Search Results - (( model validation method algorithm ) OR ( using detection method algorithm ))
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Novel algorithms of identifying types of partial discharges using electrical and non-contact methods / Mohammad Shukri Hapeez
Published 2015“…Experimental work was conducted to obtain PD data on both ultrasonic and electrical methods. The validated PD data acquired from ultrasonic method was used to test SPDI and compared with several models of NN. …”
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Thesis -
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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“…However, the challenge of class imbalance and high dimensionality in clinical data significantly impedes the efficacy of Machine Learning (ML) models in this domain. This thesis presents two innovative methods that holistically address these challenges at algorithmic and data levels to enhance heart disease detection. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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A new intelligent multilayer framework for insider threat detection
Published 2021“…The selection procedure has been developed based on the integration of the entropy-VIKOR methods. For the second layer, a hybrid insider threat detection method has been proposed, where the Misuse Insider Threat Detection (MITD) model has been created using the random forest algorithm. …”
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Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
Published 2023“…These conventional methods required expert assistance, reagent and were mostly used for diagnostic purpose. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Moreover, shortage of reliable methods on a new dataset for the intrusion detection system and anomaly detection in terms of classification is an issue. …”
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Neural network algorithm-based fall detection modelling
Published 2020“…However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. …”
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Evaluation of the Transfer Learning Models in Wafer Defects Classification
Published 2022“…In a semiconductor industry, wafer defect detection has becoming ubiquitous. Various machine learning algorithms had been adopted to be the “brain” behind the machine for reliable, fast defect detection. …”
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Conference or Workshop Item -
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Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator
Published 2011“…The data for model training and validation are generated using semi-empirical models developed by the authors. …”
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Detection of DDoS attacks in IoT networks using machine learning algorithms
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Proceeding Paper -
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Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
Published 2022“…In this paper, we present the method and performance to detect rotifer Brachionus plicatilis in 1ml sample automatically using deep learning algorithm YOLOv3. …”
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Proceedings -
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques
Published 2011“…In the past decades, machine learning methods have been successfully used in several intrusion detection methods because of their ability to discover and detect novel attacks. …”
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Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream
Published 2023“…Clustering, known for its versatility in real-time data stream processing and independence from labeled instances, is a suitable method for analyzing evolving data streams. Existing clustering algorithms for outlier detection encounter significant challenges due to insufficient data pre-processing methods and the absence of a suitable data summarization framework for effective data stream clustering. …”
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Sketch-based 3D modeling of symmetric objects from wireframe sketches on paper
Published 2016“…The algorithm is validated by conducting manual ground truth and the results were compared with the closest and well-known methods. …”
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REAL-TIME ILLUMINATION COMPENSATION ON DYNAMIC BACKGROUND FOR CROWD ANALYTIC SURVEILLANCE
Published 2015“…Moreover, the results of our method is been used with other behavior learning algorithms to improve their efficiency.…”
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Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network
Published 2009“…One such method is machine learning, which involves computer algorithm to capture hidden knowledge from data. …”
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Road damage detection for autonomous driving vehicles using YOLOv8 and salp swarm algorithm
Published 2025“…Consequently, this paper proposes a method to improve the detection accuracy of You Only Look Once version 8 (YOLOv8) using Salp Swarm Algorithm (SSA) for hyperparameter optimization, focusing on eight key parameters. …”
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Concrete surface inspection by using unmanned aerial vehicle (UAV) and deep learning algorithms YOLOv7 / Saffa Nasuha Rusdinadi
Published 2024“…These images are then processed using yolov7, a state-of-the-art object detection algorithm, to accurately identify and classify surface cracks. the study involves the collection of a comprehensive dataset of concrete surfaces with varying crack patterns, pre-processed using Roboflow and Opencv tools to enhance crack features. the annotated dataset is utilized to train and validate the yolov7 model, ensuring high precision which is 96.8% and 90.1% recall in crack detection. the performance of the model is evaluated through metrics such as precision, recall, and f1-score, demonstrating its robustness and reliability in detecting both fine and prominent cracks. …”
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Student Project -
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Smart fall detection by enhanced SVM with fuzzy logic membership function
Published 2023“…The attained results validate that our introduced method can effectively learn from features extracted from a multiphase fall model.…”
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