By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)
This ebook constitutes the refereed complaints of the twenty fifth Canadian convention on synthetic Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in may well 2013. The 17 commonplace papers and 15 brief papers offered have been conscientiously reviewed and chosen from seventy three preliminary submissions and are observed through eight papers from the Graduate scholar Symposium that have been chosen from 14 submissions. The papers hide numerous subject matters inside of AI, resembling: details extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.
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Additional resources for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings
2 Part II, Special Treatment for Medical Terms We took the vectors resulting from Part I and focused on the terms belong to the following three groups by weighting them stronger in the bag of words than the remaining words. g. ). b) Medical term detected by MedDRA and SNOMED. c) Other medical terms that their existence in a sentence may result in a sentence to be a PHI or HI. Terms such as hospital, clinic, insurance, surgery, etc. For group (a) and group (c) we associate weight 2 (one level more than the regular terms that are presented by 1 as an indication that the terms exist in the sentence).
Section 7 concludes the paper and gives future research directions. 1 Personal Health Information in Social Networks Emergence of social networks, weblogs and other online technologies, has given people more opportunities to share their personal information. , symptoms, treatments, medical care) among other factors of personal life. In fact, 19%-28% of all Internet users participate in medical online forums, health-focused groups and communities and visit healthdedicated web sites [1,14]. A recent study  had demonstrated a real-world example of cross-site information aggregation that resulted in disclosing PHI.
Can SI determine the semantics of every VE potential deﬁned with respect to the joint distribution? The answer is no. Theorem 3. In a Bayesian network B on U , suppose VE computes a potential ψ whose evidence normal form is γ · N . If SI denotes the semantics of N as φB (X|Y ), there exists a set C of CPTs for B deﬁning a joint distribution p(U ) such that N = p(X|Y ). Strong Completeness of Semantics in Bayesian Network Inference 23 Theorem 3 states that whenever SI indicates that a potential is not deﬁned with respect to the joint distribution, then this is true for at least one set of CPTs for the given Bayesian network.
Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings by Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)