Results 211 to 220 of about 472,142 (266)

Loss‐Based Ensemble Generative Adversarial Network Model for Enhancing the Sperm Morphology Classification

open access: yesAdvanced Intelligent Systems, EarlyView.
A loss‐based ensemble generative adversarial network (GAN) framework is proposed to address mode collapse in sperm morphology classification. By integrating spatial augmentation and multiple GAN models, the study enhances synthetic data quality. The Shifted Window Transformer achieves 95.37% accuracy on the HuSHeM dataset, outperforming previous ...
Berke Cansiz   +2 more
wiley   +1 more source

Response to “Sequential Portal Vein–Hepatic Vein Embolization: Progress Yet Unaccounted Pitfalls”

open access: yes
Annals of Gastroenterological Surgery, EarlyView.
Thanh Tung Lai, Masaki Kaibori
wiley   +1 more source

Neonatal Respiration Monitoring System with Synchronized Oxygen Supply and Machine Learning‐Based Breathing Classification

open access: yesAdvanced Intelligent Systems, EarlyView.
The study presents a low‐cost, noninvasive system for real‐time neonatal respiratory monitoring. A flexible, screen‐printed sensor patch captures chest movements with high sensitivity and minimal drift. Combined with machine learning, the system accurately detects breathing patterns and offers a practical solution for neonatal care in low‐resource ...
Gitansh Verma   +3 more
wiley   +1 more source

BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models

open access: yesAdvanced Intelligent Systems, EarlyView.
This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan   +7 more
wiley   +1 more source

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

open access: yesAdvanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang   +4 more
wiley   +1 more source

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