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Article
Publication date: 22 March 2024

Mohd Mustaqeem, Suhel Mustajab and Mahfooz Alam

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have…

Abstract

Purpose

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

Design/methodology/approach

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

Findings

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Originality/value

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Details

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

Keywords

Article
Publication date: 19 July 2023

Gaurav Kumar, Molla Ramizur Rahman, Abhinav Rajverma and Arun Kumar Misra

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

Abstract

Purpose

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

Design/methodology/approach

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

Findings

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

Practical implications

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

Originality/value

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

Details

Journal of Modelling in Management, vol. 19 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 24 January 2023

Hossein Motahari-Nezhad

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the…

Abstract

Purpose

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

Design/methodology/approach

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

Findings

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

Practical implications

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

Originality/value

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

Details

Aslib Journal of Information Management, vol. 76 no. 2
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 19 March 2024

Thao-Trang Huynh-Cam, Long-Sheng Chen and Tzu-Chuen Lu

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct…

Abstract

Purpose

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

Design/methodology/approach

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

Findings

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

Practical implications

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Originality/value

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Article
Publication date: 28 February 2024

Robert J. Donovan, Geoffrey Jalleh and Catherine Drane

Source credibility is a key influencing factor across both commercial and social marketing. It is perhaps even more important for the latter given that the issues under…

Abstract

Purpose

Source credibility is a key influencing factor across both commercial and social marketing. It is perhaps even more important for the latter given that the issues under consideration generally have substantial implications for both individual and societal health and well-being. The Act-Belong-Commit campaign is a world-first population-wide application of social marketing in the area of positive mental health promotion. This study aims to focus on the perceived credibility of the Act-Belong-Commit campaign as a source of information about mental health as a predictor of three types of behavioural responses to the campaign: adopting mental health enhancing behaviours; seeking information about mental health and mental health problems; and seeking help for a mental health problem.

Design/methodology/approach

A state-wide survey was undertaken of the adult population in an Australian state where the Act-Belong-Commit campaign originated. The survey included measures of the above three behavioural responses to the campaign and measures of respondents’ perceptions of Act-Belong-Commit’s source credibility. Logistic regression analyses were performed to determine whether the three behavioural responses can be predicted based on perceived source credibility. The predictive performance of the model was examined by receiver operating characteristic curve analysis.

Findings

Greater perceived source credibility was significantly associated with having done something for their mental health and for having sought information, and an increased likelihood, but not significantly so, of having sought help for a mental health problem.

Originality/value

Despite the acknowledged importance of source credibility, there has been little published research that the authors are aware of that has looked at the impact of such on the effectiveness of social marketing campaigns. To the best of the authors’ knowledge, this is the first published study of the association between source credibility and behavioural response to a social marketing campaign.

Details

Journal of Social Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2042-6763

Keywords

Article
Publication date: 6 September 2023

Lenka Papíková and Mário Papík

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors…

Abstract

Purpose

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

Design/methodology/approach

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

Findings

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

Originality/value

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

Details

Gender in Management: An International Journal , vol. 39 no. 3
Type: Research Article
ISSN: 1754-2413

Keywords

Article
Publication date: 28 March 2024

Elisa Gonzalez Santacruz, David Romero, Julieta Noguez and Thorsten Wuest

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework…

Abstract

Purpose

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

Design/methodology/approach

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

Findings

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

Originality/value

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 22 August 2023

Ted Cléophane Ngassa

High levels of youth unemployment in Africa, the difficulty of accessing salaried jobs, and the weakness of public institutions in charge of employment issues push youths towards…

Abstract

Purpose

High levels of youth unemployment in Africa, the difficulty of accessing salaried jobs, and the weakness of public institutions in charge of employment issues push youths towards informal channels that can help them find jobs. Among these informal channels, religion has been playing an increasingly important role. Thus, this study aimed to analyse the effects of religion on youths' access to self-employment.

Design/methodology/approach

This study used data from the survey on the improvement of youth employment policies in Francophone Africa—conducted in 2018 by the Laboratory for Economic and Social Research and Studies (LARES) of Marien Ngouabi University—to estimate the effects of religion on access to self-employment. The econometric model employed is a two-stage model. Conditional mixed process developed by Roodman (2011) was used to verify the model's robustness.

Findings

The results indicate that religion exhibits a positive and significant effect on access to self-employment. This effect is stronger for youths from Muslim communities than for those from other religious communities, compared to youths who do not engage in religious communities.

Social implications

Based on the current dynamics observed in numerous African countries with respect to employment access, these results imply that religious denominations should be considered when developing policies and programs related to employment, particularly for youths.

Originality/value

The approach followed in this study contributes to the literature predominantly by demonstrating how the network theory approach helps explain, to some extent, the link between religion and access to employment in general and access to self-employment, particularly in developing economies—mainly in sub-Saharan Africa, where the recourse to informal channels of access to self-employment constitutes a significant solution approach for youths.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0097

Details

International Journal of Social Economics, vol. 51 no. 3
Type: Research Article
ISSN: 0306-8293

Keywords

Article
Publication date: 24 January 2024

Stuart John Barnes

Color psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding…

Abstract

Purpose

Color psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding, web content containing social signals may be more attractive via the initiation of a social connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success.

Design/methodology/approach

The research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence techniques were employed to analyze the listing images for color complexity and the presence of people, while specific language features, including socialness, were measured in the listing text. Logistic regression was applied, controlling for several additional variables and predictive model was developed.

Findings

The findings supported the color complexity and socialness effects on crowdfunding success. The model achieves notable predictive value explaining 56.4% of variance. Listing images containing fewer colors and that have more similar colors are more likely to be crowdfunded successfully. Listings that convey greater socialness have a greater likelihood of being funded.

Originality/value

This investigation contributes a unique understanding of the effect of features of both socialness and color complexity on the success of crowdfunding ventures. A second contribution comes from the process and methods employed in the study, which provides a clear blueprint for the processing of large-scale analysis of soft information (images and text) in order to use them as variables in the scientific testing of theory.

Details

Industrial Management & Data Systems, vol. 124 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 23 November 2023

Carla Oliveira-Silva, Andreia Soares-Semedo and Beatriz Lopez-Bermudez

When graduates cannot get a job in their field of study, they have to consider alternative scenarios: extending studies while waiting for labor market conditions to improve;…

Abstract

Purpose

When graduates cannot get a job in their field of study, they have to consider alternative scenarios: extending studies while waiting for labor market conditions to improve; continuing looking for a qualified job; accepting a less qualified job; creating their own business; change their country of residence, which in the case of foreign citizens living in Portugal may involve new emigration or return to the country of origin.

Design/methodology/approach

An online survey was conducted with 108 foreign graduates, unemployed, 47 of whom already hold Portuguese nationality. They were asked about their country of origin, nationality, graduation, field of education, previous jobs, unemployment and alternatives to face it.

Findings

The results suggest that the main options considered are extending studies, continuing to seek skilled employment or a second migration. The desire to return to the country varies according to gender and, in some cases, the country of origin.

Originality/value

There are no studies available on the possibilities for foreign recent graduate students in terms of their choices between entering the labor market or pursuing further education.

Details

Journal of Entrepreneurship and Public Policy, vol. 13 no. 1
Type: Research Article
ISSN: 2045-2101

Keywords

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