AIMC Topic: Algorithms

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Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting.

Neural networks : the official journal of the International Neural Network Society
In data analysis and forecasting, particularly for multivariate long-term time series, challenges persist. The Transformer model in deep learning methods has shown significant potential in time series forecasting. The Transformer model's dot-product ...

High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy.

Bioresource technology
Cellulose and hemicellulose are key cross-linked carbohydrates affecting bioethanol production in maize stalks. Traditional wet chemical methods for their detection are labor-intensive, highlighting the need for high-throughput techniques. This study...

Identification of four novel acute-on-chronic liver failure clusters with distinct clinical trajectories and mortality using machine learning methods.

Alimentary pharmacology & therapeutics
BACKGROUND AND AIMS: Machine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute-on-chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs.

International journal of molecular sciences
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach...

The hard problem of meta-learning is what-to-learn.

The Behavioral and brain sciences
Binz et al. highlight the potential of meta-learning to greatly enhance the flexibility of AI algorithms, as well as to approximate human behavior more accurately than traditional learning methods. We wish to emphasize a basic problem that lies under...

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.

Nature computational science
Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability...

DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.

Frontiers in endocrinology
INTRODUCTION: Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic meth...

Towards sustainable coastal management: aerial imagery and deep learning for high-resolution mapping.

PeerJ
The massive arrival of pelagic on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of in the o...

A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods o...