Search Results - parallel evaluation ((((machine algorithm) OR (mining algorithm))) OR (force algorithm))
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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem
Published 2023Conference Paper -
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Random sampling method of large-scale graph data classification
Published 2024“…Finally, we classified the graphs of data blocks using the SVM algorithm. In experimental evaluation, our proposed method outperformed state-of-the-art graph kernels on graph classification datasets in terms of accuracy.…”
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Hybridizing guided genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources
Published 2023“…This paper focuses on solving unrelated parallel machine scheduling with resource constraints (UPMR). …”
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A spark-based parallel fuzzy C median algorithm for web log big data
Published 2022“…Based on the Rand Index and SSE (sum of squared error), the parallel Fuzzy C median algorithm's performance is evaluated in the PySpark platform. …”
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Parallel Execution of Runge-Kutta Methods for Solving Ordinary Differential Equations
Published 2004“…The method used here is actually have been tailored made for the purpose of parallel machine where the subsequent functions evaluations do not depend on the previous function evaluations. …”
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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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Application of inerter in passenger vehicle suspension systems / Soong Ming Foong
Published 2015“…Then, switching algorithms were implemented to the inerter to evaluate potential further ride performance improvement brought by these modifications. …”
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Minimizing machining airtime motion with an ant colony algorithm
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Regenerative braking strategy for electric vehicles using improved adaptive genetic algorithm
Published 2017“…One uses Standard Genetic Algorithm (SGA), the second strategy uses Improved Adaptive Genetic Algorithm (IAGA). …”
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VHDL modeling and simulation of the back-propagation algorithm and its mapping to the RM
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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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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…MEMPHA, a hypergraphs-based model, aims to aspire to the challenge by providing topology modelling of the target parallel machine and application modelling of the parallel application, which is hypergraph-based model, to abstract the details of HPAs. …”
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Improved criteria determination of an automated negative lightning return strokes characterisation using Brute-Force search algorithm
Published 2021“…Accordingly, the proposed Brute-Force search algorithm characterised the negative lightning return strokes parameters based on the seven parameters. …”
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Optimization of extractive Automatic Text Summarization using Decomposition-based Multi-objective Differential Evolution and parallelization
Published 2024“…The central challenge in Automatic Text Summarization (ATS) is efficiently generating machine-generated text summaries through optimization algorithms, a critical component for systems dealing with textual information processing. …”
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Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
Published 2024“…Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. …”
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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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Leveraging data lake architecture for predicting academic student performance
Published 2024“…In addition to forecasting the student performance, appropriate machine learning algorithms such as Support Vector Classifier, Naive Bayes, and Decision Trees are used to build prediction models by using the data lake's scalability and parallel processing capabilities. …”
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