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61
Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. …”
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62
Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks
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doctoralThesis -
63
A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network
Published 2020“…The feature selection uses Grey Wolf Optimisation and Binary Search algorithms for choosing the best features out of the dataset. …”
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64
Bee colony algorithm for assigning proctors to exams. (c2013)
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masterThesis -
65
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66
Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach
Published 2006“…The search space is too large to be explored by deterministic algorithms. …”
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67
A Tabu Search Algorithm For Maintenance Scheduling Of Generating Units
Published 2020“…The algorithm was developed, implemented and tested on an integrated model for the MS problem. …”
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68
A Tabu Search Algorithm For Maintenance Scheduling Of Generating Units
Published 2020“…The algorithm was developed, implemented and tested on an integrated model for the MS problem. …”
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69
A method for the minimum coloring problem using genetic algorithms
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conferenceObject -
70
A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…A parallel simulator, based on PVM, was implemented for the proposed algorithm on a Linux Cluster. …”
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71
Natural optimization algorithms for the cross-dock door assignment problem
Published 2016“…This paper modifies a classical mathematical model that represents the CDAP and implements an evolutionary metaheuristic SS algorithm and tests it and then compares the results with those of another evolutionary algorithm, i.e., genetic algorithm (GA). …”
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72
Real-Time Implementation of GPS Aided Low Cost Strapdown Inertial Navigation System
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doctoralThesis -
73
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74
Novel Peak Detection Algorithms for Pileup Minimization in Gamma Ray Spectroscopy
Published 2006“…A number of parameter estimation and digital online peak localisation algorithms are being developed, including a pulse classification technique which uses a simple peak search routine based on the smoothed first derivative method, which gave a percentage error of peak amplitude of less than 1%. …”
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75
Correlation Clustering via s-Club Cluster Edge Deletion
Published 2023“…In certain situations, the requirement for clusters to be cliques was deemed excessively stringent, leading to the proposal of alternative relaxed clique models for dense subgraphs, such as s-club. In this work, we implement three approaches to tackle the 2-club clustering via edge deletion: a heuristic approach based on the influence of the edge to resolve maximum conflicts, a parameterized algorithm in which by deleting a maximum of k edges, the graph can be transformed into a 2-club cluster based on a branching algorithm, and the approach in Integer Linear Programming to find the optimized solution in an integer formulation. …”
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masterThesis -
76
A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†
Published 2010“…This paper presents a hardware implementation to solve the graph colouring problem (chromatic number χ(G)) for arbitrary graphs using the Hopfield neural network (HNN) model of computation. …”
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77
Modeling of magnetization curves for computer-aided design
Published 1993“…A simple algorithm is presented for obtaining the coefficients of the sine-series function for modelling the magnetization curves of magnetic materials. …”
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78
Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle
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doctoralThesis -
79
Automatic keyword extraction from a real estate classifieds data set
Published 2011“…We begin with designing data cleansing algorithms to verify different attributes of the real estate classified. …”
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80
XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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