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Protein structure prediction in the 3D HP model
Published 2009“…In this paper, we present a Particle Swarm Optimization (PSO) based algorithm for predicting protein structures in the 3D HP model. …”
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Autonomous 3D Deployment of Aerial Base Stations in Wireless Networks with User Mobility
Published 2019“…We present performance results for the algorithm as a function of various system parameters assuming a random walk mobility model. …”
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Particle swarm optimization approach for protein structure prediction in the 3D HP model
Published 2012“…In this paper, we present a particle swarm optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar model. …”
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Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence
Published 2020“…One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. …”
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Allocation and re-allocation of data in a grid using an adaptive genetic algorithm
Published 2006“…Allocation and re-allocation of data in a grid using an adaptive genetic algorithm. …”
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Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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3D GEOSTATISTICAL MODELING OF FACIES AND PETROPHYSICAL PROPERTIES OF THE UPPER KHARTAM OUTCROP OF KHUFF FORMATION, CENTRAL SAUDI ARABIA
Published 2020“…The sea level within these zones was relatively high, fluctuated and dropped within deposits of zones 1 and 2, zones 3 and 4 and zone 5, respectively. In the context of 3D modelling, lithofacies within each zone was populated separately by using different geostatistical algorithm. …”
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Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
Published 2022“…Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. …”
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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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Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms
Published 2022“…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. …”
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Evolutionary algorithm for predicting all-atom protein structure
Published 2011“…Our results show that our algorithm produces promising 3D structures and demonstrates the advantage of the energy model used.…”
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A genetic algorithm for testable data path synthesis
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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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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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