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Article
Publication date: 2 September 2013

Hesam Odin Komari Alaei and Alireza Yazdizadeh

This paper is concerned with the estimation of reservoir parameters in the presence of noise and outliers using neural network (NN) and Bayesian algorithm. The paper aims…

Abstract

Purpose

This paper is concerned with the estimation of reservoir parameters in the presence of noise and outliers using neural network (NN) and Bayesian algorithm. The paper aims to discuss these issues.

Design/methodology/approach

Outlier detection is of great importance to prediction of time series data. A reliable predictive methodology is proposed based on NN and Bayesian algorithm to efficiency estimates of the parameters of a petroleum reservoir. This strategy is applied to estimate the parameters of Marun reservoir located in Ahwaz, Iran utilizing available geophysical well log data.

Findings

For an evaluation purpose, the performance and generalization capabilities of Bayes-ANN are compared with the common technique of back propagation (BP).

Practical implications

The experimental results demonstrate that the proposed hybrid Bayes-NN algorithm is able to reveal a better performance than conventional BP NN algorithms.

Originality/value

Helped oil and gas companies to estimation of petroleum reservoir parameters more accurate than other methods in the presence of noise and outliers.

Details

Kybernetes, vol. 42 no. 7
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 7 December 2021

Yue Wang and Sai Ho Chung

This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current…

Abstract

Purpose

This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.

Design/methodology/approach

A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.

Findings

The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.

Practical implications

This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.

Originality/value

This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

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Abstract

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

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Article
Publication date: 9 May 2008

Geng Cui, Man Leung Wong, Guichang Zhang and Lin Li

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers…

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Abstract

Purpose

The purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.

Design/methodology/approach

This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.

Findings

The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.

Practical implications

To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.

Originality/value

The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Details

Marketing Intelligence & Planning, vol. 26 no. 3
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 2 May 2017

Normah Omar, Zulaikha ‘Amirah Johari and Malcolm Smith

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in…

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2626

Abstract

Purpose

This paper aims to explore the effectiveness of an artificial neural network (ANN) in predicting fraudulent financial reporting in small market capitalization companies in Malaysia.

Design/methodology/approach

Based on the concepts of ANN, a mathematical model was developed to compare non-fraud and fraud companies selected from among small market capitalization companies in Malaysia; the fraud companies had already been charged by the Securities Commission for falsification of financial statements. Ten financial ratios are used as fraud risk indicators to predict fraudulent financial reporting using ANN.

Findings

The findings indicate that the proposed ANN methodology outperforms other statistical techniques widely used for predicting fraudulent financial reporting.

Originality/value

The study is one of few to adopt the ANN approach for the prediction of financial reporting fraud.

Details

Journal of Financial Crime, vol. 24 no. 2
Type: Research Article
ISSN: 1359-0790

Keywords

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Article
Publication date: 5 June 2009

Francisco J. Veredas, Héctor Mesa and Laura Morente

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of…

Abstract

Purpose

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.

Design/methodology/approach

A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes determined by clinical experts. This training procedure is driven by a k‐fold cross‐validation method. Finally, a Bayesian committee machine is formed by training a Bayesian network to combine the classifications of the k neural networks (NNs).

Findings

The authors outcomes show high efficiency rates from a two‐stage cascade approach to tissue identification. Giving a non‐homogeneous distribution of pattern classes, this hybrid approach has shown an additional advantage of increasing the classification efficiency when classifying patterns with relative low frequencies.

Practical implications

The methodology and results presented in this paper could have important implications to the field of clinical pressure ulcer evaluation and diagnosis.

Originality/value

The novelty associated with this work is the use of a hybrid approach consisting of NNs and Bayesian classifiers which are combined to increase the performance of a pattern recognition task applied to the real clinical problem of tissue detection under non‐controlled illumination conditions.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 15 October 2021

Shaobo Liang

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

Abstract

Purpose

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

Design/methodology/approach

This paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.

Findings

This study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.

Practical implications

The future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.

Originality/value

This paper contributes to research into cross-app behavior, especially in the mobile search research domain.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 9 November 2012

Y.S. Kim and H.I. Park

The purpose of this paper is to examine the feasibility of committee neural network (CNN) theory for the improvement of accuracy and consistency of the neural network

Abstract

Purpose

The purpose of this paper is to examine the feasibility of committee neural network (CNN) theory for the improvement of accuracy and consistency of the neural network model on the estimation of preconsolidation pressure from the field piezocone measurements.

Design/methodology/approach

In this study, CNN theory is introduced to improve the initial weight dependency of the neural network model on the prediction of preconsolidation pressure of soft clay from a piezocone test result. It was found that the proposed CNN model can improve the initial weight dependency of the NN model and provide a more consistent and precise inference result than existing NN models, as well as empirical and theoretical models.

Findings

It was found that the CNN overcomes the initial weight dependency of the single neural network model. Various committees of the single multilayer perceptrons (MLPs) were tested. It was found that if eight single MLPs, which have the same structure but have been trained with a different initial weight and bias, are accumulated in the committee with the same weighting factor, any variation on the prediction of the preconsolidation pressure from the piezocone test result can be simply and successfully eliminated.

Originality/value

In recent years, ANN has been found to be a powerful theory for analyzing complex relationships involving a multitude of variables, on many geotechnical applications. However, single MLP, when repeatedly trained on the same patterns, tends to reach different minima of the objective function each time and hence give a different set of neuron weights, because the solution is not unique for noisy data, as in most geotechnical problems. The authors observed that a committee neural network system is able to provide improved performance compared with a single optimal neural network.

Details

Engineering Computations, vol. 29 no. 8
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 15 November 2021

Xiaojie Xu and Yun Zhang

Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants…

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27

Abstract

Purpose

Chinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.

Design/methodology/approach

To facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.

Findings

The authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.

Originality/value

The results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

Details

Journal of Economic Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-3585

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Article
Publication date: 16 April 2020

Mohammad Mahdi Ershadi and Abbas Seifi

This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification…

Abstract

Purpose

This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.

Design/methodology/approach

First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).

Findings

The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.

Practical implications

The proposed methodology can be applied to perform disease differential diagnosis analysis.

Originality/value

This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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