJCIET/index.asp 51 editor@iaeme.com
Figure 2 Accuracy of Decision Tree
2.5. Performance Performance Prediction using fuzzy system
Ramjeet, et al. propose a new fuzzy expert system to evaluate the performance of students.
First semester exam results and output values are fuzzified. Application rules were
determined, performance value was defuzzified and final marks were calculated [15]. Shilpa
and Bakal use three nodes fuzzy logic system in their research work. The Triangular
membership function was used in this research work. For Defuzzification Center of gravity is
used. Scores obtained was calculated with the score matrix and accuracy matrix [16].
Table 1 Fuzzy Functions used in Literary works
S.No Author Fuzzification
Rule
Inference
De
fuzzificatin
1.
Ramjeet
Singh
Yadav [19]
Triangular
Membership
function
Mamdani
max–min
inference
Center of
area
2.
Shilpa N
[20]
Triangular
Membership
function
Mamdani
max–min
inference
Ceter of
gravity
2.6. Performance Prediction using optimization technique
Hind Almayan and Waheeda Al Mayyan (2016) use Swarm Optimization method to build
their prediction model. Using Dimensionality reduction technique, feature space was reduced.
PSO is used for feature selection. C4.5 Classifier achieves 93.5% accuracy [17]. Ramanathan
et al. (2016) implements a Neural Network with a Lion – Wolf optimization technique for
weight selection. The model predicts one to eight semester exam performance of college
students. The trained network performances well compared to other prediction models with
RMSE - 2.3 [18].
98.5 97
92 94.41
0
20
40
60
80
100
Karishma B.
Bhegade 2016
[9]
R. Sumitha
2016 [10]
Tribhuvan A.P
2015 [13]
Tripti Mishra
2014 [14]
Accuracyofthework
Author Name and Year of Publication
Accuracy of Decision Tree Works
Accuracy
5. A Complete Survey on Predicting Performance of Engineering Students
http://www.iaeme.com/IJCIET/index.asp 52 editor@iaeme.com
Table 2 Accuracy Comparison of Hybrid ANN
2.9. Attributes used in prediction model
This section lists the commonly used attributes in students performance prediction works.
Total of eight attributes is listed in the following table. The most commonly occurring
attributes are identified and presented in the inferences. section.
Table 3 Attributes used in the Literary Works
S.N
o
Author Model
Accur
acy
RMSE
1.
Md. Fahim
Sikder [17]
ANN 99.8 % 0.1765
2.
Ramanathan L
[18]
Hybrid -
ANN
94 % 2.3
S.No Author 1 2 3 4 5 6 7 8
Naive Byaes
1. A. Srinivas [4]
Previous
marks
High
School
grades
Senior
secondrary
grades
Family
annual
income
Medium of
instruction
General
proficiency
Fathers'
education
Mothers'
education
2.
Ihsan A. Abu
Amra [5]
Previous
marks
Gender
Date of
birth
Students
branch
Location
School
name
Marital status
Father's
job
Neural Network
3. Bendangnuksu
ng [7]
Previous
marks
Nationality Stages
Parents
responsible
Raised
hands
Visited
resource
Parent
answers
Views
updates
4.
Md. Fahim
Sikder [19]
Previous
semester
marks
Class Test
Marks
Class
attendance
Lab
performance
Study time
Family
education
Living area
Extra
curicular
activity
SVM
5.
Huda Al-
Shehri [20]
Previous
marks
Family type
Parents'
status
Parent's job
Extra paid
classed
Extra
curricular
activities
Family
education
support
Internet at
home
6
M. Jamuna
[14]
GPA
Parents
working in
university
Students
discount
Transport Family size
Family
income
Parental
status
Parental
occupation
DECISION TREE
7.
R. Sumitha
[10]
Twelth
total Mark
Medium of
education
Type of
board
Engineering
cut off
Current
CGPA
No of
arrears
Attendance
percentage
8.
Tripti Mishra
[11]
Previous
marks
Loan
Type of
graduation
Year gap Location
Empathy
of student
Decision maker
Leaders
hip
ability
FUZZY
9.
Ramjeet Singh
Yadav [15]
Semester 1
marks
Semester 2
marks
- - - - -
10.
Meenakshi
[21]
Student
attendance
Internal
marks
External
marks - - - -
OPTIMIZATION TECHNIQUES
13.
Ramanathan L
[18]
Individual
details
Environme
ntal factors
Schooling
factors
Family
factors
14.
Hind Almayan
[17]
Three
Period
grades
Number of
absentees
Mother
education
Family
income
Number of
past failure
Alchol
usage
-
6. V. Sathya Durga and J. Thangakumar
http://www.iaeme.com/IJCIET/index.asp 53 editor@iaeme.com
2.10. Performance Measures used in prediction model
Performance of the model is assessed by a set of standard metrics. There are many metrics
used by researchers to evaluate the developed model. This section presents a table indicating
the presence or absence of nine metrics from selected research works. Presence is indicated by
a tick symbol () and absence of that metrics is indicted by a cross symbol ( ).
