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A novel hybrid methodology for fault diagnosis of wind energy conversion systems
Published 2023“…The proposed technique involved two major steps: feature selection and fault classification. Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. …”
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Investigating the Impact of Skylights and Atrium Configurations on Visual Comfort and Daylight Performance in Dubai Shopping Malls
Published 2025“…Annual simulations are used to assess seasonal variations, while sensitivity analysis identifies key parameters. A genetic algorithm and multi-objective optimisation (MOO) simulations are used to generate optimal configurations, summarised in the form of a Pareto front selection criteria guide the choice of the optimum solution, which is then applied and analysed in a case study. …”
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An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization
Published 2022“…<p dir="ltr">The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. …”
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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet
Published 2025“…To guarantee accuracy and uniformity, the data is preprocessed by addressing missing values and ensuring consistency. A hybrid feature selection technique integrates the Boruta algorithm and SHAP (SHapley Additive exPlanations) values to ensure robust feature selection. …”
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Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test
Published 2019“…Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. …”
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Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE
Published 2018“…We examined different feature selection methods and classification algorithms to find the best prediction model with the highest accuracy. …”
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A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network
Published 2020“…The preprocessing of data includes remove missing value records and remove columns that have a negligible impact. The feature selection uses Grey Wolf Optimisation and Binary Search algorithms for choosing the best features out of the dataset. …”
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VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems
Published 2019“…However, NNGE algorithm tends to produce rules that test a large number of input features. …”
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Adaptive false discovery rate for wavelet denoising of pavement continuous deflection measurements
Published 2016“…The algorithm minimizes the classification error of features in the wavelet transform domain by adaptively selecting the level at which to control the false discovery rate. …”
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The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review
Published 2022“…The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. …”
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Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes
Published 2025“…In the first stage, we performed binary classification, grouping SAH severity into “Good Outcome” (class 0), which includes MRS levels 0, 1, 2, and 3, and “Poor Outcome” (class 1), encompassing levels 4, 5, and 6. Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”
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Artificial Intelligence–Driven Serious Games in Health Care: Scoping Review
Published 2022“…PCs were the most common platform used to play serious games. The most common algorithm used in the included studies was support vector machine. …”
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A Multi-Channel Convolutional Neural Network approach to automate the citation screening process
Published 2021“…The citation screening process aims to identify the relevant primary studies fairly and with high rigor using selection criteria. Through the study selection criteria, reviewers determine whether an article should be included or excluded from the SLR. …”
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Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review
Published 2022“…A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. …”
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Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM)
Published 2024“…Due to the complexity of Arabic poetry such as the excessive use of metaphors, figurative language, unlimited imagination, and the diversity of styles from one poet to another and from one poem to another, we tackle these challenges by careful employment of preprocessing steps, feature engineering and selection. We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. …”
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Machine Learning Model for a Sustainable Drilling Process
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Regression Testing of Database Applications
Published 2002“…In phase 2, further reduction in the regression test cases is performed by using reduction algorithms. We present two such algorithms. The Graph Walk algorithm walks through the control flow graph of database modules and selects a safe set of test cases to retest. …”
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Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review
Published 2023“…The most commonly used algorithm was random forest, followed by support vector machine.…”
