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making algorithm » cosine algorithm (Expand Search)
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data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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making algorithm » cosine algorithm (Expand Search)
cscap algorithm » cosine algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
case making » lane marking (Expand Search)
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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A hybrid genetic algorithm for task allocation in multicomputers
Published 2018“…A hybrid genetic algorithm for the task allocation problem (HGATA) in multicomputers is presented. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
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XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012)
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Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands
Published 2020“…A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. …”
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The effects of data balancing approaches: A case study
Published 2023“…Our LC-HRMS dataset contains 1241 bovine urine samples, of which only 65 specimens were from animal studies and guaranteed to contain growth-stimulating hormones while the rest has been reported to be untreated, making it a ∼5% imbalanced dataset. In this research, classification algorithms, combined with resampling strategies and dimensionality reduction methods, were investigated to find a prediction model to correctly identify the samples of treated animals. …”
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A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai
Published 2024“…The proposed framework comprises key elements: Important Decisions derived from interviews with transportation leaders, Knowledge Management enhanced by AI algorithms, Data Mining/Analysis utilizing historical data, the Fleet Management System employing Oracle ERP, and a Data-Driven Decision Support Framework that leans towards the extended framework approach. …”
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Brain Source Localization in the Presence of Leadfield Perturbations
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A parallel search tree algorithm for vertex cover on graphical processing units. (c2013)
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Evaluation of C-arm CT metal artifact reduction algorithm during intra-aneurysmal coil embolization
Published 2016“…The line profile plot analysis of device profile in both phantom and clinical data demonstrated improved device visibility with MAR correction. Conclusions MAR algorithm successfully reduced streak artifacts from coil embolization in all cases, thus allowing more accurate assessment of devices and adjacent brain parenchyma.…”
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A localized navigation algorithm for radiation evasion for nuclear facilities: Optimizing the "Radiation Evasion" criterion: Part I
Published 2013“…This novel algorithm leverages the use of localized information for its operation. …”
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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
Published 2022“…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …”
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