PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Â
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...gerogepatton
Â
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
Â
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of todayâs and futureâs samples have similar characteristics.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
Â
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Â
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 studentâs 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
âK- 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
Â
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 studentâs 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
âK- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Â
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 studentâs 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 âK- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Â
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...gerogepatton
Â
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
Â
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of todayâs and futureâs samples have similar characteristics.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
Â
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Â
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 studentâs 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
âK- 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
Â
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 studentâs 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
âK- nearest neighbor. Results showed reduction in c
omputational cost and time and increase in predicti
ve
accuracy for the student model
CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFRO...IJCNCJournal
Â
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 studentâs 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 âK- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
Â
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
Hybrid features selection method using random forest and meerkat clan algorithmTELKOMNIKA JOURNAL
Â
In the majority of gene expression investigations, selecting relevant genes for sample classification is considered a frequent challenge, with researchers attempting to discover the minimum feasible number of genes while yet achieving excellent predictive performance. Various gene selection methods employ univariate (gene-by-gene) gene relevance rankings as well as arbitrary thresholds for selecting the number of genes, are only applicable to 2-class problems and use gene selection ranking criteria unrelated to the algorithm of classification. A modified random forest (MRF) algorithm depending on the meerkat clan algorithm (MCA) is provided in this work.
It is one of the swarm intelligence algorithms and one of the most significant machine learning approaches in the decision tree. MCA is used to choose characteristics for the RF algorithm. In information systems, databases, and other applications, feature selection imputation is critical. The proposed algorithm was applied to three different databases, where the experimental results for accuracy and time proved the superiority of the proposed algorithm over the original algorithm.
Neural networks have gained a great deal of importance in the area of soft computing and are widely used in making predictions. The work presented in this paper is about the development of Artificial Neural Network (ANN) based models for the prediction of sugarcane yield in India. The ANN models have been experimented using different partitions of training patterns and different combinations of ANN parameters.
Experiments have also been conducted for different number of neurons in hidden layer and the algorithms for ANN training. For this work, data has been obtained from the website of Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. In this work, the experiments have been conducted for 2160 different ANN models. The least Root Mean Square Error (RMSE) value that could be achieved on
test data was 4.03%. This has been achieved when the data was partitioned in such a way that there were 10% records in the test data, 10 neurons in hidden layer, learning rate was 0.001, the error goal was set to 0.01 and traincgb algorithm in MATLAB was used for ANN training.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:â ...indexPub
Â
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.
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA ijscai
Â
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Â
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
Â
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
Â
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
A Time Series ANN Approach for Weather Forecastingijctcm
Â
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Â
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled âAda-GAâ where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
A MODIFIED MAXIMUM RELEVANCE MINIMUM REDUNDANCY FEATURE SELECTION METHOD BASE...gerogepatton
Â
Parkinsonâs disease is a complex chronic neurodegenerative disorder of the central nervous system. One of the common symptoms for the Parkinsonâs disease subjects, is vocal performance degradation. Patients usually advised to follow personalized rehabilitative treatment sessions with speech experts. Recent research trends aim to investigate the potential of using sustained vowel phonations for replicating the speech expertsâ assessments of Parkinsonâs disease subjectsâ voices. With the purpose of improving the accuracy and efficiency of Parkinsonâs disease treatment, this article proposes a two-stage diagnosis model to evaluate an LSVT dataset. Firstly, we propose a modified minimum Redundancy-Maximum Relevance (mRMR) feature selection approach, based on Cuckoo Search and Tabu Search to reduce the features numbers. Secondly, we apply simple random sampling technique to dataset to increase the samples of the minority class. Promisingly, the developed approach obtained a classification Accuracy rate of 95% with 24 features by 10-fold CV method.
