AIMC Topic: Neural Networks, Computer

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Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses.

Artificial intelligence in medicine
BACKGROUND: Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic healt...

Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches.

Computers in biology and medicine
BACKGROUND: Clinical trials are essential in medical science and are currently the most robust strategy for evaluating the effectiveness of a treatment. However, some of these studies are less reliable than others due to flaws in their design. Assess...

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images.

Tuberculosis (Edinburgh, Scotland)
Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other people. The chest X-ray (CXR) is the top choice for lung disease screening in clinics because it is ...

Feature-Attention Graph Convolutional Networks for Noise Resilient Learning.

IEEE transactions on cybernetics
Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the m...

Visual Relationship Detection: A Survey.

IEEE transactions on cybernetics
Visual relationship detection (VRD) is one newly developed computer vision task, aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition, and is important for fully understandi...

Adaptive Dense Ensemble Model for Text Classification.

IEEE transactions on cybernetics
Text classification has been widely explored in natural language processing. In this article, we propose a novel adaptive dense ensemble model (AdaDEM) for text classification, which includes local ensemble stage (LES) and global dense ensemble stage...

Learning From Negative Links.

IEEE transactions on cybernetics
Recently, graph convolutional networks (GCNs) and their variants have achieved remarkable successes for the graph-based semisupervised node classification problem. With a GCN, node features are locally smoothed based on the information aggregated fro...

Inner-Imaging Networks: Put Lenses Into Convolutional Structure.

IEEE transactions on cybernetics
Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the compleme...

Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination.

IEEE transactions on cybernetics
In this article, a robust k -winner-take-all ( k -WTA) neural network employing the saturation-allowed activation functions is designed and investigated to perform a k -WTA operation, and is shown to possess enhanced robustness to disturbance compare...

Indoor Place Category Recognition for a Cleaning Robot by Fusing a Probabilistic Approach and Deep Learning.

IEEE transactions on cybernetics
Indoor place category recognition for a cleaning robot is a problem in which a cleaning robot predicts the category of the indoor place using images captured by it. This is similar to scene recognition in computer vision as well as semantic mapping i...