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Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. …”
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Dual-tone multifrequency signal detection using support vector machines
Published 2023Subjects:Conference paper -
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. …”
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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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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…After evaluating the results of these algorithms, a hybrid Artificial Neural Network-based Imperial Competitive Algorithm (ANN-ICA) was presented in the deployment step of the proposed methodology to identify the structural damage of illustrative structures. …”
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Intelligent detection of DTMF tones using a hybrid signal processing technique with support vector machines
Published 2023“…Efficient methods for DTMF signal detection are important for developing telecommunication equipment. This paper presents a hybrid signal processing and artificial intelligence based approach for the detection of Dual-tone Multifrequency (DTMF) tones under the influence of White Gaussian Noise (WGN) and frequency variation. …”
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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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Agarwood oil quality classification using one versus all strategies in multiclass on SVM model / Aqib Fawwaz Mohd Amidon … [et al.]
Published 2021“…Support vector machine (SVM) has been chosen as a main model and for the specific function algorithm was multiclass function. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review
Published 2022“…This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learningâ��s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. …”
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Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
Published 2024thesis::master thesis -
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection
Published 2024“…Spam detection is a critical cybersecurity and information management task with significant implications for security decision-making processes. …”
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Apply and optimize machine learning algorithms for estimating battery health
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Final Year Project / Dissertation / Thesis
