Search Results - (( based segmentation based algorithm ) OR ( parameter optimization method algorithm ))
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli
Published 2020“…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…Among others, multilevel thresholding is a robust and most widely adopted image segmentation approach. To find the optimal multilevel threshold values, various heuristic and meta-heuristic algorithms have been applied to segment COVID-19 medical images. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…Among others, multilevel thresholding is a robust and most widely adopted image segmentation approach. To find the optimal multilevel threshold values, various heuristic and meta-heuristic algorithms have been applied to segment COVID-19 medical images. …”
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4
Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems
Published 2024“…In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. …”
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5
Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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6
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The optimal scale, shape and compactness parameters of multi-resolution segmentation have been determined and the detection accuracy was evaluated based on receiver operating characteristics (ROC). …”
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7
Model-based hybrid variational level set method applied to object detection in grey scale images
Published 2024“…To tackle the persistent challenge of segmenting grayscale images with both uneven characteristics and high noise levels, a hybrid level-set algorithm based on kernel metrics is introduced. …”
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8
Color image enhancement of acute leukemia cells in blood microscopic image for leukemia detection sample
Published 2020“…The segmentation algorithm uses saturation S-component based on Hue, Saturation, Intensity (HSI) color model. …”
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Conference or Workshop Item -
9
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…In this task, two neural network algorithms, Recurrent Neural Networks (RNN) and Multi-Layer Perceptron Neural Networks (MLP-NN) were used and the hyper-parameters of the network architecture was optimized based on a systematic grid search. …”
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10
A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis
Published 2025“…To address this, we propose an efficient, modified radiomics feature processing method that integrates an optimal aerial perspective (OAP) parameter-based intensity dark channel prior (IDCP) with a 50-layer residual deep neural network (ResNet50 DNN) for autolesion segmentation (ALS-IOAP-DNN). …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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13
A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks
Published 2022“…A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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14
Efficient management of Top-k queries over Uncertain Data Streams with dynamic Sliding Window Model
Published 2024“…Current research on tackling uncertainty revolves around creating specifically tailored algorithms that can operate in the presence of value uncertainty using both a count-based and a time-based approach. …”
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15
Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data
Published 2018“…In addition, the results also showed that the proposed hierarchical classification method could extract geometric road elements with an average error rate of 6.25% for slope parameter and 6.65% for superelevation parameter, and it is transferable to other regions of similar environments. …”
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16
Image segmentation based on normalised cuts with clustering algorithm
Published 2013“…With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. …”
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17
An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation
Published 2016“…Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. …”
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18
An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition
Published 2014“…This paper highlights a novel strategy for online Arabic text recognition using a hybrid Genetic Algorithm (GA) and Harmony Search algorithm (HS). The strategy is divided into two phases: text segmentation using dominant point detection, and recognition-based segmentation using GA and HS. …”
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Customer segmentation on clustering algorithms
Published 2023“…Then, k-means, DBSCAN, and GMM clustering algorithms are applied to segment customers based on their buying behaviour. …”
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Final Year Project / Dissertation / Thesis -
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The framework uses machine learning methods, including classification, clustering, feature selection, and parameter tuning, to improve accuracy and reliability. …”
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Thesis
