Business Intelligence, Analytics and Cognitive Technologies for Industry - Specific Applications

Permanent URI for this collection

https://hdl.handle.net/10125/63608

Browse

Recent Submissions

Now showing 1 - 3 of 3
  • Item
    Multicriteria Decision Analysis and Conversational Agents for Children with Autism
    ( 2020-01-07) Spitale, Micol ; Catania, Fabio ; Crovari, Pietro ; Garzotto, Franca
    Conversational agents has emerged as a new means of communication and social skills training for children with autism spectrum disorders (ASD), encouraging academia, industry, and therapeutic centres to investigate it further. This paper aims to develop a methodological framework based on Multicriteria Decision Analysis (MCDA) to identify "the best", i.e. the most effective, conversational agent for this target group. To our knowledge, it is the first time the MCDA is applied to this specific domain. Our contribution is twofold: i) our method is an extension of traditional MCDA and we exemplify how to apply it to decision making process related to CA for person with autism: a methodological result that would be adopted for a broader range of technologies for person with impairments similar to ASD; ii) our results, based on the above mentioned method, suggest that Embodied Conversational Agent is most appropriate conversational technology to interact with children with ASD.
  • Item
    Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry
    ( 2020-01-07) Walk, Jannis ; Kühl, Niklas ; Schäfer, Jonathan
    Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have—besides their value as recyclable assets—additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools to foster value co-creation between tool manufacturer and tool user (customer) in the future. In the work at hand, we present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping. The resulting Matthews Correlation Coefficient of 0.878 and 0.644 confirms the feasibility of our system based on the VGG-16 network and Gradient Boosting. Based on these first results we derive a research agenda which addresses the need for a more holistic tool characterization by semantic segmentation and assesses the perceived business impact and usability by different user groups.
  • Item