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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Prediction of Machine Failure by Using Machine Learning Algorithm by Fakhrurazi, Nur Amalina

“…Then, the data is cluster by using K Means to produce labeled input that will be trained by using Gradient Boosting Machine, a decision tree algorithm to make prediction. …”
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Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations by Kumar M., Elbeltagi A., Pande C.B., Ahmed A.N., Chow M.F., Pham Q.B., Kumari A., Kumar D.

Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”

A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping by Althuwaynee, Omar F., Pradhan, Biswajeet, Park, Hyuck Jin, Lee, Jung Hyun

“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data by Ong, Song Quan, Pradeep Isawasan, Ahmad Mohiddin Mohd Ngesom, Hanipah Shahar, As’malia Md Lasim, Gomesh Nair

“…Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”
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Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques by Kayalvily, Tabianan, Denis, Arputharaj, Mohd Norshahriel, Abd Rani, Sarasvathi, Nahalingham

“…The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data. …”
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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

“…The model had an impressive performance during the training phase, with a R2 of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. …”

Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Robin Yee Hui, Ng, Giap Weng

“…However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. …”
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration by Althuwaynee, Omar F., Pradhan, Biswajeet, Ahmad, Noordin

“…This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…The results indicated that good classification performance depends on these factors. All algorithms showed more stability and accuracy when training size applied is more than 6% by the Equal Sample Rate (ESR) method with six variables. …”
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Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques by Tofiq Y.M., Latif S.D., Ahmed A.N., Kumar P., El-Shafie A.

Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…The sample size was comprised of 55 non-infected trees and 37 infected trees. During the field experiments, oil palm tree samples of non-infected (T0), mild infected (T1), moderate infected (T2), and severe infected (T3) were measured using the FLIR T620 IR infrared thermal imaging camera to obtain the temperature of the oil palm trees. …”
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Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? by Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Yee Hui Robin, Ng, Giap Weng

“…However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. …”
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Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate by Nadimalla, Altamashuddinkhan, Masjuki, Siti Aliyyah, Gubbi, Abdullah, Khan, Anjum, Mokashi, Imran

“…Machine learning is a critical subset of AI that deliberates the development of self-trained algorithms that use previous databases and analysis for result predictions. …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

“…The experiment involved five (5) common algorithms: Linear Regressor, Decision Tree Regressor, Random Forest Regressor, Ridge Regressor and Lasso Regressor. …”
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Recommendation System Model For Decision Making in the E-Commerce Application by Mohammed Mahmudur Rahman

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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