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This paper describes the use of an objective fuzzy approach for fast and accurate vision-based inspection. An inspection problem faced by a Canadian automotive parts manufacturer is being used as a case study. The problem is related to a vision system that is being operated to confirm the placement of metal fastening clips on a structural member that supports a truck dash panel. The manufacturer was interested in identifying the presence or absence of metal clips inserted by a robot arm. It took the manufacturer over 8 months to tune its commercial machine vision system to detect missing clips and yet the accuracy and efficiency of the system are being questioned. Five different universities across Canada have been working in parallel on this problem over a time span of 2 years. To this end, we developed an efficient fuzzy model after trying various statistical approaches. The proposed model properly identifies all the images in a database containing 1910 images. The robustness of the fuzzy model is confirmed by its strong performance on the entire database. [ABSTRACT FROM AUTHOR]

Organizations are using crowdsourcing to capture innovation knowledge from the crowd in the form of ideas and then using the crowd to evaluate those ideas using votes. In this paper, we investigate a crowdsourcing setting in which Canada solicited information from its citizens to develop a digital transformation strategy. Canada used a two‐phase approach. Phase 1 was used to determine which ideas had the largest number of crowd votes, whereas in Phase 2, the crowd voted on the 30 leading vote‐getting ideas to determine the three winning ideas. This research investigates the ability to use information from ideas to estimate the number of votes that the ideas generate. This approach could be used to estimate the number of ideas, before making information available to the crowd. The unstructured text information in the idea is structured by using target concept dictionaries, which are used to estimate the extent to which the dictionary words appear in the ideas (e.g., "globalism") and are related to the number of votes. Using this approach, roughly 1% of the total words are used to explain roughly 60% of the variance in the votes. Further, we also find that the variables associated with Phase 1 votes are not the same variables associated with Phase 2 votes; that is, the decision‐making variables changed. Finally, we find that votes are statistically significantly related to the content in the idea titles and the idea statements. [ABSTRACT FROM AUTHOR]