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Multi objective optimization and experimental investigation of the stirring performance of a novel micro actuator
Published 2025-05-01“…Subsequently, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is utilized for Multi-Objective Optimization to identify the optimal combination of structural parameters. …”
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603
An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations
Published 2025-07-01“…The proposed methodology leverages the Grey Wolf Optimization (GWO) metaheuristic algorithm, enthused by the grey wolves hunting, to identify the most strategic locations for EVCSs. …”
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604
Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization
Published 2025-05-01“…To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. …”
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605
APPLYING GRAPH THEORY TO OPTIMIZE PRODUCT DELIVERY ROUTES AND MINIMIZE COSTS IN THE RESTAURANT BUSINESS
Published 2025-06-01“…Routing algorithms based on graphical description are considered the most optimal analysis method for developing optimal product delivery routes, which helps minimize enterprise costs. …”
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606
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Bionic Compass Method Based on Atmospheric Polarization Optimization in Non-Ideal Clear Condition
Published 2024-11-01“…This paper proposes a bionic navigation method based on atmospheric polarization optimization to improve heading accuracy under non-ideal clear conditions. …”
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608
Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks
Published 2025-01-01“…This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. …”
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609
Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases
Published 2024-12-01“…Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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610
Research on the optimization model of anti-breast cancer candidate drugs based on machine learning
Published 2025-04-01“…Additionally, a multi-model fusion strategy and Particle Swarm Optimization (PSO) algorithm were employed to optimize both biological activity and ADMET properties, thereby improving the prediction of Caco-2, CYP3A4, hERG, HOB, and MN properties. …”
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611
PSA-Optimized Compressor Speed Control Strategy of Electric Vehicle Thermal Management Systems
Published 2025-05-01“…Precise control of compressor speed, informed by real-time sensor data, is essential for improving TMS efficiency and extending EV range. This study proposes a control strategy based on the PID Search Algorithm (PSA), ensuring optimal thermal management for an integrated battery and cabin TMS. …”
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612
Optimization of Rotary Blade Wear and Tillage Resistance Based on DEM-MBD Coupling Model
Published 2025-02-01“…The optimized rotary blade achieves the effects of reduced resistance and wear, improves the lifespan of the blade, reducing material loss, and meeting the requirements of sustainable agricultural production.…”
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613
Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches
Published 2025-08-01“…The K-Nearest Neighbors (KNN) algorithm is employed as the base model, with hyperparameter optimization performed using GridSearchCV, RandomizedSearchCV, Optuna, and Particle Swarm Optimization (PSO). …”
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614
Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials
Published 2022-09-01“…In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. …”
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615
Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management
Published 2025-04-01“…The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). …”
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Class-aware multi-source domain adaptation algorithm for medical image analysis using reweighted matrix matching strategy.
Published 2025-01-01“…This algorithm employs a class-aware strategy to strengthen positive transfer effects between similar classes. …”
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618
Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology
Published 2025-04-01Get full text
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619
Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations
Published 2025-03-01“…The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. …”
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620
Meta-Features Extracted from Use of kNN Regressor to Improve Sugarcane Crop Yield Prediction
Published 2025-05-01“…A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. …”
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