AIMC Topic: Algorithms

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Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.

IET systems biology
Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elde...

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

Current cardiology reports
PURPOSE OF REVIEW: This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved fo...

Synchronization-Inspired Interpretable Neural Networks.

IEEE transactions on neural networks and learning systems
Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. In...

Clinical Prompt Learning With Frozen Language Models.

IEEE transactions on neural networks and learning systems
When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work sugg...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

IEEE transactions on neural networks and learning systems
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...

MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest.

IEEE transactions on neural networks and learning systems
Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of tradit...

Biologically Plausible Sparse Temporal Word Representations.

IEEE transactions on neural networks and learning systems
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...

A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis.

IEEE transactions on neural networks and learning systems
The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mut...

DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition.

IEEE transactions on neural networks and learning systems
With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning al...

Self-Lateral Propagation Elevates Synaptic Modifications in Spiking Neural Networks for the Efficient Spatial and Temporal Classification.

IEEE transactions on neural networks and learning systems
The brain's mystery for efficient and intelligent computation hides in the neuronal encoding, functional circuits, and plasticity principles in natural neural networks. However, many plasticity principles have not been fully incorporated into artific...