Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Sibyan, Hidayatus; Švajlenka, Jozef; Hermawan, Hermawan; Faqih, Nasyiin; Arrizqi, Annisa Nabila

doi:10.3390/su142315663

Cited by 6 publications

(3 citation statements)

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“…K-nearest neighbor (KNN) [9] is a non-complex algo-rithm that can store all the available data and further classify new cases based on similarity measures. The naïve Bayes classifier (NBC) is a probabilistic classifier model working on the basis of assigning class labels to problem instances, which are represented as features of vector values [10], where the class labels are drawn from a finite set. A decision tree algorithm (DTA) is built using a labeled (training) dataset, and it forms the basis for classifying an unlabeled (testing) dataset for solving problems.…”

Section: Methodsmentioning

confidence: 99%

Can Precision Agriculture Be the Future of Indian Farming?—A Case Study across the South-24 Parganas District of West Bengal, India

Sengupta

2023

The 2nd International Online Conference on Agriculture

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Section: Methodsmentioning

confidence: 99%

Can Precision Agriculture Be the Future of Indian Farming?—A Case Study across the South-24 Parganas District of West Bengal, India

Sengupta

2023

The 2nd International Online Conference on Agriculture

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“…Where { } represent the collection of distinct terms present in at least one of the documents in . The subsequent formula can be used to calculate the probability that a document belongs to class [31].…”

Section: A Naive Bayes Classifier (Nbc)mentioning

confidence: 99%

Predicting Students' Academic Performance Through Machine Learning Classifiers: A Study Employing the Naive Bayes Classifier (NBC)

ZHENG,

2024

IJACSA

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Modern universities must strategically analyze and manage student performance, utilizing knowledge discovery and data mining to extract valuable insights and enhance efficiency. Educational Data Mining (EDM) is a theory-oriented approach in academic settings that integrates computational methods to improve academic performance and faculty management. Machine learning algorithms are essential for knowledge discovery, enabling accurate performance prediction and early student identification, with classification being a widely applied method in predicting student performance based on various traits. Utilizing the Naive Bayes classifier (NBC) model, this research predicts student performance by harnessing the robust capabilities inherent in this classification tool. To bolster both efficiency and accuracy, the model integrates two optimization algorithms, namely Jellyfish Search Optimizer (JSO) and Artificial Rabbits Optimization (ARO). This underscores the research's commitment to employing cutting-edge machine learning and algorithms inspired by nature to achieve heightened precision in predicting student performance through the refinement of decision-making and prediction quality. To classify and predict G1 and G3 grades and evaluate students' performance in this study, a comprehensive analysis of the information pertaining to 395 students has been conducted. The results indicate that in predicting G1, the NBAR model, with an F1_Score of 0.882, performed almost 1.03% better than the NBJS model, which had an F1_Score of 0.873. In G3 prediction, the NBAR model outperformed the NBJS model with F1_Score values of 0.893 and 0.884, respectively.

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“…MLR is a common quantitative analysis method, which is widely used in the field of economics due to its superior performance, and is usually used to explain the relationship between variables affected by Frontiers in Materials frontiersin.org multiple variables (Korkmaz, 2021;Rossi Salamanca-Neto et al, 2021;Sibyan et al, 2022), and its general form is:…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Wang

Zhou

2023

Front. Mater.

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Recycled concrete from construction waste used as road material is a current sustainable approach. To provide feasible suggestions for civil engineers to prepare recycled concrete with high flexural strength (FS) for the road pavement, the present study proposed three hybrid machine learning models by combining support vector machine (SVM), decision tree (DT) and multiple linear regression (MLR) with the firefly algorithm (FA) for the computational optimization, named as SVM-FA, DT-FA, and MLR-FA, respectively. Effective water-cement ratio (WC), aggregate-cement ratio (AC), recycled concrete aggregate replacement ratio (RCA), nominal maximum recycled concrete aggregate size (NMR), nominal maximum normal aggregate size (NMN), bulk density of recycled concrete aggregate (BDR), bulk density of normal aggregate (BDN), water absorption of RCA (WAR) and water absorption of NA (WAN) were employed as the input variables. To determine the predicting results of varying hybrid models, root mean square error (RMSE) and correlation coefficient (R) were used as performance indexes. The results showed that the SVM-FA demonstrated the highest R values and the lowest RMSE values, and the fitting effect of the predicted values and the actual values of the FS of recycled concrete is the best. All the above analysis proving that the SVM optimized by FA hyperparameters has the highest prediction accuracy and SVM-FA can provide engineers a more accurate and convenient tool to evaluate the FS of recycled concrete. The results of sensitivity analysis showed that WC has the most significant influence on the FS of recycled concrete, while RCA has the weakest influence on the FS, which should be noticed when engineers apply recycled concrete to road design in the future.

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Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Cited by 6 publications

References 40 publications

Can Precision Agriculture Be the Future of Indian Farming?—A Case Study across the South-24 Parganas District of West Bengal, India

Can Precision Agriculture Be the Future of Indian Farming?—A Case Study across the South-24 Parganas District of West Bengal, India

Predicting Students' Academic Performance Through Machine Learning Classifiers: A Study Employing the Naive Bayes Classifier (NBC)

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

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