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Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
Published 2018“…The results show that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as a single based computational intelligence approach were the best methods in medical applications. …”
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A preliminary lightweight random forest approach-based image classification for plant disease detection
Published 2022“…Random Forest is a special kind of ensemble learning technique and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). …”
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results
Published 2022“…Successful graduation from the Bachelor of Dental Surgery program is assessed through four Professional Exams. Objective: This study aims to predict the academic performance of dental students based on their admission results using Artificial Intelligence. …”
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Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…This study focuses to improve three main drawbacks of both AI techniques i.e. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review
Published 2022“…To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination
Published 2025“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Performance improvement through optimal location and sizing of distributed generation / Zuhaila Mat Yasin
Published 2014“…All simulations in this study were carried out using IEEE 69-bus distribution test system and 141-bus distribution test system. …”
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Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
Published 2022“…The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. …”
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