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121
Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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122
An enhanced quorum selection algorithm
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123
An Improved Quorum Selection Algorithm
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conferenceObject -
124
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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125
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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126
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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masterThesis -
127
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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129
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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130
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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131
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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132
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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133
Stochastic Search Algorithms for Exam Scheduling
Published 2007“…Then, we empirically compare the three proposed algorithms and FESP using realistic data. Our experimental results show that SA and GA produce good exam schedules that are better than those of FESP heuristic procedure. …”
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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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136
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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137
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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138
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. …”
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139
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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140
Bee Colony Algorithm for Proctors Assignment.
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