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developing based » developing a (Expand Search), developing 21st (Expand Search)
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141
Edge intelligence for network intrusion prevention in IoT ecosystem
Published 2023“…This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. …”
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142
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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143
A comparative analysis to forecast carbon dioxide emissions
Published 2022“…This leads to the second step, which involves formulating the multivariate time series CO<sub>2</sub> emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). …”
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145
Optimal Trajectory and Positioning of UAVs for Small Cell HetNets: Geometrical Analysis and Reinforcement Learning Approach
Published 2023“…Then, using geometrical analysis and deep reinforcement learning (RL) method, we propose several algorithms to find the optimal trajectory and select an optimal pattern during the trajectory. …”
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147
Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers
Published 2024“…Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. …”
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148
Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice
Published 2020“…Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). …”
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149
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150
Parallel Algorithm for Hardware Implementation of Inverse Halftoning
Published 2005“…The algorithm is based on Lookup Tables from which the inverse halftone value of a pixel is directly determined using a pattern of pixels. …”
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151
Empirical comparison of regression test selection algorithms
Published 2001“…In the maintenance phase, the regression test selection problem refers to selecting test cases from the initial suite of test cases used in the development phase. In this paper, we empirically compare five representative regression test selection algorithms, which include: Simulated Annealing, Reduction, Slicing, Dataflow, and Firewall algorithms. …”
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152
Parallel algorithm for hardware implementation of inverse halftoning
Published 2005“…The algorithm is based on lookup tables from which the inverse halftone value of a pixel is directly determined using a pattern of pixels. …”
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153
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154
Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Published 2023“…Finally, PSO and RPSO-based interval centers and ranges and upper and lower bounds techniques are developed to deal with model uncertainties in WES. …”
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155
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156
Blood Glucose Regulation Modelling and Intelligent Control
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157
MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection
Published 2023“…<p dir="ltr">Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning methods have achieved reasonable accuracy at this task. …”
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158
A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
Published 2025“…A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. …”
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159
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Artificial intelligence-based methods for fusion of electronic health records and imaging data
Published 2022“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”