Three-phase approach for curriculum-based course timetabling problem. (c2012) by Jazzar, Hanaa Salem El-

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A decentralized load balancing strategy for parallel search-three optimization. (c2010) by Mouawad, Amer E.

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Three-phase simulated annealing algorithms for exam scheduling by Mansour, Nashat

“…Hence, we refer to our solution methods as 3-phase simulated annealing (3PSA). We empirically compare 3PSA with a 4-phase clustering-based heuristic algorithm using realistic data. …”
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Allocating data to distributed-memory multiprocessors by genetic algorithms by Mansour, Nashat

“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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Data reductions and combinatorial bounds for improved approximation algorithms by Abu-Khzam, Faisal N.

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Public health utility of cause of death data: applying empirical algorithms to improve data quality by Sarah Charlotte Johnson (9715415)

Parallel physical optimization algorithms for allocating data to multicomputer nodes by Mansour, Nashat

“…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval by Mohammed Tahar Habib Kaib (21633176)

“…The proposed algorithm has been tested on three tank system pilot plant and Ain El Kebira Cement rotary kiln process. …”

DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

“…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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A comparison of data mapping algorithms for parallel iterative PDE solvers by Mansour, Nashat

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Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations by Mansour, Nashat

“…Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. …”
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Efficient Approximate Conformance Checking Using Trie Data Structures by Awad, Ahmed

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Joint Inversion of Direct Current Resistivity and Seismic Refraction Data, using Local and Global Optimization Algorithms with Synthetic and Real Data Examples by unknown

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How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation by Joni Salminen (7434770)

Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

“…However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. …”

Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R² = 0.05 to 0.99 in a representative thiophene set. …”

A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015) by Wehbe, Gioia Wahib

“…Additionally, it was able to identify several differentiable regions in the real data set and on different chromosomes. However, we noticed that chromosomes 1, 3 and 6 had the highest occurrence rate of differentiable motifs (9, 8 and 6 motifs respectively). …”
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Auto-indexing Arabic texts based on association rule data mining. (c2015) by Rouba G. Nasrallah

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Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms by Usman Ali (6586886)

“…This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. …”