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modeling algorithm » scheduling algorithm (Expand Search)
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. …”
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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
Published 2021“…Different classifiers were trained, validated, and tested for two different classification experiments - personalized and generalized. …”
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Using Machine Learning Algorithms to Forecast Solar Energy Power Output
Published 2025“…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
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A method for test scheduling using an evolutionary approach. (c2000)
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masterThesis -
9
Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. …”
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Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms
Published 2007“…The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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An incremental approach for test scheduling and synthesis using genetic algorithms
Published 2017“…This paper presents a new and an efficient method for concurrent BIST synthesis and test scheduling. This method maximizes concurrent testing of modules while performing the allocation of functional units, test registers, and multiplexers. …”
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An incremental approach for test synthesis and scheduling using genetic algorithms. (c2002)
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14
A hybrid distributed test generation method using deterministic and genetic algorithms
Published 2017“…The algorithm is parallelized based on a cluster of workstations using the message passing interface (MPI) library. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. …”
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Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021Subjects: “…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…”
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Power-constrained system-on-a-chip test scheduling using a genetic algorithm
Published 2006“…This paper presents an efficient approach for the test scheduling problem of core-based systems based on a genetic algorithm. …”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
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Empirical comparison of regression test selection algorithms
Published 2001“…The empirical results show that the five algorithms can be used for different requirements of regression testing. …”
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Natural optimization algorithms for optimal regression testing
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