Browsing by Author "Chakraborty, Samit"

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    Evaluation of Personal Factors of Workers Affecting Productivity in RMG Sector in Bangladesh
    (Tekstilec, 2019-09) Mozumder, Sumon; Chakraborty, Samit; Hoque, Md. Saiful
    The readymade garment sector in Bangladesh is considered the backbone of earning foreign currency that includes a large number of workers and is mostly responsible for the economic growth of the country. Nevertheless, despite the remarkable growth of the RMG sector and its bright projection, impediments need to be overcome as well. Readymade garment industries in Bangladesh are currently facing some challenges to ensure fire safety and better work environment for garment workers. Apart from several technical factors, personal factors of workers, e.g. education, age, training, work experience and motivation, might also have a substantial impact on the increase in productivity to compete in the global export market. Thus, it was vital to observe the impacts personal factors of workers have on the productivity of readymade garment industries. This study enabled an identification of personal factors of workers which aff ect the productivity of readymade garment industries. The factors were examined through some critical analyses, e.g. hypothesis test, factor analysis and fishbone diagram analysis. After the investigation of empirical data, principal factors were identified and highlighted to improve the productivity in these industries.
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    Fashion Recommendation Systems, Models and Methods
    (Informatics, 2021-07-26) Chakraborty, Samit; Hoque, Md. Saiful; Jeem, Naimur Rahman; Biswas, Manik Chandra; Bardhan, Deepayan; Lobaton, Edgar
    In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.