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4961
Application of PSO-Optimized Twin Support Vector Machine in Medium and Long-Term Load Forecasting under the Background of New Normal Economy
Published 2022-01-01“…In order to improve the accuracy of medium and long-term load forecasting in the new normal economy, this paper combines the PSO-optimized twin support vector machine to build a medium and long-term load forecasting model under the background of the new normal economy. …”
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4962
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4963
UCSwin‐UNet model for medical image segmentation based on cardiac haemangioma
Published 2024-10-01“…To evaluate the effectiveness of the proposed model, this paper demonstrates its optimality through ablation experiments and comparisons with other mainstream models. …”
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4964
Rescheduling of Multi-Scenario and Multi-Objective Dynamic Changes of Ship Group Construction
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4965
Deep reinforcement learning for multi-objective location optimization of onshore wind power stations: a case study of Guangdong Province, China
Published 2025-07-01“…To solve this model at large scale, a deep reinforcement learning (DRL) algorithm is designed and implemented. …”
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4966
Intelligent building construction site safety inspection model based on YOLOX
Published 2025-06-01“…At the same time, the optimized YOLOX algorithm will be applied to the construction of a model for safety detection in intelligent building construction sites. …”
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4967
A graph-based sensor recommendation model in semantic sensor network
Published 2022-05-01“…We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. …”
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4968
Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines
Published 2023-02-01“…Solving the problem of multi-criteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. …”
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4969
Towards precision agriculture: metaheuristic model compression for enhanced pest recognition
Published 2025-07-01“…The core of this approach integrates InceptionV3 as a backbone feature extractor to capture rich and discriminative features, enhanced further using a channel attention (CA) mechanism for feature refinement. To reduce model complexity and improve deployment feasibility, a metaheuristic optimization algorithm was incorporated that significantly reduces computational overhead without compromising performance. …”
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4970
Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection
Published 2025-06-01“…This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. …”
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4971
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4972
Evaluation method of e-government audit information based on big data analysis
Published 2025-12-01“…Furthermore, a parallel PSO-RF algorithm combining Particle Swarm Optimization (PSO) and Random Forest (RF) is designed to enhance classification performance and computational efficiency. …”
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4973
A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling
Published 2025-06-01“…The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes.…”
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4974
Power Dispatch Stability Technology Based on Multi-Energy Complementary Alliances
Published 2025-06-01“…It aims to enhance dispatch stability and energy utilization through an innovative Distributed Overlapping Coalition Formation (DOCF) model. A distributed algorithm utilizing tabu search is proposed to solve the complex optimization problem in power resource allocation. …”
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4975
GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model
Published 2025-05-01“…This process significantly reduces modeling complexity and improves computational efficiency. …”
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4976
Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery
Published 2024-10-01“…Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. …”
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4977
A Hybrid GRA-TOPSIS-RFR Optimization Approach for Minimizing Burrs in Micro-Milling of Ti-6Al-4V Alloys
Published 2025-04-01“…Moreover, the GRA-TOPSIS-RFR method significantly outperforms existing optimization and prediction models, with the integration of the RFR model enhancing prediction accuracy by 42.6% compared to traditional linear regression approaches. …”
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4978
Short-Term Traffic Flow Forecasting Model Based on GA-TCN
Published 2021-01-01“…Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. …”
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4979
Development of a manipulator kinematic model using Abb Robot Studio
Published 2024-12-01“…The optimization of trajectories and collision management allowed to reduce the maintenance and operation costs of the robot, which led to an overall reduction in production costs, which will also be reflected in long-term use. …”
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4980
Inversion of snow geophysical parameters using the VHR PAZ X-band dual polarimetric SAR data: first known experiments in the Himalayan region
Published 2025-07-01“…In the present work, we utilized the copolar phase difference (CPD) for SD and Integral Equation model (IEM) for snow density. In this study, we proposed an improved algorithm for SD inversion, instead of relying on a single in-situ snow density value, we incorporated a range of snow densities (0.15 to 0.27 g/cm3), optimizing the axial ratio between 1.13 and 1.17. …”
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