Search Results - (( evolution optimisation based algorithm ) OR ( problem segmentation learning algorithm ))
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding
Published 2025“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation
Published 2011“…These algorithms have been applied to the problem of image segmentation. …”
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Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design
Published 2014“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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A competitive co-evolutionary approach for the nurse scheduling problem
Published 2026“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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Automated visual defect detection using deep learning
Published 2022“…The main goal of this project is to study and develop various automated defect detection models by utilizing state-of-the-art deep learning segmentation algorithms, including U-Net, Double U-Net, SETR, TransU-Net, TransDAU-Net, CAM and SEAM to perform semantic segmentation in fully supervised and weakly supervised learning manners. …”
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Old Jawi manuscript: Digital recognition / Zaidi Razak
Published 2016“…The literature review in this research looks at works conducted in the past on line and character segmentation as well as recognition systems. The information gathered is useful for the development of a line segmentation algorithm, a new character segmentation algorithm, as well as a recognition algorithm which is based on the use of a unique code and Hamming distance calculation. …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
Published 2022“…The tri-agent reinforcement learning (TA-RL) algorithm has been used by the segment spectrum agent (SSA) in this suggested CSS to make the global decision. …”
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Enhanced Deep Learning Framework for Fine-Grained Segmentation of Fashion and Apparel
Published 2022“…This work proposes a deep learning framework that can learn how to detect and segment clothing objects accurately. …”
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Fostering motivation in TVET students: the role of learner-paced segments and computational thinking in digital video learning
Published 2024“…This study aims to address this gap by examining how learner-paced predefined segments and CT algorithmic thinking can impact TVET students' perceived motivation. …”
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K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data
Published 2022“…In order to process the collected data and segment the customers, an learning algorithm is used which is known as K-Means clustering. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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River segmentation with Atrous Convolution via DeepLabv3 / Nur Adilah Hamid
Published 2020“…The Deep Learning segmentation algorithm DeepLabv3 and DeepLabv3+ are trained and tested for the task of water segmentation and the performances are compared with previous works. …”
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Morphological segmentation and analysis of Bangla text
Published 2016“…This paper deals with lexicon and system development for word segmentation in Bangla language.Our goal in this paper is to develop a morphological segmentation algorithm that can work well for Bangla and to address the problem of unsupervised word segmentation across different languages.From a hand-corrected Bangla corpus, 5000 popular words were segmented into suffixes, prefixes and roots manually.These were the sample lexicon used as seed for next step. …”
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Hybridization of SLIC and extra tree for object based image analysis in extracting shoreline from medium resolution satellite images
Published 2018“…The performance of the segmentation algorithms and machine learning classifiers were assessed in terms of segmentation time and overall accuracy in four experimental settings comprising of three different parameters. …”
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B-spline curve fitting with different parameterization methods
Published 2020“…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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Final Year Project / Dissertation / Thesis
