AIMC Topic: Neural Networks, Computer

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Sound Event Detection by Pseudo-Labeling in Weakly Labeled Dataset.

Sensors (Basel, Switzerland)
Weakly labeled sound event detection (WSED) is an important task as it can facilitate the data collection efforts before constructing a strongly labeled sound event dataset. Recent high performance in deep learning-based WSED's exploited using a segm...

Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling.

Sensors (Basel, Switzerland)
Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major componen...

The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.

Sensors (Basel, Switzerland)
Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait rec...

Spine Medical Image Segmentation Based on Deep Learning.

Journal of healthcare engineering
The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image s...

D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images.

Computational intelligence and neuroscience
Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the...

Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

Psychometrika
Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not id...

Deep learning aided quantitative analysis of anti-tuberculosis fixed-dose combinatorial formulation by terahertz spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Anti-tuberculosis fixed-dose combinatorial formulation (FDCs) is an effective drug for the treatment of tuberculosis. As a compound medicine, its efficacy is based on the comprehensive action of multiple main ingredients. If the content of an active ...

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network.

Computers in biology and medicine
Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-...

A CNN-transformer hybrid approach for decoding visual neural activity into text.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the o...

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.

Computers in biology and medicine
Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly ...