Search Results - (( evolution optimisation based algorithm ) OR ( parallel implementation matching algorithm ))
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GPU-based odd and even hybrid string matching algorithm
Published 2016“…String matching is considered as one of the fundamental problems in computer science.Many computer applications provide the string matching utility for their users, and how fast one or more occurrences of a given pattern can be found in a text plays a prominent role in their user satisfaction.Although numerous algorithms and methods are available to solve the string matching problem, the remarkable increase in the amount of data which is produced and stored by modern computational devices demands researchers to find much more efficient ways for dealing with this issue.In this research, the Odd and Even (OE) hybrid string matching algorithm is redesigned to be executed on the Graphics Processing Unit (GPU), which can be utilized to reduce the burden of compute-intensive operations from the Central Processing Unit (CPU).In fact, capabilities of the GPU as a massively parallel processor are employed to enhance the performance of the existing hybrid string matching algorithms.Different types of data are used to evaluate the impact of parallelization and implementation of both algorithms on the GPU. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA
Published 2018“…The proposed implementation shown significant speedups of 11Ã� and 5Ã� compared to the sequential and parallel CPU implementations respectively. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA
Published 2018“…The proposed implementation shown significant speedups of 11Ã� and 5Ã� compared to the sequential and parallel CPU implementations respectively. …”
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…By implementing optimization algorithm in image template matching, it is expected that the computation time can be reduced. …”
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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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Resource Minimization in a Real-time Depth-map Processing System on FPGA
Published 2011“…Depth-map algorithm allows camera system to estimate depth. It is a computational intensive algorithm, but can be implemented with high speed on hardware due to the parallelism property. …”
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A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences
Published 2008“…The genetic similarity algorithm combines semantic similarity measure algorithm with parallel genetic algorithm. …”
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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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Job Matching Mobile Application using Fuzzy Analytic Hierarchy Process (FAHP) / Mohammad Ashraf Jefrizin
Published 2017“…FAHP is implemented in the job matching process. This project’s aim is to assist the job seekers to find ICT jobs that match to their preferences. …”
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Student Project -
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MINIMIZATION OF RESOURCE UTILIZATION FOR A REAL-TIME DEPTH-MAP COMPUTATIONAL MODULE ON FPGA
Published 2011“…However, the issue can also be addressed by implementing hardware architectures without the need to modify the depth-map algorithm. …”
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Enhanced and effective parallel optical flow method for vehicle detection and tracking
Published 2016“…With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Parallel Optical Flow method based on Lucas-Kanade 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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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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Efficient Malware Detection And Response Model Using Enhanced Parallel Deep Learning (EPDL-MDR)
Published 2026“…Upon converting PE files to images, the deep learning pixel-matching algorithm identifies obscured malware features. …”
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Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink: article / Siti Nor Safinaz Ozaimidi
Published 2012“…This paper studies barcode recognition which implements using Simulink MATLAB 7.13. The proposed algorithms and the implementations of image are for EAN-13 barcode by using camera devices. …”
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Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink / Siti Nor Safinaz Ozaimidi
Published 2012“…This paper studies barcode recognition which implements using Simulink MATLAB 7.13. The proposed algorithms and the implementations of image are for EAN-13 barcode by using camera devices. …”
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Thesis -
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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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