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Article
Publication date: 27 July 2021

Anahita Farhang Ghahfarokhi, Taha Mansouri, Mohammad Reza Sadeghi Moghaddam, Nila Bahrambeik, Ramin Yavari and Mohammadreza Fani Sani

The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual…

287

Abstract

Purpose

The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.

Design/methodology/approach

In this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.

Findings

Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.

Originality/value

In this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

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Publication date: 6 September 2019

Abstract

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Experiencing Persian Heritage
Type: Book
ISBN: 978-1-78754-813-8

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Article
Publication date: 3 September 2021

Hosam Alden Riyadh, Laith T. Khrais, Salsabila Aisyah Alfaiza and Abdulsatar Abduljabbar Sultan

The key purpose of this research paper was to identify the association between mass collaboration and knowledge management in the context of Jordanian companies. Apart from that…

291

Abstract

Purpose

The key purpose of this research paper was to identify the association between mass collaboration and knowledge management in the context of Jordanian companies. Apart from that, this study also aims to examine the moderating effect of trust and leadership on the association between mass collaboration and knowledge management.

Design/methodology/approach

In this study, the researcher has followed theprimary quantitative method. For data collection, the researcher has conducted a survey questionnaire, whereas the sample was based on 323 participants from the manufacturing sector of Jordan specifically for data analysis; the technique of structural equation modeling was implemented.

Findings

All the independent variables, including organizational structure, adoptedtechnologies in mass collaboration and collaborative learning techniques, have a significantimpact on knowledge management and leadership. Moreover, leadership was also found to be significantly moderating the association between adopted technologies in mass collaboration and knowledge management. Similarly, trust also significantly moderates the association of organizational structure and adopted technologies in mass collaboration significantly with knowledge management.

Research limitations/implications

All study respondents were from Jordan, which might limit the generalizability of the findings. The researchers also invited for more researchers in the incorporation of the time sequence in the proposed causal relations and in the organization level through which mass collaboration and knowledge management.

Originality/value

This study promises to make a valuable contribution to the existing literature, as there was a lack of evidence in the previous studies regarding the impact of mass collaboration on knowledge management within the context of Jordan.

Details

International Journal of Organizational Analysis, vol. 31 no. 4
Type: Research Article
ISSN: 1934-8835

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Article
Publication date: 28 June 2024

Imadeddine Oubrahim and Naoufal Sefiani

Over the last 2 decades, supply chain sustainability research has become a highly dynamic and fruitful study area. This field has garnered significant attention due to its…

259

Abstract

Purpose

Over the last 2 decades, supply chain sustainability research has become a highly dynamic and fruitful study area. This field has garnered significant attention due to its potential to reshape decision-making processes within supply chains. At the same time, the practical side of supply chain operations remains intensely competitive in today’s business landscape. Furthermore, the current academic research aims to outline effective strategies for achieving sustainability across supply chains, particularly in the manufacturing sector. In response to these challenges, this research has conducted an integrated multi-criteria decision-making approach to evaluate sustainable supply chain performance from the triple bottom line perspective, including financial, environmental, and social performance.

Design/methodology/approach

The initial stage involves selecting the crucial criteria (short-term and long-term) and alternatives for sustainable supply chain performance (SSCP) from experts and conducting an in-depth literature review. Initially, there were 17 criteria, but after a pilot test with co-authors and online discussions with experts, the number of criteria was subsequently reduced to 9. In the second phase, the Best-Worst Method (BWM) was applied to rank and prioritize the criteria. The third and final stage examined the causal relationship between the identified criteria, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique.

Findings

Based on BWM analysis results, the top three criteria in terms of prominence are: (1) return on investment (ROI), (2) product quality, and (3) manufacturing lead time. Out of the three alternatives, financial performance (FP) is the most crucial dimension for SSCP, followed by environmental performance (ENP) and social performance (SP). On the other hand, the DEMATEL approach showed that work health and safety (short-term criterion), asset utilization (long-term criterion), energy consumption (long-term criterion), waste disposal (long-term criterion), manufacturing lead time (short-term criterion), and on-time delivery (short-term criterion) are categorized within the cause group, while criteria such as return on investment (ROI) (long-term criterion), customer-service level (short-term criterion), and product quality (long-term criterion) fall into the effect group.

Research limitations/implications

The proposed study has certain drawbacks that pave the way for future research directions. First, it is worth noting the need for a larger sample size to ensure the reliability of results, the potential inclusion of additional criteria to enhance the assessment of sustainability performance, and the consideration of a qualitative approach to gain deeper insights into the outcomes. In addition, fuzziness in qualitative subjective perception could be imperative when collecting data to ensure its reliability, as translating experts’ perceptions into exact numerical values can be challenging because human perceptions often carry elements of uncertainty or vagueness. Therefore, fuzzy integrated MCDM frameworks are better suited for future research to handle the uncertainties involved in human perceptions, making it a more appropriate approach for decision-making in scenarios where traditional MCDM methods may prove insufficient.

Practical implications

The proposed framework will enable decision-makers to gain deeper insights into how various decision criteria impact SSCP, thus providing a comprehensive evaluation of SSCP that considers multiple dimensions, such as financial, environmental, and social performance within the manufacturing sector.

Originality/value

The proposed study is the first empirical study to integrate both BWM and DEMATEL approaches to evaluate sustainable supply chain performance in the manufacturing context.

