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A hybrid graph representation for recursive backtracking algorithms
Published 2017“…The use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. …”
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42
A genetic algorithm for testable data path synthesis
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43
A neural networks algorithm for data path synthesis
Published 2003“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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44
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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45
Distributed dimension reduction algorithms for widely dispersed data
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46
A parallel optimization algorithm for the maximum clique problem. (c2008)
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48
Autonomous 3D Deployment of Aerial Base Stations in Wireless Networks with User Mobility
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49
Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function
Published 2020“…In this paper, a fuzzy based process targeting model is developed for a product with multi-characteristic. …”
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50
Process Targeting Of Multi-Characteristic Product Using Fuzzy Logic And Genetic Algorithm With An Interval Based Taguchi Cost Function
Published 2020“…In this paper, a fuzzy based process targeting model is developed for a product with multi-characteristic. …”
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Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
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54
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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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…Various clustering methods are available, including K-means, hierarchical clustering, and density-based clustering. Among these, K-means is widely used for efficiently solving clustering problems. …”
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masterThesis -
57
Predicting Dropouts among a Homogeneous Population using a Data Mining Approach
Published 2019“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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A genetic algorithm for corrective retesting. (c1995)
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Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques
Published 2020“…This conclusion was achieved after preprocessing a number of data values from these data sets.</p><h2>Other Information</h2><p dir="ltr">Published in: Algorithms<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/a13080202" target="_blank">https://dx.doi.org/10.3390/a13080202</a></p>…”