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.

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.