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An enhanced quorum selection algorithm
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An Improved Quorum Selection Algorithm
Published 2008Get full text
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164
Fuzzy genetic algorithm for floorplanning
Published 2020“…Genetic algorithms (GAs) have been found to be very effective in solving numerous optimization problems, especially those with many (possibly) conflicting and noisy objectives. …”
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165
Use data Mining Techniques to Predict Users’ Engagement on the Social Network Posts in The Period Before, During and After Ramadan
Published 2017“…Different classification algorithms were applied to the dataset using the Rapidminer tool. …”
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166
The use of multi-task learning in cybersecurity applications: a systematic literature review
Published 2024“…Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. …”
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167
Correlation Clustering with Overlaps
Published 2020“…In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT
Published 2021“…This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. …”
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A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
Published 2022“…The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. …”
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172
QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
Published 2024“…We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. …”
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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
Published 2024“…The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. …”
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174
A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems
Published 2025“…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. …”
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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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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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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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179
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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180
Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches
Published 2024“…In recent studies, traditional machine learning and deep learning algorithms have been implemented to detect fake job postings; this research aims to use two transformer-based deep learning models, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT-Pretraining Approach (RoBERTa) to detect fake job postings precisely. …”