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.

The textile and fashion industries account for a significant part of global business. Textile wet processing (TWP) is a crucial stage in textile manufacturing. It imparts aesthetics as well as functional appeal on the textile fabric and ultimate products. Nevertheless, it is considered as one of the most polluting industries and threatens sustainability. There have been different approaches to transform this polluting industry to a sustainable industry. Many researchers have found this challenging, as sustainable, eco-friendly, green or cleaner wet processing might not be always applicable and relevant from the perspective of industrial applications. The present work helps us understand the current state of research of cotton-based textile processes including proposed sustainable approaches. It also examines the achievement of the degree of sustainability of those proposed processes with the lens of the triple bottom line (TBL) framework, identifies existing limitations, and suggests future research scopes that might pave ways for young researchers to learn and undertake new experimental and theoretical research.

Defect detection is a crucial step in textile and apparel quality control. An efficient defect detection system can ensure the overall quality of the processes and products that are acceptable to consumers. Existing techniques for real-time defect detection tend to vary according to unique manufacturing processes, focal defects and computational algorithms. Although the need is high, research related to automatic printed fabric defect detection processes is not prevalent in academic literatures. This research proposes a novel methodology that demonstrates the application of convolutional neural network (CNN) to classify printing defects based on the fabric images collected from industries. The research also integrated visual geometric group (VGG), DenseNet, Inception and Xception deep learning networks to compare model performance. The results exhibit that the VGG-based models perform better compared to a simple CNN model, suggesting promise for automatic defect detection (ADD) of printed fabrics that can improve profitability in fashion supply chains.

Trend forecasting is a challenging job and needs precise prediction based on colour, pattern, and style. Nowadays, researchers are applying machine learning and predictive models to predict the trend. Fashion runways are considered important events by high-street and fast fashion retailers. These events inspire them to design and develop different styles for the mass people. This research presented an approach to predict pattern and outfit based on the images collected from New York Fashion Week Fall/Winter 2019 (NYFW-19) Instagram posts, using logistic regression. The results predicted the patterns that could be used by retailers in the coming season for mass-market consumers. However, it could not predict outfit as a function of colour as there was no relationship between these two variables.

This research paper will illustrate different forms of contemporary sustainable measures that can be applied in knitwear manufacturing industries. Apart of discussing this aspect the paper will also report what types of sustainable measures are taken by garments industries in Bangladesh while producing knitted garments. Different opportunities of introducing and maintaining sustainability in garments industry will be elaborately presented in this paper. Besides of these, in order to study the sustainable practices that have been taken by knit composite industries in Bangladesh in recent years will be presented as a report. The research will also describe what kinds of difficulties impede them in adopting or executing sustainable manufacturing practices in the production of knitted apparels. In order to collect the information required to carry out the research the author will visit five garments industries and conduct interview with key responsible persons of those industries. A questionnaire will be prepared for interviewing them. Then their answers will be discussed elaborately followed by a concise conclusion.