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Abstract

Details

Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

Open Access
Article
Publication date: 9 June 2021

Jin Gi Kim, Hyun-Tak Lee and Bong-Gyu Jang

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea…

Abstract

Purpose

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

Details

Journal of Derivatives and Quantitative Studies: 선물연구, vol. 29 no. 2
Type: Research Article
ISSN: 1229-988X

Keywords

Article
Publication date: 31 January 2022

Simone Massulini Acosta and Angelo Marcio Oliveira Sant'Anna

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms…

Abstract

Purpose

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

Design/methodology/approach

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

Findings

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

Originality/value

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 29 April 2021

Emmanuel Adinyira, Emmanuel Akoi-Gyebi Adjei, Kofi Agyekum and Frank Desmond Kofi Fugar

Knowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was…

Abstract

Purpose

Knowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.

Design/methodology/approach

The study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.

Findings

The developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.

Originality/value

The developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

Details

Engineering, Construction and Architectural Management, vol. 28 no. 5
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 3 August 2020

Djordje Cica, Branislav Sredanovic, Sasa Tesic and Davorin Kramar

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects…

Abstract

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2210-8327

Keywords

Open Access
Article
Publication date: 22 October 2021

Syed Farid Uddin, Ayan Alam Khan, Mohd Wajid, Mahima Singh and Faisal Alam

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC…

Abstract

Purpose

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

Design/methodology/approach

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Findings

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

Originality/value

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

Article
Publication date: 24 March 2022

Shu-Ying Lin, Duen-Ren Liu and Hsien-Pin Huang

Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market…

Abstract

Purpose

Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS).

Design/methodology/approach

A novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue.

Findings

The authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN.

Originality/value

There are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Book part
Publication date: 6 September 2019

Vivian M. Evangelista and Rommel G. Regis

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector

Abstract

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78754-290-7

Keywords

Article
Publication date: 13 June 2016

Carlos Fernandez-Lozano, Francisco Cedrón, Daniel Rivero, Julian Dorado, José Manuel Andrade-Garda, Alejandro Pazos and Marcos Gestal

The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques…

Abstract

Purpose

The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)).

Design/methodology/approach

The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils.

Findings

A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model.

Originality/value

The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.

Details

Engineering Computations, vol. 33 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 27 February 2020

Seyed Amin Bagherzadeh

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

Abstract

Purpose

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

Design/methodology/approach

A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.

Findings

The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.

Practical implications

In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.

Originality/value

This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 3
Type: Research Article
ISSN: 1748-8842

Keywords

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