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modeling algorithm » scheduling algorithm (Expand Search)
using algorithm » cosine algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
elemental » elementary (Expand Search), elements (Expand Search), element (Expand Search)
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141
Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands
Published 2020“…An IEEE standard test system is used as the hybrid AC/DC microgrid case study to assess the performance of proposed model.…”
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142
A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai
Published 2024“…My research aims to develop a data-driven decision support framework for fleet management, focusing on leveraging advanced algorithms, including decision trees and random forests, to generate domain-specific AI models. …”
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143
A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
Published 2021“…In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. …”
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. …”
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147
Clustering/partitioning algorithms and comparative analysis
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148
KNNOR: An oversampling technique for imbalanced datasets
Published 2021“…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. …”
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Predicting Plasma Vitamin C Using Machine Learning
Published 2022“…The objective of this study is to predict plasma vitamin C using machine learning. The NHANES dataset was used to predict plasma vitamin C in a cohort of 2952 American adults using regression algorithms and clustering in a way that a hypothetical health application might. …”
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151
A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015)
Published 2016“…New technologies, such as Next-Generation Genome Sequencing, can now provide huge amounts of data in little time. Big initiatives such as the International Hapmap Project and the 1000 Genome project are making use of these technologies to provide the scientific community with a detailed genetic reference from different populations. …”
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152
Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Published 2025“…To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. The model considers VoI and energy constraints of the SNs, enhancing both efficiency and sustainability. …”
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153
UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…<p>Feature selection (FS) is a crucial technique in machine learning and data mining, serving a variety of purposes such as simplifying model construction, facilitating knowledge discovery, improving computational efficiency, and reducing memory consumption. …”
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154
Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE
Published 2020“…The data analytics maturity model is used as the conceptual model for evaluating both data analytics and data governance in this research. …”
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155
Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices
Published 2025“…Drawing on data from the Smart Cities Index (SCI) and other economic and sustainability competitiveness metrics, the study uses various <u>ML algorithms</u> to categorize cities into <u>performance classes</u>, ranging from high-achieving Class 1 to emerging Class 3 cities. …”
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Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter
Published 2023“…A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. …”
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Detecting latent classes in tourism data through response-based unit segmentation (REBUS) in Pls-Sem
Published 2016“…This research note describes Response-Based Unit Segmentation (REBUS), a “latent class detection” technique used in partial least squares–structural equation modeling (PLS-SEM) to examine data heterogeneity. …”
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