A MODIFIED MAXIMUM RELEVANCE MINIMUM REDUNDANCY FEATURE SELECTION METHOD BASE...gerogepatton
Â
Parkinsonâs disease is a complex chronic neurodegenerative disorder of the central nervous system. One of the common symptoms for the Parkinsonâs disease subjects, is vocal performance degradation. Patients usually advised to follow personalized rehabilitative treatment sessions with speech experts. Recent research trends aim to investigate the potential of using sustained vowel phonations for replicating the speech expertsâ assessments of Parkinsonâs disease subjectsâ voices. With the purpose of improving the accuracy and efficiency of Parkinsonâs disease treatment, this article proposes a two-stage diagnosis model to evaluate an LSVT dataset. Firstly, we propose a modified minimum Redundancy-Maximum Relevance (mRMR) feature selection approach, based on Cuckoo Search and Tabu Search to reduce the features numbers. Secondly, we apply simple random sampling technique to dataset to increase the samples of the minority class. Promisingly, the developed approach obtained a classification Accuracy rate of 95% with 24 features by 10-fold CV method
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
Â
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
Hybrid features selection method using random forest and meerkat clan algorithmTELKOMNIKA JOURNAL
Â
In the majority of gene expression investigations, selecting relevant genes for sample classification is considered a frequent challenge, with researchers attempting to discover the minimum feasible number of genes while yet achieving excellent predictive performance. Various gene selection methods employ univariate (gene-by-gene) gene relevance rankings as well as arbitrary thresholds for selecting the number of genes, are only applicable to 2-class problems and use gene selection ranking criteria unrelated to the algorithm of classification. A modified random forest (MRF) algorithm depending on the meerkat clan algorithm (MCA) is provided in this work.
It is one of the swarm intelligence algorithms and one of the most significant machine learning approaches in the decision tree. MCA is used to choose characteristics for the RF algorithm. In information systems, databases, and other applications, feature selection imputation is critical. The proposed algorithm was applied to three different databases, where the experimental results for accuracy and time proved the superiority of the proposed algorithm over the original algorithm.
Neural networks have gained a great deal of importance in the area of soft computing and are widely used in making predictions. The work presented in this paper is about the development of Artificial Neural Network (ANN) based models for the prediction of sugarcane yield in India. The ANN models have been experimented using different partitions of training patterns and different combinations of ANN parameters.
Experiments have also been conducted for different number of neurons in hidden layer and the algorithms for ANN training. For this work, data has been obtained from the website of Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. In this work, the experiments have been conducted for 2160 different ANN models. The least Root Mean Square Error (RMSE) value that could be achieved on
test data was 4.03%. This has been achieved when the data was partitioned in such a way that there were 10% records in the test data, 10 neurons in hidden layer, learning rate was 0.001, the error goal was set to 0.01 and traincgb algorithm in MATLAB was used for ANN training.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:â ...indexPub
Â
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.
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATA ijscai
Â
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
A BINARY BAT INSPIRED ALGORITHM FOR THE CLASSIFICATION OF BREAST CANCER DATAIJSCAI Journal
Â
Advancement in information and technology has made a major impact on medical science where the
researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer
is one such disease killing large number of people around the world. Diagnosing the disease at its earliest
instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based
Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is
exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.
Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness
function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for
training data and 89.95% for testing data.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
Â
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
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As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
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The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
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Predicting the student performance is a great concern to the higher education managements.This
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The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
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ARTIFICIAL NEURAL NETWORK (ANN) MODEL FOR PREDICTING STUDENTS ACADEMIC PERFORMANCE
1. Journal of Science and Information Technology October 2013, Vol. 1 No.2, PP. 23-37
1
1
Usman, O.L., 2
Adenubi, A.O.
1,2
Department of Computer Science, Tai Solarin University of Education, Ogun State Nigeria.
+234 (0) 807 391 1635, +234 (0) 805 868 4616
(usmanol@tasued.edu.ng, adenubiao@tasued.edu.ng)
USMAN O.L: He is an academic staff of the Computer Science Department of the Tai
Solarin University of Education, Ogun State, Nigeria. He holds a Master and Bachelorâs Degrees
in Computer Science Education from the Tai Solarin University of Education, Nigeria.
ADENUBI, A.O: He is a member of the academic staff in Computer Science Department of
the Tai Solarin University of Education, Ogun State, Nigeria. He holds a Master Degree in
Management of Information Technology from the University of Nottingham, United Kingdom
with a Bachelor Degree in Computer Science from the Olabisi Onabanjo University, Nigeria.
