Search results

1 – 10 of 63
Article
Publication date: 1 June 2005

C.M. Tam and Thomas K.L. Tong

Accurate estimation or prediction of the resource required for a project is very important for construction. The more accurate the prediction model, the greater the…

Abstract

Accurate estimation or prediction of the resource required for a project is very important for construction. The more accurate the prediction model, the greater the potential for cost savings will be through elimination of any redesign and the minimization of the maintenance expenses. Contractors can also make use of the models for last‐minute bid estimation. In the past the estimators perform the task by analogy with similar previous projects. This approach highly relies on their experience and knowledge. Owing to the lack of a scientific and easily apprehensible method in resource estimation, prediction outcomes are mainly based on humans’ perception, which is inconsistent and exhibits large variations. This paper proposes the use of multiple Group Method of Data Handling (GMDH) models in developing models for resource estimation. The illustrative example has demonstrated the high accuracy of the approach which is superior to other architectures based on artificial neural networks.

Details

Construction Innovation, vol. 5 no. 2
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 8 March 2021

Behrooz Nazemi and Mohsen Rafiean

The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city…

Abstract

Purpose

The purpose of this paper is to use Group Method of Data Handling (GMDH)-type artificial neural network to model the affecting factors of housing price in Isfahan city housing market.

Design/methodology/approach

This paper presents an accurate model based on GMDH approach to describing connection between housing price and considered affecting factors in case study of Isfahan city based on trusted data that have been collected from 1995 to 2017 for every six months. The accuracy of the model has been evaluated by mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) in this case.

Findings

Due to the obtained value of MAPE, RMSE and MAE and also their interpretation, accuracy of modelling the factors affecting housing price in Isfahan city housing market using GMDH-type artificial neural network that has been conducted in this paper, is acceptable.

Research limitations/implications

Due to limitation of reliable data availability about affecting factors, selected period is from 1995 to 2017. Accessing to longer periods of reliable data can improve the accuracy of the model.

Originality/value

The key point of this research is reaching to a mathematical formula that accurately shows the relationships between housing price in Isfahan city and effective factors. The simplified formula can help users to use it easily for analysing and describing the status of housing market in Isfahan city of Iran.

Details

International Journal of Housing Markets and Analysis, vol. 15 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 12 August 2020

Behrooz Nazemi and Mohsen Rafiean

An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying…

Abstract

Purpose

An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH).

Design/methodology/approach

This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap.

Findings

Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom.

Research limitations/implications

In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors.

Originality/value

The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

Details

International Journal of Housing Markets and Analysis, vol. 14 no. 3
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 4 March 2014

Ahmad Mozaffari, Alireza Fathi and Saeed Behzadipour

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults…

Abstract

Purpose

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

Design/methodology/approach

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Findings

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

Originality/value

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 7 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 27 June 2008

Fen‐May Liou and Chien‐Hui Yang

The objective of this paper is to stress the importance of detecting financial frauds in predicting business failures disclosed by the unexpected financial crisis brought…

2399

Abstract

Purpose

The objective of this paper is to stress the importance of detecting financial frauds in predicting business failures disclosed by the unexpected financial crisis brought by Enron, Worldcom and other corporate distresses involving accounting irregularities.

Design/methodology/approach

The most frequently used methodologies in predicting business failures, discriminant analysis and neural network (NN) (based on the Kolmogorov‐Gabor polynomial Volterra series algorithm) are used. This paper suggests a two‐stage NN procedure: the first stage detected the false financial statements, which were excluded from samples that used to predict the business failures at the second stage. The one‐stage discriminant analysis and the NN model are used to contrast the two‐stage approach in terms of accuracy rate.

Findings

The one‐stage NN model has a higher accuracy rate in identifying failed firms than the discriminant analysis, while the two‐stage NN approach has an even higher accuracy rate than the one‐stage NN model.

Practical implications

Detecting the fraudulent reporting in advance can effectively improve the accuracy rate of business failure predictions.

Originality/value

The paper draws attention to the importance of excluding fraudulent financial reporting to increase the accuracy rate in predicting business failures.

Details

International Journal of Accounting & Information Management, vol. 16 no. 1
Type: Research Article
ISSN: 1834-7649

Keywords

Article
Publication date: 27 May 2021

Sara Tavassoli and Hamidreza Koosha

Customer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification…

Abstract

Purpose

Customer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.

Design/methodology/approach

In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.

Findings

To examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.

