Predictive analysis of user behavior of E-commerce platform based on machine learning image algorithm in internet of things environment

2021 ◽  
pp. 1-8
Author(s):  
Jiacheng Ni
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Theoretical Foundation ◽  
User Behavior ◽  
Rbf Neural Network ◽  
Model Structure ◽  
Internet Of Things Environment ◽  
Detailed Method ◽  
Overall Performance

With the development of the Internet, the rise of e-commerce has changed the shopping habits of most people. The research of this article is mainly divided into three parts. The first part is the theoretical foundation and core concept research. The second part of this article is a detailed method of establishing a predictive model based on machine learning image algorithms. In addition to reclassifying features, image algorithms are also used to optimize the model structure. The third part is the experimental results and analysis. After comparing with BP neural network and RBF neural network, through data analysis, the prediction model in this paper greatly improves the prediction accuracy and time, and the overall performance has a breakthrough.

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

scholarly journals A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)

2013 ◽  
Vol 7 (3) ◽  
pp. 646-653
Author(s):  
Anshul Chaturvedi ◽  
Prof. Vineet Richharia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Rbf Neural Network ◽  
Neural Network Approach ◽  
Feed Forward Network ◽  
Machine Learning Approach ◽  
Network Process ◽  
Reinforced Learning ◽  
Attack Data

The Internet, computer networks and information are vital resources of current information trend and their protection has increased importance in current existence. Any attempt, successful or unsuccessful to finding the middle ground the discretion, truthfulness and accessibility of any information resource or the information itself is measured a security attack or an intrusion. Intrusion compromised a loose of information credential and trust of security concern. The mechanism of intrusion detection faced a problem of new generated schema and pattern of attack data. Various authors and researchers proposed a method for intrusion detection based on machine learning approach and neural network approach all these compromised with new pattern and schema. Now in this paper a new model of intrusion detection based on SARAS reinforced learning scheme and RBF neural network has proposed. SARAS method imposed a state of attack behaviour and RBF neural network process for training pattern for new schema. Our empirical result shows that the proposed model is better in compression of SARSA and other machine learning technique.

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

Eccentricity of Rotor Prediction of Aero-Engine Rotor Based on Image Identification and Machine Learning

2019 ◽  
Author(s):  
Zihao Zhang ◽  
Junkang Guo ◽  
Yanhui Sun ◽  
Jun Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Direction Error ◽  
Rotor Eccentricity ◽  
The Neural Network ◽  
Aero Engine ◽  
Feature Information ◽  
Engine Rotor

Abstract The eccentricity of rotor seriously affect the vibration and reliability of aero-engine. Due to the machining error of parts, it is very important to accurately predict the error propagation in assembly. A method based on image recognition and machine learning is proposed to predict the eccentricity of rotor. Firstly, by analyzing and calculating the axial and radial runout error data, the error is mainly concentrated in the first 30 orders of the Fourier series. Secondly, based on the mapping relationship between profile trajectory and eccentricity of rotor, the feature information of the profile trajectory is extracted by constructing the complex domain autoregressive (CAR) model for the radial and axial direction error profile trajectory. Then use the finite element method to calculate the rotor eccentricity. Using the feature information as the input of the neural network, the rotor eccentricity is assembled as the output of the neural network, and the radial basis function (RBF) neural network is built to predict the rotor eccentricity. Theoretical and experimental results show that the proposed method has good enforceability, high accuracy, short calculation time and high engineering application value. In addition, this method can not only be applied to predict the eccentricity of aero-engine rotor flange assembly, but also can be used in the general field of interference fit of assembly.

scholarly journals High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

2021 ◽  
Vol 10 (9) ◽  
pp. 623
Author(s):  
Yajie Shi ◽  
Chao Ren ◽  
Zhiheng Yan ◽  
Jianmin Lai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Satellite System ◽  
Environmental Data ◽  
Navigation Satellite ◽  
Global Navigation Satellite

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

Human resource management model based on three-layer BP neural network and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Guohua Wei ◽  
Yi Jin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Bp Neural Network ◽  
Management System ◽  
Learning Technology ◽  
Human Resource Management System ◽  
Resource Management System

At present, data is in a state of explosive growth. The rapid growth of data collected by enterprises has exceeded the processing capacity of traditional human resource management systems, resulting in their inability to perform data management and data analysis. In order to improve the practicality of the human resource management system, this paper applies machine learning technology to the human resource management system, selects dimensions according to the prediction method, and builds a combined model consisting of an optimized GM (1,1) model and a BP neural network model. The model is implemented by a three-layer BP neural network. In order to verify the performance of the research model, this article conducts research using an entity as an example. The research results show that the method proposed in this paper has certain practical effects and can improve the reference for subsequent related research.

Application of Artificial Neural Network in GPS Height Transformation

2014 ◽  
Vol 501-504 ◽  
pp. 2162-2165 ◽  
Author(s):  
Bo Fu ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nonlinear Coefficient ◽  
Height Anomaly ◽  
Engineering Practice ◽  
Model Structure ◽  
Normal Height ◽  
Sample Data ◽  
Geodetic Height ◽  
Residual Errors

GPS technology has been widely used since it was put into use. At present, the plane locational accuracy of GPS can already achieve millimeter level. But in terms of height, in order to apply the GPS ellipsoidal height in engineering practice, the geoid seperation or height anomaly of the corresponding point must be achieved to transform GPS geodetic height to normal height. In this paper, by taking 12 points from the national GPS control network of Xuxiang Village of Haining City as sample data, a BP neural network using 3-4-2-1 model structure is adopted and a nonlinear coefficient 1.1 is added in the response function. The height anomalies of the 5 points of the test set are calculated and the residual errors are achieved by comparing with the measured values. The internal and external coincidence accuracies of the model are 0.824cm and 0.922cm separately. The result shows that the model can completely meet the precision requirement of the fourth-grade leveling survey and can be used to transform the heights of the study area.

Sign in / Sign up

Export Citation Format

Share Document