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1
Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
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2
Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…It is difficult to have efficient detection systems to precisely separate normal behavior from malicious behavior, based on the analysis of network traffic. …”
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Machine Learning Approach for the Design of an Assessment Outcomes Recommendation System
Published 2021“…Our research processes and workflows supported by AI utilize machine learning technology in order to interpret big data, analyze broad data sets and recognize associations with more reliably. …”
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An ant colony optimization algorithm to improve software quality prediction models
Published 2011“…As a result, the prediction models built on one data set show a significant deterioration of their accuracy when they are used to classify new, unseen data. …”
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Mapping realistic data sets on parallel computers
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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm
Published 2022“…The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. …”
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Parameter Estimation Of Wiener-Hammerstein Models Via Genetic Algorithms
Published 2020“…Conventional methods of estimating model parameters have difficulties with both nonlinear systems and with systems operating in noisy environments. …”
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Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems
Published 2022“…The developed techniques are based on Support Vector Machine (SVM) model to improve the diagnosis of WEC systems. First, to deal with model uncertainties in WEC systems, the SVM will be extended to interval-valued data with the aim of achieving greater accuracy and robustness for these uncertainties. …”
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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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Modeling and Control of a Robot Based Rehabilitation System for the Head-Neck Joint
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12
Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval
Published 2024“…Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. …”
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Automatic keyword extraction from a real estate classifieds data set
Published 2011“…Keywords are considered to be a solution to this problem and are now widely used to search the information over the internet.In this project we analyze a real estate classifieds data set, with an objective to find keywords that represent this data set. …”
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Data reductions and combinatorial bounds for improved approximation algorithms
Published 2016“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System
Published 2021Subjects: “…Distributed computing and systems software…”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We also present a technique for efficient mapping of large data sets. The algorithms include a parallel genetic algorithm (PGA), a parallel neural network algorithm (PNN) and a parallel simulated annealing algorithm (PSA). …”
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