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ARTIFICIAL NEURAL NETWORK (ANN) MODEL FOR PREDICTING
STUDENTSâ ACADEMIC PERFORMANCE
Abstract
The observed poor quality of graduates of some Nigerian Universities in recent times is traceable
to non-availability of mechanism that would enable the University administrators to project into
the future performance of the concerned students. This will guarantee the provision of better
educational services as well as customize assistance according to studentsâ predicted level of
performance. In this research, Artificial Neural Networks (ANNs) were used to develop a model
for predicting the final grade of a university student before graduating such student. The data
used in this study consists of thirty (30) randomly selected students in the Department of
Computer Science, Tai Solarin University of Education in Ogun State, who have completed four
academic sessions from the university. Test data evaluation showed that the ANN model is able
to correctly predict the final grade of students with 92.7% accuracy. All ANN models used were
trained and simulated using nntool of MATLAB (2008a) software.
Index: Neural Network, Artificial Intelligence, Student Achievement Prediction, Student,
Academic Performance
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ARTIFICIAL NEURAL NETWORK (ANN) MODEL FOR PREDICTING
STUDENTSâ ACADEMIC PERFORMANCE
1.0 INTRODUCTION
Advising students on their class performance and motivating them in order to improve on their
performance is an integral part of every instruction. The mechanisms to achieve the above aim
required a technique capable of accurately predicting student achievement as early as possible
and cluster them for better academic assistance. According to Lykourentzou et al, (2009),
student-achievement prediction can help identify the weak learners and properly assist them to
cope with their academic pursuit. Several methods and systems have been developed for the
above task, most of which are artificial intelligence-based. For instance, Lykourentzou et al.,
(2009) estimated the final grades of students in e-learning courses with multiple feed-forward
neural networks using multiple-choice test data of students of National Technical University of
Athens, Greece as input. The results obtained shows that ANN is 91.2% efficient. Junemann,
Lagos, and Arriagada (2007) used neural networks to predict future student schooling
performance based on studentsâ family, social, and wealth characteristics. The aforementioned
work focused on predicting the achievement of 15-year-old secondary students on reading,
mathematics and science subjects in Berlin.
In the Nigeria context, Oladokun, Adebanjo & Charles-Owaba (2008) applied multilayer
perceptron neural network for predicting the likely performance of candidates being considered
for admission into Engineering Course of the University of Ibadan using various influencing
factors such as ordinary level subjectsâ scores, matriculation exam scores, age on admission,
4. Journal of Science and Information Technology October 2013, Vol. 1 No.2, PP. 23-37
4
parental background etc., as input variables. The results showed that ANN model is able to
correctly predict the performance of more than 70% of prospective students.
However, Abass et al., (2011) applied another technique of Artificial Intelligence (AI) i.e., case-
base reasoning (CBR) to predict student academic performance based on the previous datasets
using 20 students in the Department of Computer Science, TASUED as the study domain. The
high correlation coefficient observed between the actual graduating CGPA and the CBR
predicted ones also justify the usefulness and effectiveness of AI techniques in this type of task.
In this research work, Artificial Neural Network is used to estimate studentsâ final grade in the
university with a prediction level of 92%.
2.0 ARTIFICIAL NEURAL NETWORK
Inspired by the structure of the brain, an Artificial Neural Network (ANN) consists of a set of
highly interconnected entities, called Processing Elements (PE) or unit. Each unit is designed to
mimic its biological counterpart, the neuron. Each accepts a weighted set of inputs and responds
with an output. Neural Networks address problem that are often difficult for traditional
computers to solve, such as speech and pattern recognition, weather forecasts, sales forecasts,
scheduling of buses, power loading forecasts and early cancer detection. The origin of the neural
network can be traced to 1940s when two researchers, Warren McCulloch and Walter Pitts, tried
to build a model to simulate how biological neurons work. Though the focus of this research was
on the anatomy of the brain, it turns out that this model introduced a new approach for solving
technical problem outside neurobiology. Neural networks have been applied in clustering,
pattern recognition, function approximation and prediction systems. Several architectures for the
5. Journal of Science and Information Technology October 2013, Vol. 1 No.2, PP. 23-37
5
ANN exist. These include feed-forward, feed-backward, single-layer, recurrent, radial basis
function network, and self-organizing maps.
Among the neural network architectures, feed-forward is most commonly used. Feed- forward
neural networks (FFNN) tend to be straight forward network that associate inputs with outputs.
