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method algorithm » mould algorithm (Expand Search)
could algorithm » mould algorithm (Expand Search), carlo algorithm (Expand Search), colony algorithm (Expand Search)
using algorithm » cosine algorithm (Expand Search)
element » elements (Expand Search)
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Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm
Published 2024“…Additionally, these data can be readily applied to compare algorithmic results referenced by (Babu, T.S. et al., 2015; PrasanthRam, J. et al., 2017) [2,3], and contribute to the development of new processes for practical applications.…”
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A decentralized load balancing strategy for parallel search-three optimization. (c2010)
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Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data
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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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Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm
Published 2022“…The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. …”
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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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Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning
Published 2023“…This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). …”
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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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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 redundancy management for leaf-edges in connected environments
Published 2022“…Although the sensed data could be useful for various applications (e.g., event detection in cities, energy management in commercial buildings), it first requires pre-processing to clean various inconsistencies (e.g., anomalies, redundancies, missing values). …”
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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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Distributed dimension reduction algorithms for widely dispersed data
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Haplotype inference by pure-parsimony using revamped delayed haplotype selection. (c2011)
Published 2011Get full text
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masterThesis