scholarly journals Adaboost-multilayer perceptron to predict the student’s performance in software engineering

2019 ◽  
Vol 8 (4) ◽  
pp. 1556-1562
Author(s):  
Ahmad Firdaus Zainal Abidin ◽  
Mohd Faaizie Darmawan ◽  
Mohd Zamri Osman ◽  
Shahid Anwar ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Performance Analysis ◽  
Student Performance ◽  
Computer Technology ◽  
Multilayer Perceptron ◽  
Machine Learning Classification ◽  
Student’S Performance ◽  
Percent Accuracy ◽  
Computer Students

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

scholarly journals Student Intervention System using Machine Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2061-2065
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Student Records ◽  
Teaching Methodologies ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Learning Techniques ◽  
Vector Machines

Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.

scholarly journals Crop Disease Recognition using Machine Learning Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 164-166
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
Simple Logistic

Classification is a method of observing the features of a new object and assigning it to a known class. Machine learning classification problem consists of known classes and a vivid training set of pre-categorized examples. The work diagnoses groundnut diseases using outstanding machine learning algorithms namely simple logistic, decision tree, random forest and multilayer perceptron for accurate identification of groundnut diseases. Experiments are conducted with the help of 10-fold cross validation strategy. The results advocate that above mentioned classification algorithms diagnose the groundnut diseases with excellent accuracy level. Simple logistic and multilayer perceptron show outstanding performance than other algorithms and result in 96.37% and 95.80% disease classification accuracy. Random forest and decision tree algorithms provide fair accuracies in less time. These machine learning algorithms can be used in diagnosing other crop diseases also.

Student Performance Analysis and Prediction of Employable Domains Using Machine Learning

2020 ◽  
Author(s):  
Ankit Patel ◽  
Savio Mascarenhas ◽  
Akhil Thomas ◽  
Ditty Varghese
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Student Performance
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