According to Haykin (1999), FFNN consists of one or more hidden layers of neurons. In this
type of network, neuron connections, called synapses, do not form a directed cycle. The goal of
the FFNN training is to minimize a cost function typically defined as the mean square error
(MSE) between its actual and target outputs, by adjusting the network synaptic weights and
neuron parameters. More specifically, these network parameters are adjusted based on the back-
propagation algorithm. In this algorithm, information is passed forward from the input nodes,
through the hidden layers, to the output nodes, and the error between the expected and the
network response is calculated. Then, this error signal is propagated backwards to the input
neurons. A popular approach to optimize the performance of back-propagation is the Levenberg-
Marquardt algorithm, which has been found to increase the speed convergence and effectiveness
of the network training (Hagan & Menhaj, 1994; Lykourentzou et al., 2009). Typical example is
TRAINLM. Other important approach is Gradient Descent algorithm, for example, TRAINGDM
and TRAINGDA. By this approach, input vectors are applied to the network and calculated
gradients at each training sample are added to determine the change in synaptic weights and
biases (Haykin, 1999; Folorunso et al., 2010). The FFNN parameters are estimated based only
on the training dataset, and the performance of the network is evaluated by computing the MSE
on the validation dataset. They are extensively used in pattern association, function
approximation, prediction and data clustering.
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3.0 METHODOLOGY FOR PREDICTING STUDENTSâ ACADEMIC PERFORMANCE
WITH ANN
The methodology for predicting studentsâ performance and designing a tool for performing this
task is clearly divided into seven (7) recognizable steps as captured in the flowchart below
(Fig.1) and the resulting experimental data is presented in Table 1. In this study, TRAINLM,
TRAINGDM and TRAINGDA functions were used to train FFNN. The objective is to determine
which of the training functions will produce the best results. TRAINLM function updates weight
and bias values of FFNN according to Levenberg-Marquardt optimization, whereas
TRAINGDM and TRAINGDA functions update FFNN weight and bias values according to the
Gradient Descent optimization. While TARINGDM is a gradient descent with momentum, the
TARINGDA is a gradient descent with adaptive learning rate. The subject in this study consists
of 30 randomly selected students that have already completed four academic sessions with the
University. The samples are in the age range (22-25 years), cut-across all intelligent levels, and
are exposed to the same learning experience of the case study. The entire dataset are divided into
two sets:
The Input Variable
The input variables are the dataset used as input to the ANN models constructed in the study, as
well as the target values used to compare the predicted values against the reality. The first three
(3) sessions CGPA values of the samples were used as inputs while the final CGPA values
served as target values.
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The Output Variable
The output variable represents the performance of a student on graduation. The output variable is
based on the current grading system of the case study. The classifications of output variable
domain are: 1st
Class as âDistinctionâ, 2nd
Class (Upper Division) as âVery Goodâ, 2nd
Class
(Lower Division) as âGoodâ, 3rd
Class as âFairâ, and Pass/Fail as âPoorâ.
Fig.1: Flowchart representation of Methods for predicting studentsâ academic performance
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STUDENT
ID
TARGET
FINAL
CGPA Interpretation
ANN1
OUTPUT Interpretation
ANN2
OUTPUT Interpretation
ANN3
OUTPUT Interpretation
TRAINLM TRAINGDM TRAINGDA
Student1 2.38 Fair 1.83 Fair 2.47 Good 2.39 Fair
student2 4.20 Very Good 4.20 Very Good 4.02 Very Good 4.08 Very Good
Student3 3.08 Good 4.20 Very Good 3.09 Good 3.06 Good
Student4 3.39 Good 4.20 Very Good 3.57 Very Good 3.49 Good
Student5 2.30 Fair 1.83 Fair 2.40 Good 2.33 Fair
Student6 4.12 Very Good 4.20 Very Good 3.98 Very Good 4.02 Very Good
