Search Results - (( solution detection method algorithm ) OR ( using vectorization based algorithm ))
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Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine
Published 2023“…In this dissertation, a new vegetation encroachment detection method was proposed by studying the feasibility of using the visible-light band of highresolution satellite images using the RetinaNet deep learning model and Support Vector Machine algorithm (SVM). …”
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. …”
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Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif
Published 2024“…This approach integrates a conditional variational autoencoder (CVAE) to effectively balance the dataset and a stack predictor (SPFHD) that utilizes tree-based ensemble learning algorithms. The base models' predictions are integrated using a support vector machine, significantly enhancing detection accuracy. …”
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Fast shot boundary detection based on separable moments and support vector machine
Published 2021“…Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. …”
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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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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. …”
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Classification of microcalcification in mammogram images using Enhanced Support Vector Machine (ESVM) / Muhammad Akmal Firdaus... [et al.]
Published 2019“…Support Vector Machine (SVM) is a supervised machine learning algorithm with the ability to build a classification model from a labeled dataset. …”
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Data stream clustering by divide and conquer approach based on vector model
Published 2016“…The continuous effort on data stream clustering method has one common goal which is to achieve an accurate clustering algorithm. …”
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Subspace Techniques for Brain Signal Enhancement
Published 2009“…Next, the validity and the effectiveness of the algorithms to detect the P100's (used in objective assessment of visual pathways) are evaluated using real patient data collected from a hospital. …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection
Published 2024“…The results affirm the effectiveness of the proposed methodology, offering a robust and interpretable model for spam detection. This study is a significant advancement in the field, providing a comprehensive and reliable solution to the spam detection problem.…”
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Stage of maturity banana fruit classification using image processing / Nadia Kasim ... [et al.]
Published 2019“…This study attempted to propose a system that uses image processing to detect the maturity stage of banana based on its color and size using Support Vector Machine (SVM) learning algorithm. …”
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Spear- phishing attack detection using artificial intelligence
Published 2024“…The goal is to mitigate phishing and spam threats by using advanced algorithms to detect malicious URLs and classify messages effectively. …”
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Final Year Project / Dissertation / Thesis -
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Optimisation of support vector machine hyperparameters using enhanced artificial bee colony variant to diagnose breast cancer
Published 2023“…Machine learning offers a solution to these challenges, with support vector machines (SVM) being a popular choice for breast cancer diagnosis given its strength in binary classification, which suited well with the dataset used in this thesis. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System
Published 2006“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Detection and classification of moving objects for an automated surveillance system
Published 2006“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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A fuzzy approach for early human action detection / Ekta Vats
Published 2016“…Most existing methods deal with the detection of an action after its completion. …”
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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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Detection and classification of moving objects for an automated surveillance system
Published 2006“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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