This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. Patients with sepsis were extracted from the Medical Information M...
Effective speech emotion recognition (SER) poses a significant challenge due to the intricate and subjective nature of human emotions. Recognizing emotional states accurately from speech signals has a broad spectrum of practical applications, such as...
Single-cell spatial transcriptomics can provide subcellular resolution for a deep understanding of molecular mechanisms. However, accurate segmentation and annotation remain a major challenge that limits downstream analysis. Current machine learning ...
Visual Simultaneous Localization and Mapping (VSLAM) is the key technology for autonomous navigation of mobile robots. However, feature-based VSLAM systems still face two major challenges in dynamic complex environments: insufficient feature reliabil...
The safety and reliability of chemical equipment are crucial to industrial production, as they directly impact production efficiency, environmental protection, and personnel safety. However, traditional fault detection techniques often exhibit limita...
Manual interpretation of sedimentary structures in core-based analyses is critical for understanding subsurface geology but remains time-intensive, expert-dependent, and susceptible to bias. This study investigates the use of convolutional neural net...
Accurate eye detection in thermal images is essential for diverse applications, including biometrics, healthcare, driver monitoring, and human-computer interaction. However, achieving this accuracy is often hindered by the inherent limitations of the...
Several computational methods have been developed to construct single-cell pseudotime embeddings for extracting the temporal order of transcriptional cell states from time-series scRNA-seq datasets. However, existing methods suffer from low predictiv...
OBJECTIVES: This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.
UNLABELLED: CT-based opportunistic screening using artificial intelligence finds a high prevalence (43%) of osteoporosis in CT scans obtained for planning of transcatheter aortic valve replacement. Thus, opportunistic screening may be a cost-effectiv...
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