Affective Web Service Design

Song, Insu; Governatori, Guido

doi:10.1007/11801603_10

Cited by 2 publications

(2 citation statements)

References 11 publications

(13 reference statements)

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“…and Guido (Song and Guido 2006) Instagram for affective design. These social media data can be collected by big data collection technologies: parallel and distributed processing paradigms such as MongoDB, Cassandra, Hadoop (Bello-Orgaz and Jason 2016).…”

Section: Social Media For Affective Designmentioning

confidence: 99%

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

Chan

Kwong

Clark

et al. 2018

International Journal of Computer Integrated Manufacturing

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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.

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Section: Social Media For Affective Designmentioning

confidence: 99%

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

Chan

Kwong

Clark

et al. 2018

International Journal of Computer Integrated Manufacturing

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“…MSDL includes Short Message Service (SMS) binding to allow services to be discovered and utilized over SMS. To ensure the services are usable by 2.2 billion people, the core-banking system implements a Well-Behaved Service Interface (WBSI) based on human emotion models [26,27].…”

Section: Mobile Core-banking Servermentioning

confidence: 99%

Mobile Core-Banking Server: Cashless, Branchless and Wireless Retail Banking for the Mass Market

Song

Vong

2013

2013 International Conference on IT Convergence and Security (ICITCS)

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Banking is essential to modern economies and nearly half of the world's people are unbanked and underserved by banks. The high operating costs of banks passed on to retail customers are driving them away who otherwise should be in the financial system. Without having access to the financial system the unbanked and rural poor have little means to engage in mainstream economic activities. A mobile phone-based banking system was designed to provide retail banking services to the world's unbanked population and rural poor of 2.2 billion based on a cashless banking system that reduces operating cost and reduces lending risks and therefore can lower the current exorbitant rates charged on retail lending and micro-credit. A core-banking server on a low-cost mobile device (an Android tablet) could serve over 15,000 micro-banking customers. The system definitely appears to be suitable for most retail banks or microfinance institutions.

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Affective Web Service Design

Cited by 2 publications

References 11 publications

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

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

Mobile Core-Banking Server: Cashless, Branchless and Wireless Retail Banking for the Mass Market

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