AIMC Topic: Neural Networks, Computer

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Research on computational propagation and identification of mine microseismic signals based on deep learning.

PloS one
In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion. However, when dealing with suc...

A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking.

PloS one
BACKGROUND: Pneumonia is a major cause of mortality among children under five and adults over 65, especially in low-resource settings where access to skilled radiologists is limited. Accurate and early diagnosis is essential, but is often hindered by...

Descattering and image restoration with a transformer-based neural network in deep tissue imaging.

Proceedings of the National Academy of Sciences of the United States of America
Imaging biological structures deep inside tissues is crucial but challenging due to common light scattering. This study proposes a multiattention network that directly maps degraded scattering two-photon excitation fluorescence (TPEF) images to high-...

A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction.

Scientific reports
Sparse numerical datasets are dominant in fields such as applied mathematics, astronomy, finance, and healthcare, presenting challenges due to their high dimensionality and sparse distribution. The predominance of zero values complicates optimal feat...

Enhancing indoor monitoring of visually impaired people using temporal convolutional network with optimization model in IoT environment.

Scientific reports
The Internet of Things (IoT) has emerged as a powerful technology in various fields, including healthcare, assisting the elderly and disabled individuals. Solution-based IoT is widely utilized in healthcare support in diverse aspects of their daily l...

Isometric representations in neural networks improve robustness.

Scientific reports
Artificial and biological agents are unable to learn given completely random and unstructured data. The structure of data is encoded in the distance or similarity relationships between data points. In the context of neural networks, the neuronal acti...

Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.

Scientific reports
Precise segmentation of brain tumors is essential for efficient diagnosis and therapy planning. While current automated methods frequently fail to capture complicated tumor shapes, traditional manual methods are laborious, subjective, and unpredictab...

Multi-strategy dung beetle optimization for robust indoor object detection and tracking for visually impaired people with hybrid deep learning networks.

Scientific reports
Visually impaired people generally face many troubles in their everyday lives, and technical involvement might help them perform these tasks. Object detection is a significant aspect of computer vision (CV) and machine learning (ML), which plays a su...

Implementing ensemble of deep learning model with optimization techniques for human activity recognition to assist individuals with disabilities.

Scientific reports
Recent human activity recognition (HAR) developments have allowed numerous applications like healthcare, smart homes, and improved manufacturing. Activity recognition plays a crucial part in improving human well-being by capturing behavioral data, en...

DANet a lightweight dilated attention network for malaria parasite detection.

Scientific reports
Malaria remains a critical global health challenge, requiring accurate and efficient diagnostic tools, particularly in developing countries with limited medical expertise. Detecting malaria parasites from red blood cell (RBC) blood smear images is ch...