Document parsing unveiled: Techniques, challenges, and prospects for structured information extraction
Document parsing is essential for converting unstructured and semi-structured documents-
such as contracts, academic papers, and invoices-into structured, machine-readable data …
such as contracts, academic papers, and invoices-into structured, machine-readable data …
Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model
Determining the correct mushroom species with the necessary ecological characteristics is
critical to continue mushroom production, which is essential in gastronomy. The mushroom …
critical to continue mushroom production, which is essential in gastronomy. The mushroom …
An improved RIME optimization algorithm for lung cancer image segmentation
L Guo, L Liu, Z Zhao, X Xia - Computers in Biology and Medicine, 2024 - Elsevier
Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate
diagnosis for successful treatment. Within medical imaging processing, image segmentation …
diagnosis for successful treatment. Within medical imaging processing, image segmentation …
Feature selection in high-dimensional data: an enhanced RIME optimization with information entropy pruning and DBSCAN clustering
H Wu, Y Chen, W Zhu, Z Cai, AA Heidari… - International Journal of …, 2024 - Springer
When confronted with high-dimensional data, evolutionary feature selection methods
encounter the formidable challenge known as the “curse of dimensionality”. To overcome …
encounter the formidable challenge known as the “curse of dimensionality”. To overcome …
[HTML][HTML] Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships
T Wang, C Xue, Z Zhang, T Cheng, G Yang - Computers in Biology and …, 2024 - Elsevier
Objective Depression and anxiety, prevalent coexisting mood disorders, pose a clinical
challenge in accurate differentiation, hindering effective healthcare interventions. This …
challenge in accurate differentiation, hindering effective healthcare interventions. This …
Multi-residual 2D network integrating spatial correlation for whole heart segmentation
Y Huang, J Yang, Q Sun, Y Yuan, H Li, Y Hou - Computers in Biology and …, 2024 - Elsevier
Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy,
modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on …
modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on …
Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study
C Zheng, P Yue, K Cao, Y Wang, C Zhang, J Zhong… - Abdominal …, 2024 - Springer
Purpose To develop and validate a nomogram model that combines radiomics features,
clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss …
clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss …
Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization
K Zhang, Y He, Y Wang, C Sun - Biomimetics, 2024 - mdpi.com
The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that
has been proposed in recent years. The algorithm optimizes the search ability of individuals …
has been proposed in recent years. The algorithm optimizes the search ability of individuals …