scholarly journals Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Mingtao Xu
Keyword(s):  
Cluster Analysis ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Transportation Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Demand Prediction ◽  
Usage Patterns ◽  
Accuracy Of Prediction

Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models.

scholarly journals Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

2019 ◽  
Vol 11 (3) ◽  
pp. 913 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Movie Industry ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction ◽  
Accuracy Of Prediction ◽  
Better Than

Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

An Intelligent Integrated Method for Quality Prediction in Lead-Zinc Sintering Process

2011 ◽  
Vol 52-54 ◽  
pp. 674-679
Author(s):  
Chun Sheng Wang ◽  
Min Wu ◽  
Qi Lei
Keyword(s):  
Large Time ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quality Prediction ◽  
Sintering Process ◽  
Grey System ◽  
Integrated Method ◽  
Lead Zinc ◽  
Weighting Coefficients

Based on some features in lead-zinc sintering process (LZSP), such as large time delay and strong non-linearity, an intelligent integrated method for quality prediction based on back-propagation neural network (BPNN) and improved grey system (IGS) is presented. First, the compositions of agglomerate are predicted by BPNN and IGS models. Then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory and an intelligent integrated prediction model (IIPM) is established. The compositions of sinter agglomerate are predicted by integrating the two prediction models. Application results show that the IIPM has higher prediction precision than that of single model and the proposed intelligent integrated method settles the modeling problem of the quality in the LZSP.

A comparative study of different prediction models in predicting human resource demands

2021 ◽  
pp. 1-11
Author(s):  
Bo Huang
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Comparative Study ◽  
Human Resource ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Methods ◽  
Bpnn Model ◽  
Good Agreement

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.

scholarly journals Machine Learning Model for GSM BSC Control Plane Units

2019 ◽  
Vol 8 (6S) ◽  
pp. 219-223
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Parameters ◽  
Large Set ◽  
Data Set ◽  
Wide Acceptance ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Accuracy Of Prediction

At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

scholarly journals Diagnostic Prediction Models for Cardiovascular Disease Risk using Data Mining Techniques

2020 ◽  
Vol 14 (2) ◽  
pp. 113-121
Author(s):  
Nongyao Nai-arun ◽  
Rungruttikarn Moungmai
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Prediction Models ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Back Propagation ◽  
Risk Groups ◽  
Back Propagation Neural Network ◽  
Number Of Patients

Cardiovascular disease is the top national health problem that leads to a big number of deaths in Thailand. There is still a growing number of patients with the disease. Proactive measures of disease prevention and disease control are searching for risk groups. Therefore, people who are at risk can diagnose and manage themselves to reduce risk factors and adjust their behavior accordingly. For this reason, the idea of diagnostic prediction models for Cardiovascular was conducted. The data of patients from 126 health promoting hospitals and 12 hospitals in Saraburi Province were collected. Then, the analysis was done to establish 6 models namely logistic regression, random forest, back-propagation neural network, decision tree, naïve bayes and K-nearest neighbors. Moreover, 10-fold cross validation was applied into the process of each model. The results revealed that the logistic regression model achieved the highest accuracy rate, 99.940%, followed by the back-propagation neural network model, 98.506%. The best model should be developed as a web application to search for new patients or risk groups. It will help to prevent and control the disease quickly and also to reduce mortality.

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