A Conceptual Model of Business Intelligence System Adoption for the Textile and Apparel Industry in Pakistan

Ahmad, Sumera Shan; Miskon, Suraya

doi:10.22581/muet1982.2102.01

Cited by 7 publications

(3 citation statements)

References 63 publications

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“…Its working process is as follows: firstly, the CCD camera is used to collect the image information of the sample terminal and transfer it to the image acquisition card to convert it into an electrical signal to realize the digitization of image information. en, Labview image processing software is used on the computer to process the digital image, obtain its contour, size, and other information, and then measure it [24]. Finally, the measurement results are compared with the sample parameters to judge whether they meet the requirements and complete the whole measurement process.…”

Section: System Function and Technologymentioning

confidence: 99%

Application of Digital Image Processing Technology in Textile and Garment Field

Shih

2022

Security and Communication Networks

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In order to study the shortcomings of traditional manual inspection of clothing, improve the quality of clothing. A method of applying digital image processing technology in the field of textile and clothing is proposed. This method combines computer digital image technology to propose an image processing and measurement process technology based on the Labview platform. Through the effective combination of neural networks and support vector machines, methods such as deep learning can better realize the recognition and classification of clothing images. According to a random interview survey of 80 people, 78 people think that the quality of clothing is the most important, and 2 people think that beauty is the most important. The experimental results show that the digital book processing technology platform can further improve the accuracy of clothing detection.

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Section: System Function and Technologymentioning

confidence: 99%

Application of Digital Image Processing Technology in Textile and Garment Field

Shih

2022

Security and Communication Networks

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“…[38] The digital record, exhibition, and transmission of cultural relics at the Liangzhu Museum are examined in this study to introduce the digital construction mode of museums. [39][40] Textile inspection has used increasingly sophisticated digital image processing since the 1980s. Some academics employ digital image processing technologies to examine defects, flatness, feature detection, and clothing style classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis for Textile Defect Detection

Thivakaran,

Priyanka,

Antony

et al. 2023

IJRITCC

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The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19. The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE. The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.

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“…The authors recommended that institutional pressures are more significant as compared to competitive pressure that affects the organizations for the adoption of BI system with the aim of achieving stability. It is proved by existing literature, that most studies have focused on the financial and telecommunication industry and some government and educational institutions in the context of BI system adoption [47], [48]. However, there is no comprehensive research associated with the BI system adoption model and especially for the textile and apparel industry.…”

Section: B Previous Studies On Bi System Adoptionmentioning

confidence: 99%

Statistical Assessment of Business Intelligence System Adoption Model for Sustainable Textile and Apparel Industry

et al. 2021

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The textile and apparel industry is one of the biggest competitive industries in the world. Nowadays, industry 4.0 concepts put pressures on textile and apparel companies to integrate advanced technologies. Consequently, Business Intelligence (BI) systems are diffusing rapidly to process large data sets to harness the true value of smart technologies. Regardless of its potentials, most textile and apparel companies are lagging and hesitating to adopt this credible innovation in the presence of a high failure rate (70%-80%) especially in developing countries. To achieve the successful adoption of BI systems, statistical assessment is required to better understand this complex phenomenon. Therefore, a BI system model based on Technology-Organization-Environment (TOE) is developed to evaluate the role of potential determinants pertaining to the users, technology, organization, and environment. Data were collected using a survey with self-administered questionnaires from decision-makers with authoritative designations in the textile and apparel industry, academia, and software companies. Influential relationships among critical determinants were assessed and validated by using Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The results of this study would contribute to the success of costly BI system projects and will motivate the industry experts to potentially assign investments for the BI projects in the developing countries to sustain in the competitive markets.

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A Conceptual Model of Business Intelligence System Adoption for the Textile and Apparel Industry in Pakistan

Cited by 7 publications

References 63 publications

Application of Digital Image Processing Technology in Textile and Garment Field

Application of Digital Image Processing Technology in Textile and Garment Field

Exploratory Data Analysis for Textile Defect Detection

Statistical Assessment of Business Intelligence System Adoption Model for Sustainable Textile and Apparel Industry

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