Anfis Approach with Genetic Feature Selection for Prediction of Students' Academic Performance in Distance Education Environment
نویسنده
چکیده
Recently the number of distance education platforms has been increased significantly. These platforms provide isolation between the student and teacher. Thus, there is a need for predicting the students who are possible to fail in a specific course and take the precautions like starting face-to-face on demand lectures for individual cases. Both of artificial intelligence and data mining techniques can be used perfectly for this task. In this paper a neuro-fuzzy inference approach has been used for prediction of academic performance of students in distance education system. The proposed system uses Takagi Sugeno Kang fuzzy inference system for the generation of the fuzzy rules. In addition the genetic algorithm used as feature selection method. The experimental results have shown that the proposed system in this paper can over perform both of neuro-fuzzy and conventional neural network approaches. Keywords— ANFIS, Genetic algorithms, Neuro Fuzzy systems
منابع مشابه
Prediction of Student's Academic Performance Based on Adaptive Neuro-Fuzzy Inference
Prediction of student’s performance is potentially important for educational institutions to assist the students in improving their academic performance, and deliver high quality education. Developing an accurate student’s performance prediction model is challenging task. This paper employs the Adaptive NeuroFuzzy Inference system (ANFIS) for student academic performance prediction to help stud...
متن کاملPrediction of the Academic Buoyancy and Academic Performance of Health Students at Semnan University of Medical Sciences Based on Their Perception of the Learning Environment
Introduction and purpose: Students’ perceptions of the learning environment play a vital role in the process of education, buoyancy, and academic performance. This study aimed to investigate the capability of explaining the academic buoyancy and academic performance of health students of Semnan University of Medical Sciences, Semnan, Iran, based on their perception of learning environment. Met...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کاملA new Feature Selection Algorithm using Modified PSO for an Anaerobic Wastewater Treatment System
Wastewater treatment is necessary to preserve the environment and living organisms. COD (Chemical Oxygen Demand) measures the amount of oxygen consumed by the water in the decomposition of organic matter and oxidation of inorganic matter and chemicals. Predicting effluents in the water is a time consuming process. The proposed method uses anaerobic treatment process to utilize anaerobic bacteri...
متن کاملA New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction
Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...
متن کامل