Advances in Artificial Intelligence: 26th Canadian by Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.),

By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)

This e-book constitutes the refereed lawsuits of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in may possibly 2013. The 17 ordinary papers and 15 brief papers awarded have been conscientiously reviewed and chosen from seventy three preliminary submissions and are followed by means of eight papers from the Graduate pupil Symposium that have been chosen from 14 submissions. The papers conceal numerous issues inside AI, corresponding to: details extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.

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Additional info for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings

Example text

Recall once again ψ(g, h = 0, i = 1, j) in (3), which SI denotes as φB (g, h = 0, l|i = 1, j). With respect to p(U ) defined by the CPTs in Table 1, we have ψ(g, h = 0, i = 1, j) = p(g, h = 0|i = 1, j). However, Theorem 3 can be made significantly stronger. Lemma 5. , for X, Y, Z ⊆ U , Ip (X, Y, Z) ⇐⇒ IB (X, Y, Z). Theorem 4. Except for a measure zero set in the space of all joint distributions p(U ) defined by all discrete Bayesian networks (B, C), for any potential ψ built by VE, ψ = γ · p(X|Y ) ⇐⇒ SI denotes ψ as pB (X|Y ), where γ · N is the evidence normal form of ψ.

J. Butz, W. L. Madsen Lemma 2. [3] Consider a Bayesian network (B, C) on U . Given any nonempty subset X of U , vi ∈X p(vi |P (vi )) is a CPT ψ(X|P (X)), where P (X) = (∪vi ∈X P (vi )) − X. Lemma 3. [3] When evidence is not considered, each potential constructed by VE is a CPT. Lemma 3 can be seen as first applying Lemma 1 on the evidence expanded form of a potential built by VE, keeping in mind E = ∅, and then applying Lemma 2. For example, consider the potential ψ built by (6), which is already in evidence expanded form.

13 Detecting Health-Related Privacy Leaks in Social Networks Using Text Mining Tools 37 Next, we used the same two classification methods and performed five by two crossvalidation. The results are shown in Table 9 in the right column. Comparing the results from Part I and II show that there is almost 10% improvement in detecting sentences that reveal health information using the terms detected by MedDRA and SNOMED which confirms our hypothesis. 95. 7 Conclusion and Future Work In this research work, we developed tools that detect and, if necessary, protect personal health information that might be unknowingly revealed by users of social networks.

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