Results 251 to 260 of about 183,434 (340)

Dual‐Scale Transformer Fusion With Meta Learning for Micro Metastasis Detection in Thyroid Cancer

Advanced Intelligent Systems, EarlyView.
A dual‐scale transformer model enhanced by meta‐learning enables accurate detection of tiny metastatic lesions in thyroid cancer. By combining cellular and tissue‐level features, the method outperforms existing models and shows strong adaptability to rare cases with limited data.
Jingtao Wang, Junhai Shi, Hengli Ni, Limei Zhao, Jing Huang, Jianming Li   +5 more
wiley   +1 more source

EnCTN: an enhanced AI-enabled deep learning framework for security enhancement in blockchain transactions. [PDF]

Sci Rep
Bhuvaneshwari P, Krishnaveni A, Robinson YH, Julie EG.   +3 more
europepmc   +1 more source

Combinatorial SiO2‐Encapsulated Quantum Dot Nanoparticles and their Use in Spectral Unmixing Analysis

Analysis &Sensing, EarlyView.
Combinatorial QD‐SiO2 nanoparticles combined with linear unmixing of the photoluminescence spectrum increase the multiplexity of assays. Linear unmixing spectral analysis is a technique where signals from tens of fluorophores can be deconvoluted to increase multiplexing by 4–5‐fold.
Yuwei Wang, Jennifer I. L. Chen
wiley   +1 more source

Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study. [PDF]

BioData Min
Lukyanenko P, Mayourian J, Liu M, Triedman JK, Ghelani SJ, La Cava WG.   +5 more
europepmc   +1 more source

Harnessing Machine Learning and Deep Learning Approaches for Laser‐Induced Breakdown Spectroscopy Data Analysis: A Comprehensive Review

Analysis &Sensing, EarlyView.
Laser‐induced breakdown spectroscopy (LIBS), an atomic emission technique, is widely applied in fields like geology and biology. This rapid elemental analysis method leverages computational tools to boost precision and speed up data processing. This review explores machine learning and deep learning methods for analyzing LIBS spectral data, tackling ...
Pegah Dehbozorgi, Ludovic Duponchel, Vincent Motto‐Ros, Thomas Bocklitz   +3 more
wiley   +1 more source

Sentinel-Net for anomaly events detection of meteorological stations monitoring. [PDF]

Sci Rep
Li W, Wang B, Qin Q, Zhang Y, Zhao D, Ge J.   +5 more
europepmc   +1 more source

Hierarchical Complementary Enhanced Autoencoder Integrating Spatio‐Temporal Interaction Feature for Soft Sensor

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT To address the issues of neglecting the spatiotemporal correlations among process variables, low‐level features are vulnerable to noise interference, and the gradual loss of key information layer by layer during deep network training in traditional stacked autoencoder‐based soft‐sensor models, this paper proposes a hierarchical complementary ...
Xiaoping Guo, Jinghong Guo, Yuan Li
wiley   +1 more source

Retraction notice to “TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping” [Computers & Security 144 (2024) 103937]

Sicong Li, Jian Wang, Yafei Song, Shuo Wang   +3 more
openalex   +1 more source

Digital image quality evaluation based on multi-scale aesthetic features and graph convolutional neural networks. [PDF]

Sci Rep
Wu J, Li D.
europepmc   +1 more source

Generative Deep Learning for Advanced Battery Materials

Batteries &Supercaps, EarlyView.
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal, Adrian Cierpka, Britta Nestler, Arnd Koeppe   +3 more
wiley   +1 more source