Affective design using machine learning : a survey and its prospect of conjoining big data

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design

Blockchain application to financial market clearing and settlement systemsNipun

Blockchain technology has emerged as a transformative force in the financial industry, offering the potential to streamline and enhance financial markets’ clearing and settlement processes. This paper explores the application of blockchain technology in these critical areas. We examine traditional clearing and settlement procedures, the challenges they pose, and how blockchain can address these issues. Through case studies and technical insights, we illustrate the benefits and limitations of implementing blockchain solutions. This paper utilizes the PRISMA method to survey papers related to blockchain-based clearing and settlement systems, while using Science Direct to identify papers that have been published in this area. These papers were reviewed to identify themes that relate to extending blockchain development for clearing and settlement system in financial markets. As a result, this paper also shows how the Layer One X (L1X) blockchain can be applied to develop financial markets clearing and settlement systems