AIMC Topic: Deep Learning

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Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.

ACS sensors
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevan...

Applying MLP-Mixer and gMLP to Human Activity Recognition.

Sensors (Basel, Switzerland)
The development of deep learning has led to the proposal of various models for human activity recognition (HAR). Convolutional neural networks (CNNs), initially proposed for computer vision tasks, are examples of models applied to sensor data. Recent...

Deep Learning Approaches for the Prediction of Protein Functional Sites.

Molecules (Basel, Switzerland)
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty...

Enhancing repeatability of follicle counting with deep learning reconstruction high-resolution MRI in PCOS patients.

Scientific reports
Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ultrasonography compared to MRI. Nevertheless, the repeatability of follicle counting using traditio...

Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments.

Scientific reports
Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes....

Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain.

Scientific reports
The motivation for this article stems from the fact that medical image security is crucial for maintaining patient confidentiality and protecting against unauthorized access or manipulation. This paper presents a novel encryption technique that integ...

SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.

Scientific reports
Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and fu...

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

Scientific reports
In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease detected among people. This cancer is the uncontrolled evolution of atypical skin cells. It occurs when...

Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.

Scientific reports
BACKGROUND AND OBJECTIVES: For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft, along with the fibula graft, has become established as a preferred donor region. While computer-as...

Liver fibrosis stage classification in stacked microvascular images based on deep learning.

BMC medical imaging
BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...