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

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.

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 Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7996
Author(s):  
Xiangbing Zhan ◽  
Huiyun Long ◽  
Fangfang Gou ◽  
Xun Duan ◽  
Guangqian Kong ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Decision Making ◽  
Small Cell Lung Cancer ◽  
Convolutional Neural Network ◽  
Cell Lung Cancer ◽  
Medical Records ◽  
Small Cell ◽  
Medical System ◽  
Small Cell Lung

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

scholarly journals Behaviour Based Data Dispatcher

2019 ◽  
Vol 8 (4) ◽  
pp. 12940-12944
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Human Life ◽  
The Other ◽  
Facial Emotion

Human life is a complex social structure. It is not possible for the humans to navigate without reading the other persons. They do it by identifying the faces. The state of response can be decided based on the mood of the opposite person. Whereas a person’s mood can be figured out by observing his emotion (Facial Gesture). The aim of the project is to construct a “Facial emotion Recognition” model using DCNN (Deep convolutional neural network) in real time. The model is constructed using DCNN as it is proven that DCNN work with greater accuracy than CNN (convolutional neural network). The facial expression of humans is very dynamic in nature it changes in split seconds whether it may be Happy, Sad, Angry, Fear, Surprise, Disgust and Neutral etc. This project is to predict the emotion of the person in real time. Our brains have neural networks which are responsible for all kinds of thinking (decision making, understanding). This model tries to develop these decisions making and classification skills by training the machine. It can classify and predict the multiple faces and different emotions at the very same time. In order to obtain higher accuracy, we take the models which are trained over thousands of datasets.

scholarly journals TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design For Detection of Tuberculosis Cases From Chest X-Ray Images

2021 ◽  
Author(s):  
Alexander Wong ◽  
James Lee ◽  
Hadi Rahmat-Khah ◽  
Ali Sabri ◽  
Amer Alaref
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Case Detection ◽  
Critical Factors ◽  
X Ray ◽  
Patient Cohort ◽  
National Patient ◽  
Chest X Ray

Abstract Background: Tuberculosis (TB) remains a global health problem, and is the leading cause of death from an infectious disease. A crucial step in the treatment of tuberculosis is screening high risk populations and the early detection of the disease, with chest x-ray (CXR) imaging being the most widely-used imaging modality. As such, there has been significant recent interest in artificial intelligence-based TB screening solutions for use in resource-limited scenarios where there is a lack of trained healthcare workers with expertise in CXR interpretation. Motivated by this pressing need and the recent recommendation by the World Health Organization (WHO) for the use of computer-aided diagnosis of TB in place of a human reader, we introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening.Methods: We used CXR data from a multi-national patient cohort to train and test our models. A machine-driven design exploration approach leveraging generative synthesis was used to build a highly customized deep neural network architecture with attention condensers.Results: We conducted an explainability-driven performance validation process to validate TB-Net's decision-making behaviour. Experiments on CXR data from a multi-national patient cohort showed that the proposed TB-Net is able to achieve accuracy/sensitivity/specificity of 99.86%/100.0%/99.71%. Radiologist validation was conducted on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed consistency between radiologist interpretation and critical factors leveraged by TB-Net for TB case detection for the case where radiologists identified anomalies.Conclusion: The proposed TB-Net not only achieves high tuberculosis case detection performance in terms of sensitivity and specificity, but also leverages clinically relevant critical factors in its decision making process. While not a production-ready solution, we hope that the open-source release of TB-Net as part of the COVID-Net initiative will support researchers, clinicians, and citizen data scientists in advancing this eld in the ght against this global public health crisis.

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