AIMC Topic: Neural Networks, Computer

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KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks.

Methods (San Diego, Calif.)
Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. Howe...

Pair-wise or high-order? A self-adaptive graph framework for knowledge graph embedding.

Neural networks : the official journal of the International Neural Network Society
Knowledge graphs (KGs) depict entities as nodes and connections as edges, and they are extensively utilized in numerous artificial intelligence applications. However, knowledge graphs often suffer from incompleteness, which seriously affects downstre...

Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.

Computer methods in biomechanics and biomedical engineering
Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal ar...

Intelligent Diagnosis of Cervical Lymph Node Metastasis Using a CNN Model.

Journal of dental research
Lymph node (LN) metastasis is a prevalent cause of recurrence in oral squamous cell carcinoma (OSCC). However, accurately identifying metastatic LNs (LNs+) remains challenging. This prospective clinical study aims to test the effectiveness of our con...

ProtoASNet: Comprehensive evaluation and enhanced performance with uncertainty estimation for aortic stenosis classification in echocardiography.

Medical image analysis
Aortic stenosis (AS) is a prevalent heart valve disease that requires accurate and timely diagnosis for effective treatment. Current methods for automated AS severity classification rely on black-box deep learning techniques, which suffer from a low ...

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

Computer methods and programs in biomedicine
BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medic...

Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

Neuroscience
The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning due to memory loss, cognitive decline, and behavioral problems. By using synchrosqueezing representat...

Optimized convolutional neural networks for real-time detection and severity assessment of early blight in tomato (Solanum lycopersicum L.).

Fungal genetics and biology : FG & B
Early blight, caused by Alternaria alternata, poses a critical challenge to tomato (Solanum lycopersicum L.) production, causing significant yield losses worldwide. Despite advancements in plant disease detection, existing methods often lack the robu...

A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.

BMC biology
BACKGROUND: Essential genes are crucial for the development, inheritance, and survival of species. The exploration of these genes can unravel the complex mechanisms and fundamental life processes and identify potential therapeutic targets for various...

Artificial intelligence tool development: what clinicians need to know?

BMC medicine
Digital medicine and smart healthcare will not be realised without the cognizant participation of clinicians. Artificial intelligence (AI) today primarily involves computers or machines designed to simulate aspects of human intelligence using mathema...