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modeling algorithm » scheduling algorithm (Expand Search)
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data modeling » data models (Expand Search), spatial modeling (Expand Search)
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modeling algorithm » scheduling algorithm (Expand Search)
groups algorithm » mould algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
element based » event based (Expand Search)
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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
Published 2023“…Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
doctoralThesis -
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Parallel physical optimization algorithms for allocating data to multicomputer nodes
Published 1994“…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …”
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data
Published 2023“…</p><h3>Research Design & Methods</h3><p dir="ltr">The study aimed to investigate performance of AI models in estimating BGL among diabetic patients using non-invasive wearable devices data An open-source dataset was used which provided BGL readings, diabetic status (Diabetic or non-diabetic), heart rate, Blood oxygen level (SPO2), Diastolic Blood pressure, Systolic Blood Pressure, Body temperature, Sweating, and Shivering for 13 participants by age group taken from WDs. …”
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data
Published 2022“…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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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“…Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. …”
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A Survey of Data Clustering Techniques
Published 2023“…Clustering, an unsupervised learning technique, aims to identify a specific number of clusters to effectively categorize the data through data grouping. Hence, clustering is related to many fields and is used in various applications that deal with large datasets. …”
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masterThesis -
35
Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
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Using genetic algorithms to optimize software quality estimation models
Published 2004“…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …”
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masterThesis -
39
Second-order conic programming for data envelopment analysis models
Published 2022“…This paper constructs a second-order conic optimization problem unifying several DEA models. Moreover, it presents an algorithm that solves the former problem, and provides a MATLAB function associated with it. …”
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The automation of the development of classification models and improvement of model quality using feature engineering techniques
Published 2023“…In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. …”