Results 151 to 160 of about 320,754 (285)

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

Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose

open access: yesAdvanced Intelligent Systems, EarlyView.
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk   +4 more
wiley   +1 more source

Enhancing Atomic‐Resolution in Electron Microscopy: A Deep Learning Denoiser Operating in the Frequency Domain

open access: yesAdvanced Intelligent Systems, EarlyView.
The newly developed AI‐automated Fast Fourier Transform denoising algorithm surpasses conventional real‐space methods by revealing even light atoms otherwise hidden in noisy backgrounds. Atomic resolution electron microscopy has become an essential tool for many scientific fields, when direct visualization of atomic arrangements and defects is needed ...
Ivan Pinto‐Huguet   +8 more
wiley   +1 more source

Human‐Machine Mutual Trust Based Shared Control Framework for Intelligent Vehicles

open access: yesAdvanced Intelligent Systems, EarlyView.
This work introduces a bidirectional‐trust‐driven shared control framework for human‐machine co‐driving. The method models human‐to‐machine trust from intention discrepancies and Bayesian skill assessment, and machine‐to‐human trust from integrated ability evaluation.
Zhishuai Yin   +4 more
wiley   +1 more source

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

open access: yesAdvanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera   +2 more
wiley   +1 more source

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