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181
Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose
Published 2020“…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. …”
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182
Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas
Published 2024“…Our simulation results show that the proposed Meta-RL algorithm can outperform the IoT EH of the DQN, PSO algorithm, and the greedy solution by 25%, 32%, and 45%, respectively. …”
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183
Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain
Published 2025“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”
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184
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185
Optimisation of PV Cleaning Practices: Comparison Between Performance Based and Periodic Based Approaches
Published 2018“…It is important to choose the correct cleaning method, frequency, and approach to ensure that the best practices are being used on the basis of both economic and technical criteria. To optimize the cleaning practices, an algorithm was developed by the author based on the literature review and economic analysis, and was run for multiple panels and tilts at the DEWA OTF solar lab in Dubai, UAE. …”
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186
Scatter search metaheuristic for homology based protein structure prediction. (c2015)
Published 2015“…To improve homology based PSP, we propose a scatter search (SS) metaheuristic algorithm. Our algorithm optimizes the initial poor alignments, generated by a dynamic programming method. …”
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masterThesis -
187
Scatter search for homology modeling
Published 2016“…Results obtained by our SS algorithm are compared with other approaches. The 3D models predicted by our algorithm show improved root mean standard deviations with respect to the native structures.…”
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conferenceObject -
188
Dynamic single node failure recovery in distributed storage systems
Published 2017“…The contributions of the paper are four fold: (i) generating an optimized block distribution scheme among the nodes of a given data center for fixed and variable size blocks; (ii) optimization of storage allocation under dynamic environments with data block arrivals; (iii) optimization of storage allocation with newly added storage nodes; and (iv) generating an effective block distribution scheme among the nodes by accounting for varying priorities among data blocks. …”
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189
Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring
Published 2019“…The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.…”
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190
Machine learning approach for the classification of corn seed using hybrid features
Published 2020“…For each corn seed image, a total of fifty-five hybrid-features was acquired on every non-overlapping region of interest (ROI), sizes (75 × 75), (100 × 100), (125 × 125) and (150 × 150). The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. …”
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191
Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes
Published 2025“…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”
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192
Iterative heuristics for multiobjective VLSI standard cellplacement
Published 2001“…We employ two iterative heuristics for the optimization of VLSI standard cell placement. These heuristics are based on genetic algorithms (GA) and tabu search (TS) respectively. …”
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193
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices
Published 2019“…Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger network scenarios. …”
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194
An Infrastructure-Assisted Workload Scheduling for Computational Resources Exploitation in the Fog-Enabled Vehicular Network
Published 2020“…Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. …”
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195
A Novel Centrality-Based Approach for Link Prediction
Published 2025“…We also use a genetic algorithm to optimize the weights of these metrics, reflecting their contributions to link prediction. …”
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masterThesis -
196
Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS
Published 2022“…After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic Whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. …”
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197
EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING
Published 2020“…These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) [sadiq et al., 1999] respectively. …”
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198
Multidimensional Gains for Stochastic Approximation
Published 2019“…The two algorithms assume full knowledge of the Jacobian. The recursive algorithms are proposed for generating the optimal iterative-dependent matrix gain. …”
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199
Estimation of power grid topology parameters through pilot signals
Published 2016“…Pilot voltage stimulations are injected from distributed generators and the induced currents effects are measured at several nodes in the system. …”
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conferenceObject -
200
Improving Active Resonance Damping and Unbalanced Voltage Mitigation Based on Combined DDSRF and Washout Filter in Islanded Microgrids
Published 2024“…The presented control algorithm can perform optimally under dynamic changes of active and reactive power of nonlinear and imbalanced loads. …”