AIMC Topic: Neural Networks, Computer

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Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry.

Bulletin of experimental biology and medicine
The model of artificial intelligence DENTOMO, which allows automated deciphering the CT in maxillofacial area, was developed and implemented in practical dentistry. The presented model is based on two convolutional neural networks, includes a databas...

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.

PloS one
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with differ...

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Journal of dental research
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to se...

ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs.

Journal of computational chemistry
This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular ...

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Sensors (Basel, Switzerland)
Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose...

A deep learning segmentation strategy that minimizes the amount of manually annotated images.

F1000Research
Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance ...

SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation.

BioMed research international
Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardl...

Optimal strategy of sEMG feature and measurement position for grasp force estimation.

PloS one
Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to inc...

Adaptive dimensionality reduction for neural network-based online principal component analysis.

PloS one
"Principal Component Analysis" (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-call...

A novel recurrent neural network approach in forecasting short term solar irradiance.

ISA transactions
Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar ir...