Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework

2021 ◽  
Vol 18 (6) ◽  
pp. 981-992
Author(s):  
Amlan Jyoti Baruah ◽  
Siddhartha Baruah
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Data Augmentation ◽  
Mapreduce Framework ◽  
Neuro Fuzzy ◽  
Fuzzy Network

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

scholarly journals Analyzing the dynamics of emotional scene sequence using recurrent neuro-fuzzy network

2012 ◽  
Vol 7 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Qing Zhang ◽  
Minho Lee
Keyword(s):  
Neuro Fuzzy ◽  
Fuzzy Network

scholarly journals Preference Cognitive Diagnosis for Student Performance Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 219775-219787
Author(s):  
Peichao Jiang ◽  
Xiaodong Wang
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Cognitive Diagnosis

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

scholarly journals Neuro-fuzzy network approach for modeling submicron MOSFETs: application to MOSFET subcircuit simulation

2014 ◽  
Vol 22 ◽  
pp. 573-581
Author(s):  
Amir Hossein ABDOLLAHI NOHOJI ◽  
Fardad FAROKHI ◽  
Majid ZAMANI
Keyword(s):  
Network Approach ◽  
Neuro Fuzzy ◽  
Fuzzy Network
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