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Article
Publication date: 27 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

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Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

Article
Publication date: 10 July 2023

Surabhi Singh, Shiwangi Singh, Alex Koohang, Anuj Sharma and Sanjay Dhir

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive…

Abstract

Purpose

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

Design/methodology/approach

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

Findings

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

Practical implications

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

Originality/value

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

Details

Industrial Management & Data Systems, vol. 123 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 5 February 2018

Bingjun Li, Weiming Yang and Xiaolu Li

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Abstract

Purpose

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Design/methodology/approach

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

Findings

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

Practical implications

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

Originality/value

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

Details

Grey Systems: Theory and Application, vol. 8 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 2 December 2022

Jingyu Cao, Jiusheng Bao, Yan Yin, Wang Yao, Tonggang Liu and Ting Cao

To avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).

Abstract

Purpose

To avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).

Design/methodology/approach

A simulated braking test bench for automobile disc brake was used. The correlation and mechanism between the three braking condition parameters of initial braking speed, braking pressure and initial braking temperature and the tribological performance were analyzed. The different artificial neural network (ANN) models of wear loss were discussed. Genetic algorithm was used to optimize the ANN model. The structure scheme of the online prediction system of BPWL was discussed and completed.

Findings

The results showed that the braking conditions were positively correlated with the wear loss, but negatively correlated with the friction coefficient. The prediction accuracy of back propagation (BP) ANN model was higher. The model was optimized by genetic algorithm, and the average deviation of prediction results was 4.67%. By constructing the online monitoring system of automobile braking conditions, the online prediction of BPWL based on the ANN model could be realized.

Originality/value

The research results not only have important theoretical significance for the study of BPWL but also have practical value for guiding the maintenance and replacement of automobile brake pads and avoiding the occurrence of braking accidents.

Details

Industrial Lubrication and Tribology, vol. 75 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

Open Access
Article
Publication date: 30 June 2021

Mohammad Abdullah

Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout…

4839

Abstract

Purpose

Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.

Design/methodology/approach

This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.

Findings

This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.

Research limitations/implications

The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.

Practical implications

Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).

Originality/value

This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

Details

Journal of Asian Business and Economic Studies, vol. 28 no. 4
Type: Research Article
ISSN: 2515-964X

Keywords

Article
Publication date: 1 March 2001

K.G.B. Bakewell

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18;…

19122

Abstract

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Structural Survey, vol. 19 no. 3
Type: Research Article
ISSN: 0263-080X

Article
Publication date: 1 September 2001

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management…

14900

Abstract

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Facilities, vol. 19 no. 9
Type: Research Article
ISSN: 0263-2772

Article
Publication date: 1 March 2001

K.G.B. Bakewell

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18;…

14515

Abstract

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Property Management, vol. 19 no. 3
Type: Research Article
ISSN: 0263-7472

Article
Publication date: 1 May 2001

K.G.B. Bakewell

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18;…

14270

Abstract

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Journal of Property Investment & Finance, vol. 19 no. 5
Type: Research Article
ISSN: 1463-578X

Article
Publication date: 1 September 2000

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management…

27562

Abstract

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Details

Facilities, vol. 18 no. 9
Type: Research Article
ISSN: 0263-2772

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