AIMC Topic: Neural Networks, Computer

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Empirical study of daily link traffic volume forecasting based on a deep neural network.

PloS one
Forecasting the daily link traffic volume is critical in transportation demand analysis in feasibility studies for planning transportation facilities. The high purchase and maintenance cost of commercial transport planning software poses a challenge ...

Double reinforcement learning for cluster synchronization of Boolean control networks under denial of service attacks.

PloS one
This paper investigates the asymptotic cluster synchronization of Boolean control networks (BCNs) under denial-of-service (DoS) attacks, where each state node in the network experiences random data loss following a Bernoulli distribution. First, the ...

DCGCN: Dual-Channel Graph Convolutional Network-Based Drug-Target Interaction Prediction Method with 3D Molecular Structure.

Journal of chemical information and modeling
Exploring drug-target interactions (DTIs) is crucial for drug discovery. Most existing methods for predicting DTIs rely solely on the linear structures of molecules, such as SMILES or the amino acid sequence. However, these linear features fail to re...

Universal dimensions of visual representation.

Science advances
Do visual neural networks learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they share universal features of natural image processing? We characterized the universal...

Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.

BMC medical imaging
To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasoning method. It combines hierarchical attention convolutional networks with a multi-label decoupling lo...

Spatial attention-guided pre-trained networks for accurate identification of crop diseases.

Scientific reports
The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions...

A human activity recognition model based on deep neural network integrating attention mechanism.

Scientific reports
Human Activity Recognition (HAR) is crucial in multiple fields. Existing HAR techniques include manual feature extraction, codebook-based methods, and deep learning, each with limitations. This paper presents DCAM-Net (DeepConvAttentionMLPNet), a nov...

LSTM autoencoder based parallel architecture for deepfake audio detection with dynamic residual encoding and feature fusion.

Scientific reports
With the rapid advancement of synthetic speech technologies, detecting deepfake audio has become essential for preventing impersonation and misinformation. This study aims to enhance detection performance by addressing limitations in existing models,...

A dual path graph neural network framework for dementia diagnosis.

Scientific reports
Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. However, leveraging the complementary temporal,...

Optimizing visual data retrieval using deep learning driven CBIR for improved human machine interaction.

Scientific reports
Content-based image retrieval (CBIR) systems have formidable obstacles in connecting human comprehension with machine-driven feature extraction due to the exponential expansion of visual data across many areas. Robust performance across varied datase...