Search Results - (( parameter detection method algorithm ) OR ( based optimization model algorithm ))
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Fuzzy modelling using butterfly optimization algorithm for phishing detection
Published 2020“…To generate the fuzzy parameter automatically, an optimization method is required and Butterfly Optimization Algorithm (BOA) is one of the good methods to be applied. …”
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A comparative analysis of metaheuristic algorithms in fuzzy modelling for phishing attack detection
Published 2021“…The optimization method derives from the metaheuristic algorithm. …”
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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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Conference or Workshop Item -
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Fuzzy modelling using firefly algorithm for phishing detection
Published 2019“…To generate the fuzzy parameters automatically, an optimization method is needed. …”
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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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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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Student Project -
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Improved Malware detection model with Apriori Association rule and particle swarm optimization
Published 2019“…In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Thesis -
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…The ADAM optimizer effectively tackles challenges in continuous parameter optimization by dynamically updating the model's weights and biases, adapting the learning rate for each parameter based on accumulated historical gradient information to achieve more efficient minimization of the loss function during training. …”
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A fast learning network with improved particle swarm optimization for intrusion detection system
Published 2019“…However, the internal power parameters (weight and basis) of FLN are initialized at random, causing the algorithm to be unstable. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
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Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO)
Published 2024“…Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications
Published 2025“…On the other hand, network compression and acceleration techniques such as pruning and quantization also been the focus of the studies for light-weight algorithms in embedded system. While in our studies, we primarily focusing on the deployment and fine-tuning of deep learning model which is YOLOv5 for PCB defects detection. …”
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Final Year Project / Dissertation / Thesis -
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Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks / Wang Jie ... [et al.]
Published 2024“…Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. …”
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Defect recognition method for magnetic leakage detection in oil and gas steel pipes based on improved neural networks
Published 2024“…Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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Model-based hybrid variational level set method applied to object detection in grey scale images
Published 2024“…To address these challenges, the proposed thesis introduces a novel model-based hybrid variational level-set method, termed VKMHLS, specifically tailored for object detection in grayscale images. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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