Table 4 Performance Metrics used in the Literary works
S.N
o
Author
Yea
r
Accurac
y
Confusio
n Matrix
F -
Measur
e
Preciso
n
Recal
l
RMS
E
True
Positiv
e
False
Positiv
e
1.
Fahad Rafiq [3] 2018
2.
Bendangnuksun
g [7]
2018
3
A.Srinivas [4] 2017
4.
Ihsan A. Abu
Amra [5]
2017
5.
F.Okubo [8] 2017
6.
K.B.Eashwar
[13]
2017
7.
M.Jamuna [14] 2017
8.
Huda Al-Shehri
[20]
2017
9.
Omar Augusto
[22]
2017
10.
Hind Almayan
[17]
2016
11.
Ramanathan.L
[18]
2016
12.
Karishma B.
Bhegade [9]
2016
13.
Raj Kumar [14] 2015
14.
Tribhuvan A.P
[11]
2015
15.
Tripti Mishra
[12]
2015
16.
Amirah [25] 2015
17.
Azwa Abdul
Aziz [6] 2014
18.
Fahim Sikder
[19]
2014
20.
Ruhi Kabra [23] 2014
7. A Complete Survey on Predicting Performance of Engineering Students
http://www.iaeme.com/IJCIET/index.asp 54 editor@iaeme.com
3. INFERENCES FROM THE LITERATURE
In this section, answers to the research question framed in the introduction part can be found.
These questions are answered after a thorough analysis of the literature.
Question 1: What is the common methodology used to build student academic
prediction model.
The common methodology observed from literature for predicting students’ academic
performance is as follows.
Collect data from Educational Institutes. Pre-process the data and form the data set. Split the
data into a training set and testing set.
Select an appropriate technique for prediction. Using the training set of data, build the model.
With the built model, predict result with test data. Measure the accuracy of classifiers using
standard metrics.
Question 2: What are the common techniques used for academic performance
prediction of students.
It can be inferred from the survey, that machine learning techniques are widely used for
prediction. Their simple nature and wide availability of development tools make these
techniques a preferred one by the researchers.
Question 3: What are the commonly used attributes in predicting the
performance of students.
Almost all research works use the same attributes like personal details, academic details and
details from Socio-Economic factors. It was noted that the selection of the right subsets of
attributes increased the accuracy of the prediction works radically.
Question 4: What are the common metrics used in evaluating the prediction
model.
Accuracy is the most preferred performance metrics by all researcher. Next to Accuracy,
widely used metrics by researchers were RMSE, Precision and Recall.
4. DISCUSSIONS
We wrap up by discussing briefly on how to build a better prediction model after an elaborate
literature review. To build a prediction model the following steps can be followed.
Steps for building a predictive model
Collect the required data and preprocess it.
Select standard Machine Learning techniques instead of resorting to hybrid approaches.
Attributes mentioned in the attribute matrix can be used to build the model.
Split the data into training and testing set. Built the model with the training set and predict
grades with the test data.
The model can be evaluated with metrics like Accuracy, RMSE, Precision and Recall.
8. V. Sathya Durga and J. Thangakumar
http://www.iaeme.com/IJCIET/index.asp 55 editor@iaeme.com
5. CONCLUSIONS
This survey intends to compile research works on students performance prediction. The result
of the survey was elaborately presented. To Make Every Man a Success and No Man a Failure
[24] is the vision of the K. C. G. Verghese, Founder, Hindustan Institute of Technology and
Science. If every educational institute implements predictive models to assess their students
regularly, definitely we can make every student success and no student a failure.
REFERENCES
[1] Bernard Marr, How Much Data Do We Create Every Day? The Mind-Blowing Stats
Everyone Should Read, 2018.
https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-
every-day-the-mind-blowing-stats-everyone-should-read/#692eade360ba
[2] Bruce Goldman, King of the mountain - Digging data for a healthier world, 2012.
http://sm.stanford.edu/archive/stanmed/2012summer/article3.html
[3] Fahad Razaque., Nareena Soomro., Shoaib Ahmed Shaikh., Safeeullah Soomro., Javed
Ahmed Samo., Natesh Kumar. and Huma Dharejo. Using Naïve Bayes Algorithm to
Sudents' Bachelor Academic Performances Analysis. Proceedings 4th IEEE International
Conference on Engineering Technologies and Applied Sciences, Salmabad, 2017, pp. 1-5.
[4] Srinivas,A and Ramaraju,V. Detection of Failures by Analysis of Student Academic
Performance using Naïve Bayes Classifier .International Journal of Computer &
Mathematical Sciences, 7(3), 2017, pp. 277-283.
[5] Ihsan Abu Amra,A. and Ashraf Maghari, Y.A. Students Performance Prediction Using
KNN and Naïve Bayesian. Proceedings 8th International Conference on Information
Technology.Amman, 2017, pp. 412-422.
