Search Results - (( variable prediction using algorithm ) OR ( variable extraction method algorithm ))
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Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
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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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. …”
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Analysis of Traffic Accident Patterns Using Association Rule Mining
Published 2024“…This study also demonstrated the practicality of the apriori algorithm in analyzing extensive datasets to extract actionable insights. …”
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The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers
Published 2024“…Background: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. …”
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Classification Of Cervical Cancer Stage From Pap Smear Tests
Published 2019“…Feature extraction is then used to select the appropriate features that contribute most to the predicted variable from the image. …”
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Final Year Project -
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…The correlation analysis is used for the identification and selection of the most influential input variable vector (IVV). …”
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Thesis -
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Perturbation stochastic model updating of a bolted structure / Mohamad Azam Shah Aziz Shah
Published 2022“…The SMU method based on the improved model was used to predict the variability of the dynamic behaviour of the structure. …”
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Thesis -
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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor
Published 2024“…The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. …”
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Development of noise induced hearing loss prediction model using artificial neural network / Siti Fairus Mohd Zain
Published 2019“…The 24 input layers encompassed 12 risk factors and 12 audiogram variables. It also embedded with 10 hidden layers in the prediction models using Levenberg-Marquardt algorithm as a transfer function from input vectors to the five binary outputs. …”
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Thesis -
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Clinical relevance of VKORC1 (G-1639A and C1173T) and CYP2C9*3 among patients on warfarin
Published 2011“…However, our best model does not account sufficiently for the variability in dose requirements for it to be used in dose prediction for the individual patient. …”
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Classification and prediction analysis for weld bead surface quality using feature extraction and mahalanobis-taguchi system
Published 2025“…The results reveal that while the K-means clustering method outperforms the Variable Bin Width method across several performance metrics, including an accuracy of 86.36% and a high specificity of 94.5%, the method’s recall rate of 50.49% indicates room for improvement in identifying true positives. …”
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…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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Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi
Published 2022“…Besides, Artificial Neural Network (ANN) and Random Forest (RF) algorithm was used to predicted the AGB using different combination of variables. …”
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Thesis -
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. …”
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Conference or Workshop Item -
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network
Published 2023“…Therefore, the main objective of this study is to improve the validity and reliability of the simplified biofilm growth model to optimize the laboratory MEC process of biohydrogen production from sago wastewater substrate using MATLAB. Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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Thesis -
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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…s orthogonal array is used as a variable selection approach in optimizing the predictive model. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The combined influence of the genetic algorithm and correlation analysis are used in this technique. …”
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