AIMC Topic: Neural Networks, Computer

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An improved path planning algorithm based on artificial potential field and primal-dual neural network for surgical robot.

Computer methods and programs in biomedicine
Safety and accuracy are essential for path planning in a surgical navigation system. In this paper, an improved path planning algorithm is proposed to increase the autonomous level of spine surgery robots for higher safety and accuracy. Firstly, the ...

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

IEEE transactions on visualization and computer graphics
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functio...

Visual Analytics for RNN-Based Deep Reinforcement Learning.

IEEE transactions on visualization and computer graphics
Deep reinforcement learning (DRL) targets to train an autonomous agent to interact with a pre-defined environment and strives to achieve specific goals through deep neural networks (DNN). Recurrent neural network (RNN) based DRL has demonstrated supe...

Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this pape...

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EE...

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis.

Sensors (Basel, Switzerland)
The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spo...

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.

Sensors (Basel, Switzerland)
Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-l...

Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach.

PloS one
The purpose of this study is to explore two concepts: first, the use of artificial neural networks (ANN) to forecast the base pressure (β) and wall pressure (ω) originating from a suddenly expanded flow field at subsonic Mach numbers. Second, the imp...

Rapid proximate analysis of coal based on reflectance spectroscopy and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slo...

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

Environmental science and pollution research international
As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various typ...