Details

International Journal of Productivity and Performance Management, vol. 74 no. 1
Type: Research Article
ISSN: 1741-0401

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Article
Publication date: 17 October 2024

Suhang Yang, Tangrui Chen and Zhifeng Xu

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of…

23

Abstract

Purpose

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

Design/methodology/approach

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

Findings

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Originality/value

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Details

Engineering Computations, vol. 41 no. 10
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 14 July 2021

Jessica Maalouf, Jennifer C. Tomazou, Stephanie Azar, Christelle Bou-Mitri, Jacqueline Doumit, Amira Youssef, Roland B. Andary, Wadih A. Skaff and Milad G. El Riachy

This study aims to identify the effect of selected agro-industrial factors associated with the olive oil phenolic composition, total phenolic content (TPC), antioxidant capacity…

129

Abstract

Purpose

This study aims to identify the effect of selected agro-industrial factors associated with the olive oil phenolic composition, total phenolic content (TPC), antioxidant capacity and oxidative stability index (OSI). The study also aims to assess the relationship between the quality indices and each of the individual phenol, TPC, antioxidant capacity and OSI.

Design/methodology/approach

Olive oil samples (n=108) were collected from Lebanese northern (Akkar and Zgharta-Koura) and southern (Hasbaya and Jezzine) regions, at three harvesting times (early, intermediate, late) and using different types of mills (traditional, sinolea, two- and three-phase decanters). The samples were analyzed using official standard methods.

Findings

The highest TPC, antioxidant capacity and OSI were obtained in early harvested olive oil, using two-phase decanters for TPC and three-phase decanters for antioxidant capacity and OSI. A prediction model, including the free acidity, K232, TPC, C18:2, C18:0, tyrosol and apigenin, was obtained; it allowed to predict very highly significantly the OSI (p < 0.001). Apigenin, tyrosol and C18:2 recorded the highest standardized coefficients (ß^+= 0.35) and thus had the highest influence on OSI. As per antioxidant capacity of olive oil, another very highly statistically significant prediction model was constructed (p < 0.001). It included only two predictors, oleacein and TPC, with the latter having the most influence (ß^+= 0.37).

Originality/value

The overall results highlighted the detrimental effects of agro-industrial factors on olive oil chemical composition, and this contributes significantly to improve olive oil’s quality and characteristics, which are important for the product economical and nutritional values.

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Article
Publication date: 16 May 2024

Evans Sokro, Theresa Obuobisa-Darko and Bernard Okpattah

This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone…

207

Abstract

Purpose

This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone and McLean’s information system success model. It also examines the moderating effect of perceived supervisory support and learners’ self-regulation on online learning quality in Higher Education Institutions.

Design/methodology/approach

Survey data were obtained from 540 students in both private and public higher institutions of learning in Ghana. The Partial Least Squares – Structural Equations Modelling (PLS-SEM) technique was used to test the hypothesised relationships.

Findings

The results revealed that system quality emerged as the single most important variable in the DeLone and McLean model, that influences learner success and satisfaction. Further, learner satisfaction has a significant positive effect on learner attitudes, whilst self-regulation was found to moderate the relationship between online learning quality and learner success as well as learner satisfaction.

Originality/value

The study appears to be among the first to explore the inter-relationship among online learning environment quality and learner attitudes and moderating factors perceived supervisory support and self-regulation. The study highlights insightful practical implications for students, faculty and administrators of higher institutions.

Details

International Journal of Educational Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0951-354X

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Article
Publication date: 5 June 2017

Deepa Mishra, Zongwei Luo, Shan Jiang, Thanos Papadopoulos and Rameshwar Dubey

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up…

3055

Abstract

Purpose

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

Design/methodology/approach

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

Findings

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

Research limitations/implications

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

Originality/value

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Details

Business Process Management Journal, vol. 23 no. 3
Type: Research Article
ISSN: 1463-7154

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Article
Publication date: 17 February 2022

Yangyan Shi, Xiaofei Zheng, V.G. Venkatesh, Eias AI Humdan and Sanjoy Kumar Paul

Facing turbulent environments, firms have strived to achieve greater supply chain resilience (SCR) to leverage the resources and knowledge of supply chain members. Both SCR and…

3150

Abstract

Purpose

Facing turbulent environments, firms have strived to achieve greater supply chain resilience (SCR) to leverage the resources and knowledge of supply chain members. Both SCR and supply chain integration (SCI) require digitization in the supply chain, but their interrelationships have rarely been researched empirically. This paper aims to uncover the impact of digital technology (DT) on SCR and SCI and the role of SCI in mediating between DT and SCR.

Design/methodology/approach

China manufacturing enterprises were surveyed through a Web-based questionnaire, and 96 responses were received. Structural equation modeling was used to test the conceptual model.

Findings

The level of enterprise digitization is not directly related to supply chain resilience, but the level of enterprise digitization has a positive impact on the improvement of SCI and SCI also has a positive effect on SCR. Therefore, SCI has a complete intermediary effect between the level of DT and SCR.

Originality/value

This is a pioneer study to examine the relationships among DT, SCI and SCR. The findings of this study present that firms need to improve DT, SCI and SCR consequently.

Details

Journal of Business & Industrial Marketing, vol. 38 no. 1
Type: Research Article
ISSN: 0885-8624

Keywords

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