AIMC Topic: Semantics

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Enhancing diagnosis prediction with adaptive disease representation learning.

Artificial intelligence in medicine
Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records. The time series model can better capture the temporal progression relationship of patient diseases...

DER-GCN: Dialog and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialog Emotion Recognition.

IEEE transactions on neural networks and learning systems
With the continuous development of deep learning (DL), the task of multimodal dialog emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional infor...

Author name disambiguation based on heterogeneous graph neural network.

PloS one
With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguation is at a historic peak, the number of paper authors is increasing every year, and the situation o...

Chinese medical named entity recognition utilizing entity association and gate context awareness.

PloS one
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information ...

Adversarial perturbation and defense for generalizable person re-identification.

Neural networks : the official journal of the International Neural Network Society
In the Domain Generalizable Person Re-Identification (DG Re-ID) task, the quality of identity-relevant descriptor is crucial for domain generalization performance. However, for hard-matching samples, it is difficult to separate high-quality identity-...

Open-world semi-supervised relation extraction.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the cl...

A simple clustering approach to map the human brain's cortical semantic network organization during task.

NeuroImage
Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. Howev...

ZS-MNET: A zero-shot learning based approach to multimodal named entity typing.

Neural networks : the official journal of the International Neural Network Society
The task of named entity typing (NET) on social platforms is significant as it involves identifying the various types of named entities within unstructured text. The existing methods for NET only utilize the text modality to classify the types of nam...

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer.

Scientific reports
Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to u...

A text-speech multimodal Chinese named entity recognition model for crop diseases and pests.

Scientific reports
Named Entity Recognition for crop diseases and pests (NER-CDP) is significant in agricultural information extraction and offers vital data support for subsequent knowledge services and retrieval. However, existing NER-CDP methods rely heavily on plai...