بدائل البحث:
development level » development goal (توسيع البحث)
models algorithm » mould algorithm (توسيع البحث), deer algorithm (توسيع البحث)
method algorithm » mould algorithm (توسيع البحث)
level algorithm » deer algorithm (توسيع البحث)
element » elements (توسيع البحث)
development level » development goal (توسيع البحث)
models algorithm » mould algorithm (توسيع البحث), deer algorithm (توسيع البحث)
method algorithm » mould algorithm (توسيع البحث)
level algorithm » deer algorithm (توسيع البحث)
element » elements (توسيع البحث)
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21
Parallel physical optimization algorithms for allocating data to multicomputer nodes
منشور في 1994"…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …"
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article -
22
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
منشور في 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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masterThesis -
23
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24
Using genetic algorithms to optimize software quality estimation models
منشور في 2004"…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …"
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احصل على النص الكامل
masterThesis -
25
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26
Second-order conic programming for data envelopment analysis models
منشور في 2022"…This paper constructs a second-order conic optimization problem unifying several DEA models. Moreover, it presents an algorithm that solves the former problem, and provides a MATLAB function associated with it. …"
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27
DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data
منشور في 2022"…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …"
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احصل على النص الكامل
masterThesis -
28
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29
An ant colony optimization algorithm to improve software quality prediction models
منشور في 2011"…For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. …"
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article -
30
A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models
منشور في 2010"…In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.…"
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article -
31
Correlation Clustering with Overlaps
منشور في 2020"…In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …"
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masterThesis -
32
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33
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34
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35
Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting
منشور في 2011احصل على النص الكامل
doctoralThesis -
36
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37
An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
منشور في 2021الموضوعات: -
38
A reduced model for phase-change problems with radiation using simplified PN approximations
منشور في 2025"…The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. …"
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article -
39
The automation of the development of classification models and improvement of model quality using feature engineering techniques
منشور في 2023"…In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. …"
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40
UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
منشور في 2024"…<p>Feature selection (FS) is a crucial technique in machine learning and data mining, serving a variety of purposes such as simplifying model construction, facilitating knowledge discovery, improving computational efficiency, and reducing memory consumption. …"