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141
Use data Mining Techniques to Predict Users’ Engagement on the Social Network Posts in The Period Before, During and After Ramadan
Published 2017“…In contrast to traditional approaches on this topic, this dissertation seeks to analyze the depth of users’ engagement with social media posts and discuss the basis for a predictive model that to predict the total engagement of a post before publishing. At the beginning, a data set was collected from Crowdbabble online tool and the collected dataset presented different periods and different social media networks to examine the user behavior at different times. …”
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142
Just-in-time defect prediction for mobile applications: using shallow or deep learning?
Published 2023“…Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. …”
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143
Enhancing e-learning through AI: advanced techniques for optimizing student performance
Published 2024“…The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. …”
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144
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145
Developing a UAE-Based Disputes Prediction Model using Machine Learning
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doctoralThesis -
146
Autonomous 3D Deployment of Aerial Base Stations in Wireless Networks with User Mobility
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Parallel implementation of clique partitioning using artificial neural networks. (c2000)
Published 2000Get full text
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masterThesis -
150
Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches
Published 2021Get full text
doctoralThesis -
151
Adaptive temperature control of a reverse flow process by using reinforcement learning approach
Published 2024“…Additionally, a second algorithm is presented to enhance the implementability of the reinforcement learning algorithm from a practical perspective. …”
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152
Brain Source Localization in the Presence of Leadfield Perturbations
Published 2015Get full text
doctoralThesis -
153
Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning
Published 2025“…Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. …”
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154
Scatter search for protein structure prediction. (c2008)
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masterThesis -
155
Privacy-Preserving Framework for Blockchain-Based Stock Exchange Platform
Published 2022“…<p dir="ltr">This paper presents a privacy persevering framework for a decentralized stock exchange platform, ensuring anonymity and unlinkability of the investors’ accounts and their respective trading activities. …”
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156
Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters
Published 2025“…In this study, we introduce the multimodal feature fusion for non-intrusive occupancy monitoring (MMF-NIOM) framework, which leverages both classical and deep machine learning algorithms to achieve state-of-the-art occupancy detection performance using smart meter data. …”
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157
A parallel ant colony optimization to globally optimize area in high-level synthesis. (c2011)
Published 2011Get full text
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masterThesis -
158
Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques
Published 2024“…Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. …”
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159
Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques
Published 2017Get full text
doctoralThesis -
160
A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
Published 2021“…The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. …”