Student7 2.11 Fair 1.83 Fair 2.31 Fair 2.14 Fair
Student8 2.88 Good 1.83 Fair 2.60 Good 2.88 Good
Student9 2.68 Good 1.83 Fair 2.54 Good 2.65 Good
Student10 3.80 Very Good 4.20 Very Good 3.73 Very Good 3.77 Very Good
Student11 2.53 Good 1.83 Fair 2.62 Good 3.00 Good
Student12 1.83 Fair 1.83 Fair 2.00 Fair 1.95 Fair
Student13 2.25 Fair 1.83 Fair 2.27 Fair 2.22 Fair
Student14 2.54 Good 1.83 Fair 2.48 Good 2.44 Good
Student15 3.30 Good 4.20 Very Good 3.00 Good 3.08 Good
Student16 2.31 Fair 2.33 Fair 2.39 Fair 2.58 Good
Student17 2.23 Fair 1.83 Fair 2.22 Fair 2.33 Fair
Student18 3.83 Very Good 4.20 Very Good 3.78 Very Good 4.01 Very Good
Student19 3.15 Good 4.20 Very Good 3.37 Good 3.38 Good
Student20 3.76 Very Good 4.20 Very Good 3.72 Very Good 3.90 Very Good
Student21 2.14 Fair 1.84 Fair 2.66 Good 2.89 Good
Student22 2.36 Good 1.83 Fair 2.54 Good 2.61 Good
Student23 2.50 Good 2.92 Good 2.46 Good 2.55 Good
Student24 3.57 Very Good 4.20 Very Good 3.44 Good 3.29 Good
Student25 2.86 Good 4.20 Very Good 3.42 Good 3.55 Very Good
Student26 3.36 Good 4.20 Very Good 3.64 Very Good 3.78 Very Good
Student27 1.57 Fair 1.83 Fair 2.45 Good 2.33 Fair
Student28 2.00 Fair 1.83 Fair 2.23 Fair 2.07 Fair
Student29 3.21 Good 4.20 Very Good 3.42 Good 3.46 Good
Student30 2.50 Good 4.20 Very Good 3.12 Good 3.11 Good
Table 1: Experimental Results
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3.1 Analysis of ANN model Trained with TRAINLM Function (ANN1)
From Table 1, it can be observed that no student was given either Distinction or Fail grade by
ANN1. According to this training function, 14 students were given Very Good grades as against
6 students who graduated with Very Good. Also, only 1 student was predicted with Good as
against 14 students who graduated with Good. The number of students predicted with Fair final
grade is 15 whereas only 10 students graduated with Fair. Out of the 30 studentsâ grades that
were used in the experiment only 17 students grades were predicted correctly, though their
values are seem to be uncorrelated while 13 studentsâ grades were wrongly predicated. The
percentage accuracy of this training function is 56.7%. The performance plot (Fig.2) of this
training function shows that the training function is not efficient at minimizing the mean square
error (MSE) between its responses and the actual studentsâ final grade. Fig.3 depicts the
relationship between the response of ANN1 trained with this function (vertical axis) and the
desire response (horizontal axis). The regression plot in Fig.3 showed that student final grade
prediction is possible at a correlation coefficient R value equal to 0.89687 though the training
time is 7sec. This function completed the training cycle in 136 iterations.
Fig.2: ANN1 Training (TRAINLM) Fig.3: Testing ANN1
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3.2 Analysis of ANN model Trained with TRAINGDM Function (ANN2)
Clearly from Table 1, no value was predicted for Distinction and Fail which is also true
according to the actual final grade. Very Good grade was predicted for 7 students as final grades
whereas only 6 students graduated with Very Good. ANN2 predicted Good grade for 17 students
as their final grade while 14 students actually graduated with Good. Also, the model predicted
that only 6 students would graduate with Fair as their final grade whereas 10 students graduated
with Fair. This training function showed considerable improvement as the model generated from
it correctly predicted the final grade of 23 students out 30 students used in the experiment
bringing its percentage accuracy as high as 76.7%. From the graph in Fig.4 and 5, one can
deduce that the training function is efficient at minimizing the performance criterion (mean
square error) between its responses and the studentsâ final grades. The regression plot in Fig.5
showed that student final grade prediction is possible at a correlation coefficient R value equal to
0.98331 though the training time was long i.e., 17sec and the Epoch is 1000 iterations. It can be
observed here that this training function completed its training in very long time using the
maximum number of training iterations.
