A New Classification Model Fuzzy-Genetic Algorithm for Detection of learning disability of Dyslexia in Secondary School Students

Background and Objective Learning disability is a neurological disorder. Simply, learning disabilities result from a person's misunderstanding of the brain. Children with learning disabilities are more accurate and intelligent than their peers, but they may have difficulty in reading, writing, pronouncing, concentrating, reasoning, recalling, or organizing information. Reading is the most basic and essential tool of education. Because by acquiring this skill, one will be able to acquire the necessary information in the affairs of life. The advancement of science in the present century is so rapid that reading is one of the most important tools for understanding today's world. One can learn the results of others' research and studies in a short period of time. Reading is a complex process that involves many different components. Learning disability is very common in childhood. The most important disability is reading disorder which is related to reading skills.  Among the skills a student learns in school, reading is especially important. Meanwhile, there are students in higher grades whose reading progress is significantly lower than the standard level compared to their calendar age. This research represents a hybrid scoring model using genetic algorithm and fuzzy set theory to manage uncertainty in diagnosis of reading disability. Methods: For this, fuzzy classification models were applied for diagnosis of the reading disability. In the fuzzy system, the knowledge was extracted from a group of experts who were teachers and specialists. In the proposed model, the knowledge of experts was automatically extracted using the learning process of the Genetic algorithm. A dataset of 260 girl students was collected from the Marefat High school in the Alborz province in the years of 1394 and 1395. The performance of the proposed model was investigated using the ROC curve analysis. Findings: The results show efficiency of the fuzzy classification model was increased to 98.51% after the rule learning with the Genetic algorithm. The proposed fuzzy classifier models uncertainty in the knowledge of expert to improve students’ progress. Conclusion: The results of this algorithm show that compared to several other methods, the fuzzy-genetic combination method performs better than other methods. The results of the performance characteristic curve also prove this. Comparing the efficiency of the system and its analysis using ROC shows that fuzzy classification system is able to identify reading disorders with high reliability. In the future, we can adjust the parameters of the membership functions and also use other meta-algorithms to improve the method. The prevalence of learning disabilities, especially reading in students, indicates the need to use strategies to reduce this disorder to prevent students' academic pathology. Another limitation of this study is the impossibility of examining the relationship between reading disorder and important variables such as parents’ education level and socio-economic status. It is suggested that these limitations be considered in future studies.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

University entry selection framework using rule-based and back-propagation

Processing thousands of applications can be a challenging task, especially when the applicant does not consider the university requirements and their qualification. The selection officer will have to check the program requirements and calculate the merit score of the applicants. This process is based on rules determined by the Ministry of Education and the institution will have to select the qualified applicants among thousands of applications. In recent years, several student selection methods have been proposed using the fuzzy multiple decision making and decision trees. These approaches have produced high accuracy and good detection rates on closed domain university data. However, current selection procedure requires the admission officers to manually evaluate the applications and match the applicants’ qualifications with the program they applied. Because the selection process is tedious and very prone to mistakes, a comprehensive approach to detect and identify qualified applicants for university enrollment is highly desired. In this work, a student selection framework using rule-based and backpropagation neural network is presented. Two processes are involved in this work; the first phase known as pre-processing uses rule-based for checking the university requirements, merit calculation and data conversion to serve as input for the next phase. The second phase uses back-propagation neural network model to evaluate the qualified candidates for admission to particular programs. This means only selected data of the qualified applicants from the first phase will be sent to the next phase for further processing. The dataset consists of 3,790 datasets from Universiti Pendidikan Sultan Idris. The experiments have shown that the proposed method of ruled-based and back-propagation neural network produced better performance, where the framework has successfully been implemented and validated with the average performance of more than 95% accuracy for student selection across all sets of the test data

Improving ANN Classification Accuracy for the Identification of Students with LDs through Evolutionary Computation

[[abstract]]Due to the implicit characteristics of learning disabilities (LD), the identification and diagnosis of students with learning disabilities has long been a difficult issue. Identification of LD usually requires extensive man power and takes a long time. Our previous studies using smaller size samples have shown that ANN classifier can be a good predictor to the identification of students with learning disabilities. In this study, we focus on the genetic algorithm based feature selection method and evolutionary computation based parameters optimization algorithm on a larger size data set to explore the potential limit that ANN can achieve in this specific classification problem. In addition, a different procedure in combining evolutionary algorithms and neural networks is proposed. We use the back propagation method to train the neural networks and derive a good combination of parameters. Evolutionary algorithm is then used to search around the neighborhood of the previously acquired parameters to further improve the classification accuracy. The outcomes show that the procedure is effective, and with appropriate combinations of features and parameters setting, the accuracy of the ANN classifier can reach the 88.2% mark, the best we have achieved so far. The result again indicates that AI techniques do have their roles on the identification of students with learning disabilities