A new approach for vaginal microbial micrograph classification using convolutional neural network combined with decision-making tree (CNN-DMT)

2019 ◽  
Author(s):  
Kongya Zhao ◽  
Hao He ◽  
Peng Gao ◽  
Sunxiangyu Liu ◽  
Xinyan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Convolutional Neural Network ◽  
New Approach

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

Maximum Gradient Decision-Making for Railways Based on Convolutional Neural Network

2019 ◽  
Vol 145 (11) ◽  
pp. 04019047 ◽  
Author(s):  
Hao Pu ◽  
Hong Zhang ◽  
Paul Schonfeld ◽  
Wei Li ◽  
Jie Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Convolutional Neural Network ◽  
Maximum Gradient

On Transfer Learning for Classifying COVID-19 in Chest X-Ray Images

2020 ◽  
Author(s):  
Elilson Santos ◽  
Lúcio Flavio De Jesus Silva ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Decision Making ◽  
Severe Acute Respiratory Syndrome ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Decision Making Process ◽  
Network Architectures ◽  
X Ray ◽  
Chest X Ray

COVID-19 is an exceptionally infectious disease caused by severe acute respiratory syndrome. The illness has spread itself worldwide rapidly and can lead to death only in a few days. In this context, investigating fast ways of detection that help physicians in the decision-making process is essential to help in the task of saving lives. This work investigates fourteen convolutional neural network architectures using transfer learning. We used a database composed of 2,928 x-ray images divided into three classes: Normal, COVID-19, and Viral Pneumonia. Results showed that DenseNet169 presented the best results regarding classification reaching a mean accuracy of 94%, a precision of 97.6%, a recall of 95.6%, and an F1-score of 96,1%, approximately.

Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

2020 ◽  
pp. 154851292095454
Author(s):  
Junfeng Zhang ◽  
Qing Xue
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Difficult Problem ◽  
Learning Method ◽  
Rule Based ◽  
Autonomous Decision ◽  
Decision Making Problem

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

scholarly journals A Hybrid Convolutional Neural Network-Gradient Boosted Classifier for Vehicle Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 213-216
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transport System ◽  
Traffic Management ◽  
Vehicle Tracking ◽  
Vehicle Classification ◽  
Intelligent Transport System ◽  
Hybrid Classifier ◽  
New Approach ◽  
Intelligent Transport

Vehicle tracking and classification are used for intelligent transport system to provide data in terms of traffic management, routing, vehicle volume and others. A new approach will be introduced in this paper, a hybrid classifier that would detect vehicles that would be adaptable to Philippine settings. A combination of convolutional neural network and gradient boosted classifier would boost the classifying accuracy. In the discussion, CNN has outperformed other classifier in terms of accuracy while GBC got the highest AUROC and highest accuracy in terms of classifying. Although CNN and GBC is prone to overfitting, the dataset that will be used contains 1 hour of video.

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