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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination
Published 2025“…Furthermore, sensitivity analyses validated SVM's robustness in capturing the nonlinear relationships governing ion removal efficiency. …”
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Feature extraction and supervised learning for volatile organic compounds gas recognition
Published 2023“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. A preliminary dataset was used to predict VOC classification through the application of five supervised machine learning algorithms: k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Artificial Neural Networks (ANN). …”
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Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models
Published 2024“…This research employs the YOLOv8 model, which is a deep learning algorithm, to determine the most effective methods for detecting skin diseases. …”
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A comparative study of supervised machine learning approaches for slope failure production
Published 2023“…The prediction result from testing data was validated based on statistical analysis. The result shows that SVM model has outperformed DT model by giving the prediction accuracy of 97%. ith the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. …”
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Human hearing disorder recognition model using eeg-aep based signal
Published 2022“…This study investigated two conventional machine learning algorithms: support vector machine (SVM) and k-nearest neighbors (KNN), and two deep learning techniques: convolutional neural network (CNN) and improved-VGG16 model. …”
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Geospatial AI-based approach to assess the spatiotemporal suitability of onshore wind-solar farms in Iraq
Published 2023“…In this context, global geospatial data for 13 conditioning factors were collected, and 55,619 inventory samples of wind and solar stations worldwide were prepared to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Integrating Phq-9 Sentiment Analysis and Clinical Interviews for Depression Detection in Medical Students
Published 2026thesis::master thesis -
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
Published 2024“…RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. …”
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Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions
Published 2018“…On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.…”
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Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. …”
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Time series data intelligent clustering algorithm for landslide displacement prediction
Published 2018“…To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. …”
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Published 2018“…In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. …”
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Intrusion Detection Systems, Issues, Challenges, and Needs
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Comparative performance of machine learning algorithms for cryptocurrency forecasting
Published 2018“…Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. …”
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Dual-tone multifrequency signal detection using support vector machines
Published 2023Subjects:Conference paper
