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581
Random vector functional link network: Recent developments, applications, and future directions
Published 2023“…Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. …”
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582
Reinforcement Learning-Based School Energy Management System
Published 2020“…In this work, a Deep Reinforcement Learning agent is proposed for controlling and optimizing a school building’s energy consumption. It is designed to search for optimal policies to minimize energy consumption, maintain thermal comfort, and reduce indoor contaminant levels in a challenging 21-zone environment. …”
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583
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584
Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks
Published 2018“…We also propose efficient sub-optimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra dense networks. …”
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article -
585
Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging
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doctoralThesis -
586
Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
Published 2025“…In the second, we consider optimized UAV position adaptation where we generated the problem as a multi-objective optimization problem and solved it as a mixed-integer linear programming problem with constraints related to UAV range, UAV steps, and time constraints. …”
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masterThesis -
587
LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
Published 2024Get full text
article -
588
DRL-Based IRS-Assisted Secure Hybrid Visible Light and mmWave Communications
Published 2024“…Furthermore, it intelligently selects the optimal technique to improve SC, whether VLC or RF. …”
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589
On Equivalent Circuit Model-Based State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles
Published 2025“…A third-order equivalent circuit model is employed for the LIB based on electrochemical impedance spectra test results, with model parameters identified using a particle swarm optimization algorithm. Two real-time model-based estimation algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared for SoC estimation. …”
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590
Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems
Published 2023“…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AASP&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …”
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591
Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
Published 2021“…The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. …”
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592
Novel hybrid informational model for predicting the creep and shrinkage deflection of reinforced concrete beams containing GGBFS
Published 2022“…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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593
Could Petrol Stations Play a Key Role in Transportation Electrification? A GIS-Based Coverage Maximization of Fast EV Chargers in Urban Environment
Published 2022“…More specifically, the location problem is modelled as a maximum coverage location problem (MCLP) and solved using a geographic information system (GIS) based platform. The spatial optimization problem is solved using a linear-programming relaxation based MCLP algorithm developed in Python. …”
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594
Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems
Published 2023“…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AAS-P&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …”
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595
Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring
Published 2024“…By formulating the video resource allocation challenge as a multi-objective optimization problem, the framework aims to minimize network delays while respecting node capacity limitations. …”
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596
An evolutionary meta-heuristic for state justification insequential automatic test pattern generation
Published 2001“…Evolutionary algorithms have been effective in solving many search and optimization problems. …”
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597
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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598
Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review
Published 2022“…Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. …”
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599
EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING
Published 2020“…The interest in finding an optimal partitioning especially in VLSI, and has been a hot issue in recent years. …”
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600
General iterative heuristics for VLSI multiobjective partitioning
Published 2003“…In this paper, we engineer two iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) and incorporate fuzzy rules in order to handle the multiobjective cost function. …”
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