scholarly journals Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chenghao Fei ◽  
Chenchen Ren ◽  
Yulin Wang ◽  
Lin Li ◽  
Weidong Li ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stepwise Discriminant Analysis ◽  
Accuracy Rate ◽  
Different Types ◽  
Chemical Content ◽  
Electronic Nose System

AbstractCrataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

scholarly journals Electronic-Nose System for Classification of Fruits and Freshness Measurement using K-NN Algorithm

2019 ◽  
Vol 8 (6S4) ◽  
pp. 641-644 ◽  
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Fruit Juice ◽  
Ph Value ◽  
Manufacturing Industries ◽  
Sensor Data ◽  
Food Manufacturing ◽  
Different Types ◽  
Electronic Nose System

The main objective of this work is to design electronic nose system (E-Nose) which detects the odour and freshness of different fruits such as mango, pineapple, orange which are mainly used in food manufacturing industries. E-Nose system consists of sensor array of two with each sensor respond to different types of odours. These sensor data is analyzed with the K-nearest neighbour algorithm (K-NN Algorithm) using MATLAB for identification of different fruits. Freshness of fruit juice is determined by the measurement of pH value of juice by using pH electrode

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.

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.

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 Assessing Community Resilience to Urban Flooding in Multiple Types of the Transient Population in China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2784 ◽  
Author(s):  
Wenping Xu ◽  
Lingli Xiang ◽  
David Proverbs
Keyword(s):  
Climate Change ◽  
Empirical Studies ◽  
Back Propagation ◽  
Community Resilience ◽  
Back Propagation Neural Network ◽  
Disaster Resilience ◽  
Urban Flooding ◽  
Adaptation To Climate Change ◽  
Mitigation And Adaptation ◽  
Different Types

While various measures of mitigation and adaptation to climate change have been taken in recent years, many have gradually reached a consensus that building community resilience is of great significance when responding to climate change, especially urban flooding. There has been a dearth of research on community resilience to urban floods, especially among transient communities, and therefore there is a need to conduct further empirical studies to improve our understanding, and to identify appropriate interventions. Thus, this work combines two existing resilience assessment frameworks to address these issues in three different types of transient community, namely an urban village, commercial housing, and apartments, all located in Wuhan, China. An analytic hierarchy process–back propagation neural network (AHP-BP) model was developed to estimate the community resilience within these three transient communities. The effects of changes in the prioritization of key resilience indicators under different environmental, economic, and social factors was analyzed across the three communities. The results demonstrate that the ranking of the indicators reflects the connection between disaster resilience and the evaluation units of diverse transient communities. These aspects show the differences in the disaster resilience of different types of transient communities. The proposed method can help decision makers in identifying the areas that are lagging behind, and those that need to be prioritized when allocating limited and/or stretched resources.

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.

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