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Article
Publication date: 17 June 2021

Ambica Ghai, Pradeep Kumar and Samrat Gupta

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered…

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Abstract

Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 18 December 2023

Arpit Gupta and Arya Kumar Srustidhar Chand

The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

Abstract

Purpose

The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

Design/methodology/approach

The authors show theoretically with a model of spillover that if foreign firms (receiving FDI) have a negative spillover effect on domestic firms (not receiving FDI), then the level of capital and skilled workers in the domestic firms falls down. Consequently, the authors conduct an empirical analysis by using system GMM estimation technique on the firm-level data of the Indian organised manufacturing sector.

Findings

The authors show that wage inequality worsens when there is negative spillover effects like competition spillover or skill spillover effect of FDI in India.

Originality/value

To the best of the authors’ knowledge, this is the first attempt to measure the various spillover effects of FDI on the wage inequality in the Indian manufacturing industries by using firm-level data.

Details

Indian Growth and Development Review, vol. 17 no. 1
Type: Research Article
ISSN: 1753-8254

Keywords

Article
Publication date: 5 December 2023

Atul Kumar Singh and V.R. Prasath Kumar

Blockchain is a developing technology that affects numerous industries, including facility management (FM). Many barriers are associated with adopting blockchain-enabled building…

Abstract

Purpose

Blockchain is a developing technology that affects numerous industries, including facility management (FM). Many barriers are associated with adopting blockchain-enabled building information modeling (BEBIM) in FM. This research aims to identify and prioritize the barriers to adopting BEBIM in FM.

Design/methodology/approach

To address the knowledge gap, this study employs a two-phase methodology for evaluating the barriers to adopting BEBIM in FM. The first phase involves a comprehensive literature review identifying 14 barriers to BEBIM adoption. Using a Delphi approach, the identified barriers were categorized into 6 groups and finalized by 11 experts, adding 3 more barriers to the list. The best-worst method (BWM) determines the priority weights of identified barriers and sub-barriers in the second phase.

Findings

This study reveals that adopting BEBIM for FM in India faces significant hurdles. The most critical barriers are “limited collaboration” and “communication among stakeholders,” “legal constraints in certain jurisdictions” and “challenges in establishing trust and governance models.” To mitigate these barriers, stakeholders should foster collaboration and communication, develop efficient blockchain technology (BT) and establish a trust and governance model.

Practical implications

This work underscores the importance of formulating effective strategies to overcome the identified barriers and emphasizes implications that can assist policymakers and industry stakeholders in achieving successful BEBIM adoption for improved FM practice.

Originality/value

The study provides valuable insights for policymakers, construction industry stakeholders and facility managers interested in leveraging this technology to improve the efficiency and effectiveness of FM practice in India.

Details

Built Environment Project and Asset Management, vol. 14 no. 2
Type: Research Article
ISSN: 2044-124X

Keywords

Article
Publication date: 11 March 2024

Sudhanshu Joshi, Manu Sharma, Sunil Luthra, Jose Arturo Garza-Reyes and Ramesh Anbanandam

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

Abstract

Purpose

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

Design/methodology/approach

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

Findings

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

Research limitations/implications

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

Originality/value

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

Details

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

Keywords

Article
Publication date: 10 October 2023

M.S. Narassima, Vidyadhar Gedam, Angappa Gunasekaran, S.P. Anbuudayasankar and M. Dwarakanath

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries…

Abstract

Purpose

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

Design/methodology/approach

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

Findings

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Research limitations/implications

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

Originality/value

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.

Details

Supply Chain Management: An International Journal, vol. 29 no. 2
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 26 September 2023

Mohammed Ayoub Ledhem and Warda Moussaoui

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric…

Abstract

Purpose

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

Design/methodology/approach

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

Findings

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

Practical implications

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

Originality/value

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Details

Journal of Modelling in Management, vol. 19 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

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