AIMC Topic: Neural Networks, Computer

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Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by traditional imaging techniques' frequent struggles with low resolution and noise, especially in Low Dose CT s...

QTypeMix: Enhancing multi-agent cooperative strategies through heterogeneous and homogeneous value decomposition.

Neural networks : the official journal of the International Neural Network Society
In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, the operation of multi-agent s...

A Robust and Efficient Representation-based DNA Storage Architecture by Deep Learning.

Small methods
As one main form of multimedia data, images play a critical role in various applications. In this paper, a representation-based architecture is proposed which takes advantage of the outstanding representation and image-generation abilities of deep le...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

International journal of computer assisted radiology and surgery
PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to ...

H control for fractional order neural networks with uncertainties subject to deception attacks via Improved memory-event-triggered scheme and Its application.

Neural networks : the official journal of the International Neural Network Society
The article discusses an improved memory-event-triggered strategy for H control class of fractional-order neural networks (FONNs) with uncertainties, which are vulnerable to deception attacks. The system under consideration is simultaneously influenc...

Motif-aware curriculum learning for node classification.

Neural networks : the official journal of the International Neural Network Society
Node classification, seeking to predict the categories of unlabeled nodes, is a crucial task in graph learning. One of the most popular methods for node classification is currently Graph Neural Networks (GNNs). However, conventional GNNs assign equal...

Simplified PCNet with robustness.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs. However, they cannot generalize well to real-world graphs with different levels of homophily. In r...

Modeling document causal structure with a hypergraph for event causality identification.

Neural networks : the official journal of the International Neural Network Society
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neura...

Decomposition based neural dynamics for portfolio management with tradeoffs of risks and profits under transaction costs.

Neural networks : the official journal of the International Neural Network Society
Real-time online optimisation plays a crucial role in high-frequency trading (HFT) strategies. The Markowitz model, as a Nobel Prize-winning framework, is widely used for portfolio management optimisation by framing the problem as a constrained quadr...

Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks.

Photodiagnosis and photodynamic therapy
In recent years, interest in applying deep learning (DL) to medical diagnosis has rapidly increased, driven primarily by the development of Convolutional Neural Networks and Transformers. Despite advancements in DL, the automated diagnosis of skin ca...