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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“…One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. …”
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Oversampling techniques for imbalanced data in regression
Published 2024“…For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. …”
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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy
Published 2023“…Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. …”
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165
Robust Control Of Sampled Data Systems
Published 2020“…They then present a numerical controller design algorithm based on the derived bounds. Examples are used for demonstration.…”
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166
Robust Control Of Sampled Data Systems
Published 2020“…They then present a numerical controller design algorithm based on the derived bounds. Examples are used for demonstration.…”
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167
Robust Control Of Sampled Data Systems
Published 2020“…They then present a numerical controller design algorithm based on the derived bounds.Examples are used for demonstration.…”
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168
Correlation Clustering via s-Club Cluster Edge Deletion
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Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval
Published 2024“…<p dir="ltr">Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). …”
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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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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling
Published 2016“…Previous research has developed a simulation-based approach to solve the problem by optimizing resources resource allocation decisions on starting specific project activities at specific times. …”
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173
A kernelization algorithm for d-Hitting Set
Published 2010“…For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. …”
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A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks
Published 2022“…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”
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The Effects of Data Mining on Small Businesses in Dubai
Published 2011“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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179
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. Our results compared favorably with those of a recognized tag SNP selection algorithm using three different data sets from the HapMap project.…”
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Particle swarm optimization algorithm: review and applications
Published 2024“…This paper surveys the published papers in PSO algorithms. Twenty research papers are analyzed and classified according to the implementation area used by the PSO algorithm (neural networks, feature selection, and data clustering). …”
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