検索結果 - "weight loss"
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981
A Simple and Effective KAN-Based Architecture for Accurate Battery RUL Prediction
出版事項 2025-01-01“…It also incorporates a channel-independent KANs structure with a regularized weighted loss function to handle variable-specific degradation patterns. …”Accurately estimating a lithium-ion battery’s Remaining Useful Life (RUL) is crucial for ensuring the safety and reliability of battery management systems. However, the performance of emerging architectures, such as Kolmogorov-Arnold Networks (KANs), is often hindered by the significant n...
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982
High performance sulfur-containing copolyesters from bio-sourced aromatic monomers
出版事項 2022-01-01“…After 28 weeks of incubation in humid soil, weight losses of up to 7.2% were observed. Considering their good mechanical properties, thermal stability and biodegradability, these renewable sulfur-containing copolyesters have great potential to replace petroleum-based commercial poly(ethylene terephthalate) in the food packaging industry, which is helpful to implement carbon neutrality and sustainable development in the polymer industry.…”In this investigation, a series of novel random bio-based thiophene–aromatic copolyesters, including thiophene and phenyl units, were successfully prepared from dimethyl 2,5-thiophenedicarboxylate, dimethyl 2,5-dimethoxyterephthalate, and the aliphatic diols ethylene glycol, 1,3-propanediol, 1,4-but...
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983
Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information
出版事項 2025-08-01“…Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. …”Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractogram...
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984
Domain adaptive deep possibilistic clustering for EEG-based emotion recognition
出版事項 2025-07-01“…Moreover, the DADPc incorporates adaptive weighted loss and memory bank strategies to enhance the reliability of pseudo-labels and cross-domain alignment. …”Emotion recognition based on electroencephalogram (EEG) faces substantial challenges. The variability of neural signals among different subjects and the scarcity of labeled data pose obstacles to the generalization ability of traditional domain adaptation (DA) methods. Existing approaches, especiall...
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985
Optimizing the acid resistance of concrete with granulated blast-furnace slag
出版事項 2022-03-01“…After 13 weeks of storing concrete specimens in sulfuric acid (H2SO4, pH 3.5), reduced damage depths and lower weight losses were observed compared to conventional binder compositions. …”Concrete structures exposed to high levels of chemical attacks are assigned to exposure class XA3, which recommends separate concrete protection or a special expert solution to ensure durability. Due to the partial substitution of Portland cement by blast-furnace slag, an increased resistance to aci...
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986
Optimizing 5G resource allocation with attention-based CNN-BiLSTM and squeeze-and-excitation architecture
出版事項 2025-07-01“…Traditional machine learning models struggle to capture intricate temporal dependencies and handle imbalanced data distributions, limiting their effectiveness in real-world applications.MethodsTo overcome these limitations, this study presents an innovative deep learning-based framework that combines a convolutional layer with squeeze-and-excitation block, bidirectional long short-term memory, and a self-attention mechanism for resource allocation prediction. A custom weighted loss function addresses data imbalance, while Bayesian optimization fine-tunes hyperparameters.ResultsExperimental results demonstrate that the proposed model achieves state-of-the-art predictive accuracy, with a remarkably low Mean Absolute Error (MAE) of 0.0087, Mean Squared Error (MSE) of 0.0003, Root Mean Squared Error (RMSE) of 0.0161, Mean Squared Log Error (MSLE) of 0.0001, and Mean Absolute Percentage Error (MAPE) of 0.0194. …”IntroductionThe swift advancement of computational capabilities has rendered deep learning indispensable for tackling intricate challenges. In 5G networks, efficient resource allocation is crucial for optimizing performance and minimizing latency. Traditional machine learning models struggle to capt...
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987
A Hybrid Quantum-Classical Approach for Multi-Class Skin Disease Classification Using a 4-Qubit Model
出版事項 2025-01-01“…We employ a class-weighted loss function with weights <inline-formula> <tex-math notation="LaTeX">$w_{y_{i}} = [{3.0, 3.5, 0.6, 0.4}]$ </tex-math></inline-formula> to address this imbalance, ensuring balanced representation across all classes. …”Quantum machine learning (QML) presents a promising avenue for addressing complex classification challenges, yet its application in medical imaging remains largely unexplored. This work introduces a hybrid quantum-classical framework designed to classify skin diseases, Chickenpox, Measles, Monkeypox...
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988
Ship Target Detection in SAR Images Based on Multiple Attention Mechanism and Cross-Scale Feature Fusion
出版事項 2025-01-01“…Furthermore, by incorporating the normalized Wasserstein distance (NWD) and combining it with the CIoU loss function, a CIoU-NWD weighted loss function is designed. This reduces the sensitivity of the CIoU loss function to positional offsets of small targets, with only a slight increase in computational and parameter costs, thereby further improving the detection accuracy of small targets. …”Aiming to address the challenges of inefficient target detection in synthetic aperture radar (SAR) images caused by complex backgrounds, small ship targets, and significant scale variations, this article proposes a novel SAR ship target detection model, YOLO-SS, based on YOLOv10n. First, the method...
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989
Evaluating the role of training data origin for country-scale cropland mapping in data-scarce regions: A case study of Nigeria
出版事項 2025-08-01“…Handling class imbalance was also critical, with weighted loss functions improving accuracy by up to 0.071 for the single-headed LSTM. …”Cropland maps are essential for remote sensing-based agricultural monitoring, providing timely insights about agricultural development without requiring extensive field surveys. While machine learning enables large-scale mapping, it relies on geo-referenced ground-truth data, which is time-consuming...
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990
Referenceless 4D flow cardiovascular magnetic resonance with deep learning
出版事項 2025-01-01“…Methods: A U-Net was trained with adversarial learning (U-NetADV) and with a velocity frequency-weighted loss function (U-NetVEL) to predict the reference encoding from the three motion encodings obtained with a non-symmetric velocity-encoding scheme. …”ABSTRACT: Background: Despite its potential to improve the assessment of cardiovascular diseases, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is hampered by long scan times. 4D flow CMR is conventionally acquired with three motion encodings and one reference encoding, as the...
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991
Bismuth ferrite reinforced porous bioactive glass scaffolds: In vitro and antibacterial properties
出版事項 2025-07-01“…The BAG and BAG-BF porous scaffolds exhibited porosities of ∼74 % (BAG), ∼65 % (0.5 BAG-BF), and ∼64 % (1.5 BAG-BF), with post-sintering weight losses of 5 %, 2.5 %, and 5 %, respectively. All samples showed ∼50 % shrinkage. …”Tissue engineering focuses on restoring damaged tissues by strategically integrating cells, bioactive factors, and scaffold materials. Despite significant advancements in biomaterials, developing an ideal scaffold for bone regeneration remains a major challenge. The porous scaffolds aim to provide a...
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992
Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China
出版事項 2025-07-01“…Hyperparameter optimisation was performed using stratified fivefold cross-validation with grid search, incorporating class-weighted loss functions to address class imbalance and model-specific regularisation techniques to maximise performance while preventing overfitting. …”Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced decision-making and targeted health management in integrated medical and older adult care institutions (IMOAC...
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