Evaluation of LMT and DNN Algorithms in Software Defect Prediction for Open-Source Software

Alazawi, Sundos Abdulameer; Salam, Mohammed Najim Al

doi:10.1007/978-981-15-7527-3_19

Cited by 1 publication

(1 citation statement)

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“…This factor has been used in previous research where the classification task was applied for calculating correct prediction [68,69]. It analyzes the result with the help of a confusion matrix, calculating all "True Positive (TP)" values identified in the dataset divided by number of "True Positive (TP) and "False Negative (FN)" values extracted during the validation process [70]. The common formula for calculating recall factor is as follows:…”

Section: Recallmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: Recallmentioning

confidence: 99%