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141
Classifying Maqams of Qur'anic Recitations Using Deep Learning
Published 2021“…We also introduce the “Maqam-478” dataset that can be used for further improvements on this work.…”
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Haplotype inference by pure-parsimony using revamped delayed haplotype selection. (c2011)
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masterThesis -
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Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks
Published 2021“…In this paper, we formulate a distributed downlink power allocation problem to optimize the transmit power for users to reach target data rates in hybrid RF/VLC networks. Then, we propose a distributed DRL-based algorithm Deep Deterministic Policy Gradient (DDPG), to solve the formulated computationally-intensive problem. …”
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146
Teaching–learning-based optimization algorithm: analysis study and its application
Published 2024“…The fundamentals of the TLBO algorithm have recently been updated by many researchers to improve its exploration and exploitation capabilities as well as performance. …”
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147
Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE
Published 2018“…Classification is a Data Mining (DM) technique used for prediction. On the other hand, feature selection is the process of finding the best set of features that has the most impact on a specific target. …”
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148
Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques
Published 2020“…The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.…”
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Content-Aware Adaptive Video Streaming Using Actor-Critic Deep Reinforcement Learning
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doctoralThesis -
154
Improving the security of SNMP in wireless networks
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conferenceObject -
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An enhanced quorum selection algorithm
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Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
Published 2021“…Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …”
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