Journal: Heliyon

Article Title: The impact of engineering students' performance in the first three years on their graduation result using educational data mining

doi: 10.1016/j.heliyon.2019.e01250

Figure Lengend Snippet: Confusion matrix for the Naïve Bayes predictor.

Article Snippet: Six (6) main data mining algorithms: the Probabilistic Neural Network (PNN) based on the DDA (Dynamic Decay Adjustment), the Random Forest Predictor, the Decision Tree Predictor, the Naïve Bayes Predictor, the Tree Ensemble Predictor, and the Logistic Regression Predictor were applied in the KNIME model for predicting the class of grade of the final CGPA of the students at graduation.

Techniques:

Journal: Heliyon

Article Title: The impact of engineering students' performance in the first three years on their graduation result using educational data mining

doi: 10.1016/j.heliyon.2019.e01250

Figure Lengend Snippet: Model performance comparison.

Article Snippet: Six (6) main data mining algorithms: the Probabilistic Neural Network (PNN) based on the DDA (Dynamic Decay Adjustment), the Random Forest Predictor, the Decision Tree Predictor, the Naïve Bayes Predictor, the Tree Ensemble Predictor, and the Logistic Regression Predictor were applied in the KNIME model for predicting the class of grade of the final CGPA of the students at graduation.

Techniques: Comparison

Journal: Heliyon

Article Title: The impact of engineering students' performance in the first three years on their graduation result using educational data mining

doi: 10.1016/j.heliyon.2019.e01250

Figure Lengend Snippet: Prediction confusion of the six data mining predictors.

Article Snippet: Six (6) main data mining algorithms: the Probabilistic Neural Network (PNN) based on the DDA (Dynamic Decay Adjustment), the Random Forest Predictor, the Decision Tree Predictor, the Naïve Bayes Predictor, the Tree Ensemble Predictor, and the Logistic Regression Predictor were applied in the KNIME model for predicting the class of grade of the final CGPA of the students at graduation.

Techniques:

Journal: International Journal of Molecular Sciences

Article Title: Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

doi: 10.3390/ijms20143572

Figure Lengend Snippet: Performance of 60 multiple QSAR models built based on Naïve Bayes ( A ) and the support vector machine ( B ) using different fingerprints determined by test set validation.

Article Snippet: The Naïve Bayes classification models were built using KNIME (version 3.4.2).

Techniques: Plasmid Preparation, Biomarker Discovery

Journal: BMC Nutrition

Article Title: Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

doi: 10.1186/s40795-023-00808-8

Figure Lengend Snippet: The performance metrics of ML-based algorithms with their confidence interval for predicting malnutrition of women in Bangladesh using BDHS, 2017-18 data

Article Snippet: The possible risk factors were obtained by Chi-square and then used six machine learning approaches Naïve Bayes (NB), Classification and Regression Tree (CART), C5.0 Classification (C5.0), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting Machine (GBM) to predict malnourishment among the women.

Techniques:

Journal: BMC Nutrition

Article Title: Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

doi: 10.1186/s40795-023-00808-8

Figure Lengend Snippet: Compare the performance of machine learning algorithms across six protocols for malnutrition of women

Article Snippet: The possible risk factors were obtained by Chi-square and then used six machine learning approaches Naïve Bayes (NB), Classification and Regression Tree (CART), C5.0 Classification (C5.0), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting Machine (GBM) to predict malnourishment among the women.

Techniques: