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coding algorithm » cosine algorithm (Expand Search), colony algorithm (Expand Search), scheduling algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
finding » findings (Expand Search)
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Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Large language models for code completion: A systematic literature review
Published 2024“…Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. …”
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…UniBFS exploits the inherent characteristic of binary algorithms-binary coding-to search the entire problem space for identifying relevant features while avoiding irrelevant ones. …”
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Auto-indexing Arabic texts based on association rule data mining. (c2015)
Published 2015“…Our model denotes extracting new relevant words by relating those chosen by the previous classical methods, to new words using data mining rules. …”
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Salp swarm algorithm: survey, analysis, and new applications
Published 2024“…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm
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Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques
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Localizing-ground Transmitters Using Airborne Antenna Array
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doctoralThesis -
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Application of Red Deer Algorithm in Optimizing Complex functions
Published 2021“…The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. …”
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Automatic image quality evaluation in digital radiography using for‐processing and for‐presentation images
Published 2024“…Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. …”
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Squirrel Search Algorithm for Portfolio Optimization
Published 2019“…Portfolio Optimization is a standard financial engineering problem. It aims for finding the best allocation of resources for a set of assets. …”
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An algorithm for solving bond pricing problem
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Towards a self-powering greenhouse using semi-transparent PV: Utilizing hybrid BESS-hydrogen energy storage system
Published 2025“…Second, by employing a comprehensive approach with the GBO algorithm, the research examines various scenarios as a multi-objective optimization problem, balancing economic considerations such as net present value (NPV) and energy dependency (ED). …”
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Parallel genetic algorithm for disease-gene association
Published 2011“…In this work, we combine few successful strategies from the literature and present a parallel genetic algorithm for the Tag SNP Selection problem. …”
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The Role of KM in Enhancing AI Algorithms and Systems
Published 2020“…Knowledge Management processes present a vital role in improving AI systems and algorithms. …”
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Natural optimization algorithms for optimal regression testing
Published 1997“…The optimal regression testing problem is that of determining the minimum number of test cases needed for revalidating modified software in the maintenance phase. The present two natural optimization algorithms, namely simulated annealing and genetic algorithms, for solving this problem. …”
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A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM
Published 2020“…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. …”
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