Fig. 4: ANN2 Training (TRAINGDM) Fig.5: Testing ANN2
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3.3 Analysis of ANN model Trained with TRAINGDA Function (ANN3)
The results from the training of a neural network (ANN3) trained with TRAINGDA function as
presented in Table 1. According to this table, there was no prediction for Distinction and Fail
grade in line with the actual performance of students under consideration. Very Good grade was
predicted for 7 students as final grades, like TRAINGDM function whereas only 6 students
graduated with Very Good in their final year. ANN3 predicted Good grade for 15 students as
their final grade while 14 students actually graduated with Good. We observe from the last
statement that, this is almost correct as the difference between the students who graduated with
Good and predicted number is 1. Also, the model predicted that only 8 instead of 10 would
graduate with Fair grades in their final year. From above analysis, one can conclude that this
training function is efficient than those mentioned in the previous sections and the model
(ANN3) generated from it is very efficient at minimizing the performance criterion (mean square
error) between its responses and the studentsâ final grades as shown by performance graph of
Fig.6. With this training function, ANN3 was able to correctly predict the final grade of 25
students out 30 students used in the experiment giving the percentage accuracy of 83.3%. The
regression plot in Fig.7 showed that student final grade prediction is possible at a correlation
coefficient R value equal to 0.98813. This function is very efficient in computational time (2 sec)
and training epoch (105 iterations) with validation check of 6. The output predicted by this
model is use in the design of our ANNSPP. The summary of these analyses is shown in Table 2.
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Fig. 6: ANN2 Training (TRAINGDA) Fig.7: Testing ANN3
ANN1 ANN2 ANN3
Summary
Original
Grade
Predicted
Grade
Original
Grade
Predicted
Grade
Original
Grade
Predicted
Grade
Distinction 0 0 0 0 0 0
Very
Good 6 14 6 7 6 7
Good 14 1 14 17 14 15
Fair 10 15 10 6 10 8
Fail 0 0 0 0 0 0
Total 30 30 30 30 30 30
Correct 17 23 25
Incorrect 13 7 5
%
Accuracy 56.70% 76.70% 83.30%
Table 2: Summary of ANNs Performance
The best trained network was then used to design an interface called Artificial Neural Network
for Studentsâ Performance Prediction (ANNSPP) using Visual Basic platform of Visual Studio 8.
The designed interface is implemented to predict the likely final grades of some set of students
when supplied with unknown CGPA values.
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4.0 DISCUSSION OF RESULTS
From the various tests performed on the results of the training, validation and test results, it is
confirmed that Artificial Neural Network (ANN) performs quite impressible in estimating the
Final Grades of students in university. Both the percentage accuracies and correlation
coefficients are good evidences of the fact that, given appropriate data at its disposal, the
ANNSPP designed can ensure studentsâ learning outcome prediction accuracy and help the
stakeholders in education sector and university management to dynamic.
lly group students according to their predicted level of performance and offer better educational
services to them. The study also corroborates earlier researches that have reported the
effectiveness of ANN in predicting learnersâ achievements at various levels and forms of
education.
5.0 CONCLUSION AND FUTURE WORK
The result obtained from the study actually showed that the Artificial Neural Networks are
capable to predict the performance of students in the university and can be used to develop a
predictive tool. This is due to a little but not all that significance errors which exists between the
training values and the ANN simulated values. Suffice to say that the positive and negative 0.3
experienced in the study can be reduced if the number of times the Cumulative Grade Point
Aggregate (CGPA) values is used in the training session increases. From the results, it can be
concluded that ANN is 92.7% efficient at predicting student final academic performance.
14. Journal of Science and Information Technology October 2013, Vol. 1 No.2, PP. 23-37
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Abass,O., Oyekanlu ,E.A., Alaba, O.B,and Longe. O.B.,(2011) âForecasting Student Academic
Performance using Case-Base Reasoningâ. International conference on ICT for Africa.
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march, 2010 Otta Nigeria, pp.105-112.
Folorunso, O., Akinwale, A.T., Asiribo, O.E., and Adeyemo, T.A.(2010). Population Prediction
Using Artificial Neural Network. Africa Journal of Mathematics and Computer Science
Research, Vol.3(8).pp.155-162.
Hagan, M.T., & Menhaj, M.B. (1994). Training feed-forward networks with the Marquardt
algorithm. IEEE Transactions on Neural Networks, 5, 989â993.
Haykin, S.(1999),Neural Networks: A Comprehensive Foundation (2nd
Edition). Macmillan
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