AIMC Topic: Neural Networks, Computer

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VBNet: An end-to-end 3D neural network for vessel bifurcation point detection in mesoscopic brain images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate detection of vessel bifurcation points from mesoscopic whole-brain images plays an important role in reconstructing cerebrovascular networks and understanding the pathogenesis of brain diseases. Existing detection m...

LOss-Based SensiTivity rEgulaRization: Towards deep sparse neural networks.

Neural networks : the official journal of the International Neural Network Society
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of the loss function with respect to the variation of the parameter. Parame...

IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks.

Mathematical biosciences and engineering : MBE
Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network ser...

A relation-based framework for effective teeth recognition on dental periapical X-rays.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dental periapical X-rays are used as a popular tool by dentists for diagnosis. To provide dentists with diagnostic support, in this paper, we achieve automated teeth recognition of dental periapical X-rays by using deep learning techniques, including...

Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentatio...

Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.

Medical physics
PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomica...

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention.

Journal of chemical information and modeling
Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired multiple target conditions based on a deep understanding o...

Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.

Journal of the American Heart Association
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we...

GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network.

Sensors (Basel, Switzerland)
Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for micros...

Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard.

Sensors (Basel, Switzerland)
The multiclass prediction approach to the problem of recognizing the state of the drill by classifying images of drilled holes into three classes is presented. Expert judgement was made on the basis of the quality of the hole, by dividing the collect...