The interplay of biotic and abiotic factors driving Ixodes ricinus abundance trends are not fully understood. Machine learning (ML) approaches are being increasingly used to explore this and predict future abundance patterns of this species, however,...
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind directi...
The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective trans...
Medical & biological engineering & computing
Jan 22, 2025
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive ...
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
The current few-shot relational triple extraction (FS-RTE) techniques, which rely on prototype networks, have made significant progress. Nevertheless, the scarcity of data in the support set results in both intra-class and inter-class gaps in FS-RTE....
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Reconstruction-based methods achieve promising performance for visual anomaly detection (AD), relying on the underlying assumption that the anomalies cannot be accurately reconstructed. However, this assumption does not always hold, especially when s...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantify...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept dr...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, an...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.