[6] Azwa Abdul Aziz, Nur Hafieza Ismail, Fadhilah Ahmad and Hasni Hassan. A Framework
for Students’ Academic Performance Analysis using Naïve Bayes Classifier. Jurnal
Teknologi, 2013, 80(5), pp. 13-19.
[7] Bendangnuksung and Prabu, P. Students' Performance Prediction Using Deep Neural
Network. International Journal of Applied Engineering Research, 13(2), 2018, pp. 1171-
1176.
[8] Okubo.,F, Yamashita.,T, Shimada.,A, and Ogata.,A. A Neural Network Approach for
Students' Performance Prediction. Proceedings Seventh International Learning Analytics
& Knowledge Conference, Canada, 2017, pp. 598-599.
[9] Karishma Bhegade, B., and Swati Shinde,V. Student Performance Prediction System with
Educational Data Mining. International Journal of Computer Applications, 146 (5), 2016,
pp. 32-35.
[10] Tribhuvan,A.P., Tribhuvan ,P.P., and Gade, J.G. 2015. Applying Naive Bayesian
Classifier For Predicting Performance Of A Student Using Weka. 7(1), 2015, pp. 239-242.
[11] Eashwar,K.B, and Venkatesan, R, Student Performance Prediction Using Svm.
International Journal of Mechanical Engineering and Technology (IJMET). 8(11), 2017,
pp .649-662.
9. A Complete Survey on Predicting Performance of Engineering Students
http://www.iaeme.com/IJCIET/index.asp 56 editor@iaeme.com
[12] Jamuna,M., and Shoba,S.A. Educational Data Mining & Students Performance Prediction
Using Svm Techniques. 2017.International Research Journal of Engineering and
Technology. 4(8), 2017,pp. 1248-1254.
[13] Tribhuvan, A.P., Tribhuvan, P.P., and Gade J.G. Applying Naive Bayesian Classifier For
Predicting Performance Of A Student Using Weka. 7(1),2015, pp. 239-242.
[14] Tripti Mishra, Dharminder Kumar and Sangeeta Gupta. Mining Students’ Data for
Performance Prediction. Proceedings of Fourth International Conference on Advanced
Computing & Communication Technologies, Washington, 2014, pp. 255-262.
[15] Ramjeet Singh Yadav, Soni,A.K and Saurabh Pal,A.M. Study of Academic Performance
Evaluation Using Fuzzy Logic Techniques. In Proceedings of the International
Conference on Computing for Sustainable Global Development NewDelhi,2014, pp. 48-
53.
[16] Shilpa Ingoley,N., and Bakal, J.W. Students’ Performance Evaluation Using Fuzzy
Logic. Proceedings nirma university international conference on engineering. Nuicone
2012, pp.1-3.
[17] Hind Almayan and Waheeda Al Mayyan. Improving Accuracy of Students' Final Grade
Prediction Model Using PSO . Proceedings 6th International Conference on Information
Communication and Management,Hatfield,2016, pp. 35-39.
[18] Ramanathan,L., Angelina Geetha, Khalid and Swarnalatha. Student Performance
Prediction Model Based on Lion-Wolf Neural Network. International Journal of
Intelligent Engineering & System, 10(1), 2016, pp. 114-123.
[19] Fahim Sikder,M.D., Jamal Uddin,M.d. and Sajal Halder. Predicting Students Yearly
Performance using Neural Network: A Case Study of BSMRSTU. Proceedings 5th
International Conference on Informatics, Electronics and Vision (ICIEV).2016.pp.524-
529
[20] Huda Al-Shehri, Amani Al-Qarni, Leena Al-Saati and Arwa Batoaq. Student Performance
Prediction Using SupportVector Machine and K-Nearest Neighbor. Proceedings of IEEE
30th Canadian Conference on Electrical and Computer Engineering. 2017. pp.1-4.
[21] Meenakshi and Pankaj. Application of Fuzzy Logic for Evaluation of Academic
Performance of Students of Computer Application Course, 3(10), pp.260-267.
[22] Omar Augusto Echegaray-Calderon and Dennis Barrios-Aranibar. Optimal selection of
factors using Genetic Algorithms and Neural Networks for the prediction of students’
academic performance. 2015 Latin America Congress on Computational Intelligence (LA-
CCI).pp.1-6.
[23] Ruhi Kabra,R., and Bichkar,R,S. 2017. Students’ performance prediction using genetic
algorithm. International Journal of Mechanical Engineering and Technology, 8(11) , pp.
649–662.
[24] Tribute to Our Founder, https://hindustanuniv.ac.in/founder.php
[25] Amirah Mohamed Shahiria., Wahidah Husaina, and Nur’aini Abdul Rashida. A Review
on Predicting Student’s Performance using Data Mining Techniques. Proceedings The
Third Information Systems International Conference, 2015.Malaysia. pp. 412-422.