By Marina Sokolova, Peter van Beek
This ebook constitutes the refereed lawsuits of the twenty seventh Canadian convention on man made Intelligence, Canadian AI 2014, held in Montréal, quality controls, Canada, in may well 2014. The 22 average papers and 18 brief papers offered including three invited talks have been rigorously reviewed and chosen from ninety four submissions. The papers conceal various issues inside of AI, comparable to: agent structures; AI functions; automatic reasoning; bioinformatics and BioNLP; case-based reasoning; cognitive versions; constraint pride; info mining; E-commerce; evolutionary computation; video games; info retrieval; wisdom illustration; laptop studying; multi-media processing; average language processing; neural nets; making plans; privacy-preserving information mining; robotics; seek; clever pix; uncertainty; person modeling; internet applications.
Read or Download Advances in Artificial Intelligence: 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, Montréal, QC, Canada, May 6-9, 2014. Proceedings PDF
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Extra resources for Advances in Artificial Intelligence: 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, Montréal, QC, Canada, May 6-9, 2014. Proceedings
In section 3, a new direction for adapting IMUIs is presented. In section 4, a controlled experiment aimed at empirically validating our approach on an IMUI for healthcare application is presented. The results of the experiment are provided in section 5. We present our conclusion in section 6. 2 Motivation and Related Work A great deal of work has been carried out recently to solve technical issues surrounding the design of an adaptable mobile application. e. the system should be usable and adaptable .
User Stereotype Results. Since our data are normal, and we have two conditions (using PHIS2-M and using PHIS2-MA) for the same participants, we have paired them. Consequently, for statistical validation, the hypotheses are verified for each factor, based on the t-Test value and the P-values for the data analysis for all the participants in each stereotype (Table 3). 09. 09. 17. 17. 20. 20. The results of the hypotheses for each factor, based on the t-Test value and the Pvalue approach, are presented in Table 3.
17. 17. 20. 20. The results of the hypotheses for each factor, based on the t-Test value and the Pvalue approach, are presented in Table 3. The symbol ↑↓ means that we can’t reject the null hypothesis, HYP0, so there is no significant difference between using the mobile without adaptation and using the mobile with adaptation for the objective factor. The symbols ↑ and ↓ mean that we reject the null hypothesis, HYP0. So, there is a significant difference between using mobile with and without adaptation, and, based on the value of the t test that falls either in the positive or the negative region for each factor, we accept the alternative hypothesis, HYP1.