Search Results - parallel selection ((((machine algorithm) OR (mining algorithm))) OR (bees algorithm))
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1
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…This research opens a wide range of future work to be considered, which includes applying the proposed method in other areas such as web mining, text mining or multimedia mining; and extending the proposed approach to work in parallel computing in data mining.…”
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Thesis -
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Random sampling method of large-scale graph data classification
Published 2024“…Since the data blocks in this model are much smaller than the entire data set, it is more efficient to analyze them on a standalone small machine, and multiple data blocks can be analyzed on multiple nodes of the cluster in parallel. …”
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Article -
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Prognosis of early cervical carcinoma using gene expression profiling
Published 2015“…Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. …”
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Proceeding Paper -
4
Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources
Published 2022“…This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. …”
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Parallel algorithms on some numerical techniques using PVM platform on a cluster of workstations
Published 2002“…In this paper, a few parallel algorithms are explained in solving one dimensional heat model problem using Parallel Virtual Machine (PVM). …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…Performing feature subset and tuning support vector machine (SVM) parameter processes in parallel with the aim to increase the classification accuracy is the current research direction in SVM. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Machine learning algorithms have widely been adopted recently to enhance the performance of IDSs. …”
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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ROA-CONS: raccoon optimization job scheduling
Published 2021“…An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. …”
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Image classification using two dimensional wavelet coefficients with parallel computing
Published 2020“…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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Final Year Project / Dissertation / Thesis -
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Parallel processing in Compute Unified Device Architecture (CUDA) for energy saving glass
Published 2014“…In order to develop a complex coating structure, genetic algorithm technique is used in this research. However, genetic algorithm require a fast processing speed in order to cope with the process of creating new chromosome from the population and undergoes the selection, crossover and mutation operation processes. …”
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Three phase induction motor coupled to DC motor in hybrid electric vehicle application
Published 2011“…An advanced electric drive controller for a high power starter-generator subsystem based on a series DC machine is presented. The machine is belt-coupled to a diesel engine in a series-parallel 2×2 HEV. …”
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15
A trade-off criterion for bi-objective problem in solving hybrid flow shop scheduling with energy efficient (EE-HFS) using multi-objective dragonfly algorithm (MODA)
Published 2024“…The optimization result was compared with well-established algorithms, the Pareto Envelope-based Selection Algorithm II (PESA2), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and new algorithms, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) and Multi-Objective Ant Lion Optimizer (MOALO). …”
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Conference or Workshop Item -
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A Simulated Annealing-Based Hyper-Heuristic For The Flexible Job Shop Scheduling Problem
Published 2023“…Flexible job shop scheduling problem (FJSP) is a common optimisation problem in the industry. The use of parallel machines allows an operation to be executed on a machine assigned from a set of alternative machines, raising a combination of machine assignment and job sequencing sub-problems. …”
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Process Planning Optimization In Reconfigurable Manufacturing Systems
Published 2008“…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market
Published 2019“…Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). In parallel, a feature selection technique based on the combination of mutual information (MI) and neural network (NN) is developed in this study to select the input variables subsets, which have substantial impact on forecasting of electricity price. …”
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Article -
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Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm
Published 2024“…For future study, other methods or algorithms can be applied in other machining processes to obtain optimum machining parameters.…”
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Conference or Workshop Item -
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