AIMC Topic: Humans

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Language-based reasoning graph neural network for commonsense question answering.

Neural networks : the official journal of the International Neural Network Society
Language model (LM) has played an increasingly important role in the common-sense understanding and reasoning in the CSQA task (Common Sense Question Answering). However, due to the amount of model parameters, increasing training data helps little in...

Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...

GMNI: Achieve good data augmentation in unsupervised graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph contrastive learning (GCL) shows excellent potential in unsupervised graph representation learning. Data augmentation (DA), responsible for generating diverse views, plays a vital role in GCL, and its optimal choice heavily depends on the downs...

Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting.

Neural networks : the official journal of the International Neural Network Society
Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solu...

BiLSTM-Filt: Neural network for radar word segmentation.

Neural networks : the official journal of the International Neural Network Society
Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electrom...

The Efficacy of a Food Supplement in the Treatment of Tinnitus with Comorbid Headache: A Statistical and Machine Learning Analysis with a Literature Review.

Audiology & neuro-otology
INTRODUCTION: Tinnitus, the perception of sound without an external auditory stimulus, affects approximately 10-15% of the population and is often associated with significant comorbidities such as headaches. These conditions can severely impact the q...

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy.

Magnetic resonance imaging
BACKGROUND: Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults. Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however...

Impact of a clinical pharmacist-led, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings.

Journal of the American Pharmacists Association : JAPhA
BACKGROUND: Chronic diseases are the leading cause of disability and death in the United States. Clinical pharmacists have been shown to optimize health outcomes and reduce health care expenditures in patients with chronic diseases through improving ...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Journal of human hypertension
Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We perf...

Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation.

Journal of neuro-oncology
INTRODUCTION: - Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (AI)-based models have improved the efficiency of these processes. This study evaluated an AI-based mo...