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  1. 1

    Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

    Published 2025
    “…s orthogonal array is used as a variable selection approach in optimizing the predictive model. …”
    Conference paper
  2. 2

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…The combined influence of the genetic algorithm and correlation analysis are used in this technique. …”
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    Article
  3. 3

    Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

    Published 2021
    “…However, the difficulty of correctly forecasting or predicting the weather continues to exist. Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. …”
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    Proceedings
  4. 4

    The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

    Published 2023
    “…In enhancing prediction accuracy, the T-method employed Taguchi�s orthogonal array as a variable selection approach to determine a subset of independent variables that are significant toward the dependent variable or output. …”
    Article
  5. 5

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

    Published 2019
    “…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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    Final Year Project
  6. 6

    Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

    Published 2025
    “…Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? …”
    Article
  7. 7
  8. 8

    Neural network based model predictive control for a steel pickling process by Kittisupakorn, P., Thitiyasook, P., Hussain, Mohd Azlan, Daosud, W.

    Published 2009
    “…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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    Article
  9. 9

    Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms by Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan

    Published 2021
    “…It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.…”
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    Article
  10. 10

    Depression prediction using machine learning: a review by Abdul Rahimapandi, Hanis Diyana, Maskat, Ruhaila, Musa, Ramli, Ardi, Norizah

    Published 2022
    “…The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. …”
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    Article
  11. 11

    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

    Published 2023
    “…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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    Article
  12. 12

    Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour by Jan, Ketil Arnulf, Kai, Rune Larsen, Øyvind, Lund Martinsen, Bong, Chih How

    Published 2014
    “…Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. …”
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    Article
  13. 13

    A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant by Bunyamin M.A., Yap K.S., Aziz N.L.A.A., Tiong S.K., Wong S.Y., Kamal M.F.

    Published 2023
    “…This paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). …”
    Conference paper
  14. 14
  15. 15

    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.

    Published 2024
    “…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. …”
    Article
  16. 16

    Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis by Kartiwi, Mira, Ab Rahman, Jamalludin, Nik Mohamed, Mohamad Haniki, Draman, Samsul, Ab Rahman, Norny Syafinaz

    Published 2017
    “…Results: By using the ID3 algorithm, it is possible to consider the relationship among variables and to identify the most informative variables for predicting the classification of the instance. …”
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    Article
  17. 17

    Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm by Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob

    Published 2022
    “…However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. …”
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    Article
  18. 18
  19. 19

    Automatic control of flotation process using computer vision by Saravani, Ali Jahed

    Published 2015
    “…A froth model correlating the image variables to process variables and a prediction system estimating the metallurgical parameters based on image variables were then developed by using a neural network structure. …”
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    Thesis
  20. 20

    Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea by Sim, Doreen Ying Ying

    Published 2018
    “…The parameters involved in the prediction algorithms developed are based on common OSA risk factors and visual-inspected variables found in the patients’ records in the OSA datasets collected. …”
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    Thesis