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181
Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
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182
Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement
Published 2025-02-01“…This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. …”
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183
Joining Application of Unmanned Aerial Vehicle Imagery with GIS for Monitoring of Soft Cliff Linear Habitats
Published 2025-01-01“…The study’s main objective was to assess short-term behavior of soft cliffs as littoral linear habitats during calm season storm events in the example of the Olandų Kepurė cliff, located on a peri-urban protected seashore (Baltic Sea, Lithuania). …”
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184
A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys
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185
Improved Motion Correction in Dynamic Contrast-Enhanced MRI Using Low Rank With Soft Weighting
Published 2025-01-01“…This paper introduces a motion-corrected, free-breathing dynamic contrast-enhanced (DCE) MRI reconstruction method, termed low-rank plus sparse (L+S) with soft weighting. We designed a soft weighting matrix that smoothly transitions spokes between the target and other motion states to suppress motion blurring. …”
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186
Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis
Published 2025-01-01“…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. …”
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187
A study on plant root apex morphology as a model for soft robots moving in soil.
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188
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189
Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN
Published 2020-01-01“…In this research work, we propose a soft sensor model based on deep learning which combines stacked autoencoders with neural network (SAE-NN). …”
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190
Complex q-Rung Orthopair Fuzzy N-Soft Sets: A New Model with Applications
Published 2021-01-01“…To that purpose, the proposed theory integrates complex q-rung orthopair fuzzy sets with the parametric structure of N-soft sets. The framework that arises captures information beyond the confined space of complex intuitionistic fuzzy N-soft sets and complex Pythagorean fuzzy N-soft sets, with the assistance of a parameter q. …”
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191
Local Community Detection Algorithm Based on Minimal Cluster
Published 2016-01-01Get full text
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192
Student Performance Prediction Using Machine Learning Algorithms
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193
RLS Beamforming Algorithm Based on Wavelet Transform
Published 2015-02-01“…This algorithm uses soft thresholding wavelet transform for signal denoising, solves the problem that the traditional Adaptive beamforming algorithm is sensitive to noise and the steady-state error is big under the environment of the low signal to noise ratio(SNR)and small forgetting factor. …”
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194
Ventricular segmentation algorithm for echocardiography based on transfer learning and GAN
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195
Posture detection of athletes in sports based on posture solving algorithms
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196
User preference information recommendation based on DCGNN and GNNPK algorithms
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197
Interior design assistant algorithm based on indoor scene analysis
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198
Sorting misorganized books in libraries based on ant colony algorithm
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199
Trajectory Tracking Control for Robotic Manipulator Based on Soft Actor–Critic and Generative Adversarial Imitation Learning
Published 2024-12-01“…Finally, it is demonstrated by the simulation results that the end effector of the robot tracking task is effectively accomplished by the proposed SL-GAIL algorithm, and more superior stability is exhibited in a test environment with interference compared with other algorithms.…”
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200
Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning
Published 2024-12-01“…Abstract Background Aiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model. Methods This paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. …”
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