All Outputs (4)

Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion (2021)
Journal Article
Rengasamy, D., Rothwell, B. C., & Figueredo, G. P. (2021). Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion. Applied Sciences, 11(24), Article 11854. https://doi.org/10.3390/app112411854

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is... Read More about Towards a more reliable interpretation of machine learning outputs for safety-critical systems using feature importance fusion.

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review (2021)
Journal Article
Majid, S., Reeves, S., Figueredo, G., Brown, S., Lang, A., Moore, M., & Morriss, R. (2021). The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review. JMIR Mental Health, 8(12), Article e27991. https://doi.org/10.2196/27991

Background: The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The a... Read More about The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review.

The Extent of User Involvement in the Design of Self-Tracking Technology for Bipolar Disorder: Literature Review (2021)
Working Paper
Majid, S., Reeves, S., Figueredo, G., Brown, S., Lang, A., Moore, M., & Morriss, R. The Extent of User Involvement in the Design of Self-Tracking Technology for Bipolar Disorder: Literature Review

Background: Self-monitoring applications for bipolar disorder are increasing in numbers. The application of user-centred design (UCD) is becoming standardised to optimise the reach, adoption and sustained use of this type of technology. Objectiv... Read More about The Extent of User Involvement in the Design of Self-Tracking Technology for Bipolar Disorder: Literature Review.

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion (2021)
Journal Article
Sanchez, S., Rengasamy, D., Hyde, C. J., Figueredo, G. P., & Rothwell, B. (2021). Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing, 32(8), 2353–2373. https://doi.org/10.1007/s10845-021-01785-0

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, par... Read More about Machine learning to determine the main factors affecting creep rates in laser powder bed fusion.