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implementing each » implementing iot (Expand Search), implementing surface (Expand Search)
using algorithm » cosine algorithm (Expand Search)
each algorithm » search algorithm (Expand Search), jaya algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
element » elements (Expand Search)
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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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Parallel Algorithm for Hardware Implementation of Inverse Halftoning
Published 2005“…A Parallel algorithm and its hardware implementation of Inverse Halftone operation is proposed in this paper. …”
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Parallel algorithm for hardware implementation of inverse halftoning
Published 2005“…A parallel algorithm and its hardware implementation are proposed for an inverse halftone operation. …”
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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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A novel encryption algorithm using multiple semifield S-boxes based on permutation of symmetric group
Published 2023“…<p dir="ltr">There is a need of secure and robust encryption algorithm that can be implemented on each and diverse software and hardware platforms. …”
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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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A Parallel Algorithm for Inverse Halftoning and its Hardware Implementation
Published 2020“…The LUT method for inverse halftoning is parallelized by dividing the single Look-Up Table of LUT method for inverse halftoning into many smaller Look-up Tables (sLUTs). In the parallel algorithm up-to four pixels can be fetched from the halftone image concurrently and go to their separate smaller Look-Up Tables (sLUT) from where each template fetches its inverse halftone value independent to other pixels. …”
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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 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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