Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Shaoqin Lu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Assessment ◽  
Supply Chain ◽  
Semantic Network ◽  
System Structure ◽  
Supply Chain Risk ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Risk Knowledge

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.

scholarly journals Reduction of transients in switches using embedded machine learning

2020 ◽  
Vol 11 (1) ◽  
pp. 235
Author(s):  
P. Suresh ◽  
S. George Fernandez ◽  
S. Vidyasagar ◽  
V. Kalyanasundaram ◽  
K. Vijayakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feedback System ◽  
Network Algorithm ◽  
Non Linear ◽  
Neural Network Algorithm ◽  
Electronic Switches

<p>Non-linear loads can cause transients in electronic switches. They also result in a fluctuating output when the device is switched ON or OFF. These transients can harm not only the switches but also the devices that they are connected to, by passing excess currents or voltages to the devices. By applying machine learning, we can improve the gate drive voltages of the switches and thereby reduce switch transients. A feedback system is built that measures the output transients and then feeds it to a neural network algorithm that then gives a proper gate drive to the device. This will reduce transients and also improve performances of switch based devices like inverters and converters.</p>

Method for Evaluation of Railway Dynamic Safety Based on 5M Model and Neural Network Algorithm

2015 ◽  
Vol 743 ◽  
pp. 445-449 ◽  
Author(s):  
G. Wang ◽  
Xiao Qing Zeng ◽  
J. Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Safety Assessment ◽  
Dynamic Assessment ◽  
Development Trend ◽  
Rail Transit ◽  
Network Algorithm ◽  
Railway Safety ◽  
Neural Network Algorithm ◽  
The Development Trend

Traditional risk assessment methods, mainly static analysis, can’t anticipate the development trend and consequences of accidents. This paper firstly presents 5M safety model, combined with the characteristics of the rail transit safety assessment, including complexity, dynamic, ambiguity, etc. By using neural network algorithm, this paper proposes a method of railway safety dynamic assessment. Finally, the method is validated by comparing the output value of the model and the veritable value. The result indicated that it has advantages of real time and predictability.

scholarly journals Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

2020 ◽  
Author(s):  
Aravind Akella ◽  
Vibhor Kaushik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Network Model ◽  
Neural Network Model ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Artery Disease

AbstractThe development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” All six ML algorithms achieved accuracies greater than 80%, with the “Neural Network” algorithm achieving accuracy greater than 93%. The recall achieved with the “Neural Network” model is also highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true positive percentage” achieved with this model. We also extracted the variables of importance in the “Neural Network” model to help in the risk assessment. We have released the full computer code generated in this study in the public domain as a preliminary effort toward developing an open solution for predicting the presence of coronary artery disease in a given population and present a workflow model for implementing a possible solution.

Computer public course teaching based on improved machine learning and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Jingxin Cao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
College Education ◽  
Colleges And Universities ◽  
Point Of View ◽  
Basic Knowledge ◽  
Network Algorithm ◽  
Computer Course ◽  
Neural Network Algorithm ◽  
Computer Courses

With the continuous progress of the times, the development of college education is also constantly tending to enrich and diversify. In the course of curriculum setting in many colleges and universities, more and more attention is paid to the teaching of computer courses for college students. In the course of setting up and teaching, we still follow the traditional teaching mode and do not pay more attention to students’ practical practice. By observing the computer course materials selected by many colleges and universities for students, we can see that most of the textbooks still focus on arranging some exercises from the point of view that science and engineering are students, and lack the basic knowledge of the curriculum. Because the understanding and research of computer course is not deep enough, the teaching effect obtained since the course is not ideal. By studying the relevant knowledge of machine learning and some important problems in the development of neural network algorithm theory, this paper puts forward some viewpoints based on the current curriculum system in colleges and universities in order to improve the learning quality of computer courses. And hope to build a variant learning model to improve students’ interest in computer courses. The exploration and inference of some knowledge in this paper are mostly my own views, some places are not professional enough, the majority of experts and scholars can criticize and correct at will.

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