Originality/value

In this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

Details

Kybernetes, vol. 51 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 16 October 2009

Zhang Shuhong and Chen Mianyun

The purpose of this paper is to select the main impact factors of environment change automatically and identify and analyze the potential environmental impact factors with…

695

Abstract

Purpose

The purpose of this paper is to select the main impact factors of environment change automatically and identify and analyze the potential environmental impact factors with time delay by computer simulation, analyzing the impact rate of environmental impact factors. Then, the environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Design/methodology/approach

Applying data mining methods in the analysis and decision making of regional environmental impact factors will have broad perspective. Self‐organization data mining is a new modeling method of complex systems modeling with strong modeling capability. It was first presented by A.G. Ivakhnenko, based on the principle of self‐organization of biological cybernetics and Kolmogoorov‐Gabor polynomial function. In this paper, the impact factors of regional environment quality evolution based on self‐organization data mining method is studied, selecting the main impact factors of environment change automatically by computer simulation, analyzing the impact contribution rate of environmental impact factors.

Findings

The environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Research limitations/implications

Accessibility and availability of data are the main limitations affecting which model will be applied.

Practical implications

The paper has important theoretical and practical significance for the sustainable development of regional environment, resource, economy system and the constitution of environmental protection and management measures.

Originality/value

This paper not only exploits new application domains of self‐organization data mining, but also explores new ways for regional environment impact factors analysis.

Details

Kybernetes, vol. 38 no. 10
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 28 January 2020

Datta Bharadwaz Y., Govinda Rao Budda and Bala Krishna Reddy T.

This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of…

74

Abstract

Purpose

This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle swarm optimization (PSO) with Derringer’s desirability.

Design/methodology/approach

The performance parameters such as brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), CO, HC, NOx and smoke are considered as objectives with compression ratio, blend percentage and load as input factors. Optimization is carried out by using PSO coupled with the desirability approach.

Findings

From results, the optimum operating conditions are found to be at compression ratio of 18.5 per cent of fuel blend and 11 kg of load. At this input’s parameters of the engine, outputs performance parameters are found to be 34.84 per cent of BTHE, 0.29 kg/kWh of BSFC, 2.86 per cent of CO, 13 ppm of HC, 490 ppm of NOx and 26.25 per cent of smoke.

Originality/value

The present study explores the abilities of both particle swarm algorithm and desirability approach when used together. The combined approach resulted in faster convergence and better prediction capability. The present approach predicted performance characteristics of the variable compression ratio engine with less than 10 per cent error.

Details

World Journal of Engineering, vol. 17 no. 2
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 29 March 2021

Steffany N. Cerda-Avila, Hugo I. Medellín-Castillo and Theodore Lim

The purpose of this study is to evaluate the capability and performance of analytical models to predict the structural mechanical behaviour of parts fabricated by fused…

Abstract

Purpose

The purpose of this study is to evaluate the capability and performance of analytical models to predict the structural mechanical behaviour of parts fabricated by fused deposition modelling (FDM).

Design/methodology/approach

A total of eight existing and newly proposed analytical models, tailored to satisfy the structural behaviour of FDM parts, are evaluated in terms of their capability to predict the ultimate tensile stress (UTS) and the elastic modulus (E) of parts made of polylactic acid (PLA) by the FDM process. This evaluation is made by comparing the structural properties predicted by these models with the experimental results obtained from tensile tests on FDM specimens fabricated with variable infill percentage, perimeter layers and build orientation.

Findings

Some analytical models are able to predict with high accuracy (prediction errors smaller than 5%) the structural behaviour of FDM and categories of similar additive manufactured parts. The most accurate model is Gibson’s and Ashby, followed by the efficiency model and the two new proposed exponential and variant Duckworth models.

Research limitations/implications

The study has been limited to uniaxial loading conditions along three different build orientations.

Practical implications

The structural properties of FDM parts can be predicted by analytical models based on the process parameters and material properties. Product engineers can use these models during the design for the additive manufacturing process.

Originality/value

Existing methods to estimate the structural properties of FDM parts are based on experimental tests; however, such methods are time-consuming and costly. In this work, the use of analytical models to predict the structural properties of FDM parts is proposed and evaluated.

Details

Rapid Prototyping Journal, vol. 27 no. 4
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 8 July 2021

Michele Cedolin and Mujde Erol Genevois

The research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical…

Abstract

Purpose

The research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.

Design/methodology/approach

A comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.

Findings

The proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.

Research limitations/implications

The research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.

Practical implications

The proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.

Originality/value

This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

Details

Kybernetes, vol. 51 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

1 – 10 of 63