AIMC Topic: Neural Networks, Computer

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Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack.

PloS one
In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and netw...

Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.

PloS one
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of im...

Advanced deep learning algorithms in oral cancer detection: Techniques and applications.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
As the 16 most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of...

An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
While self-labeled methods can exploit unlabeled and labeled instances to train classifiers, they are also restricted by the labeled instance number and distribution. SEG-SSC, k-means-SSC, LC-SSC, and LCSEG-SSC are sophisticated solutions for overcom...

Simplified self-supervised learning for hybrid propagation graph-based recommendation.

Neural networks : the official journal of the International Neural Network Society
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenge...

Physics-informed Neural Implicit Flow neural network for parametric PDEs.

Neural networks : the official journal of the International Neural Network Society
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric...

GAN-based data reconstruction attacks in split learning.

Neural networks : the official journal of the International Neural Network Society
Due to the distinctive distributed privacy-preserving architecture, split learning has found widespread application in scenarios where computational resources on the client side are limited. Unlike clients in federated learning retaining the whole mo...

DVPT: Dynamic Visual Prompt Tuning of large pre-trained models for medical image analysis.

Neural networks : the official journal of the International Neural Network Society
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only ta...

PFENet: Towards precise feature extraction from sparse point cloud for 3D object detection.

Neural networks : the official journal of the International Neural Network Society
Accurate 3D point cloud object detection is crucially important for autonomous driving vehicles. The sparsity of point clouds in 3D scenes, especially for smaller targets like pedestrians and bicycles that contain fewer points, makes detection partic...

Towards parameter-free attentional spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Brain-inspired spiking neural networks (SNNs) are increasingly explored for their potential in spatiotemporal information modeling and energy efficiency on emerging neuromorphic hardware. Recent works incorporate attentional modules into SNNs, greatl...