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Fuzzy Cognitive Maps (FCMs) are a class of Artificial Intelligence methods that allows for the modelling of knowledge using fuzzy logic. This approach is more flexible as the knowledge can be modelled as rules, intuition, experience or even to the best degree of approximation by data. The goal of FCM is to combine multiple sources of information. When combined, the result can be interesting for several applications.
In this task, we put in perspective the applied FCM methods with the learning structure in this task. Fuzzy cognitive maps were used to group the relationship of various collaboration and learning models applied in education.
The first main point to consider is that FCM is a learning algorithm and as such it will be easier to detect any training errors.
The most common mistake is not considering the negation of the rule. As such, the correct answer is the second choice. This is due to the universal application of the conjunction rule. However, when the conjunction rule is negated, the answer is the first choice.
Using a tree diagram to display the model can be useful to follow the application process. For instance, Figure 4 displays the result of the Conjunction Rule.
Figure 4 Conjunction Rule
Using a tree diagram to display the model can be useful to follow the application process
Figure 5 displays the result of the Inversion Rule. In this case, there is no negative form.
The Inversion Rule allows the conditions to be inverted before being tested. However, the FCM implementation will select the first option of the rule, which is the answer.
In Figure 6, we see the Inclusion Rule, where it was not possible to trace a path to the initial nodes of the map.
One of the factors that influence the number of rules is the quality of the data used. The data is considered as good when every single relationship is included in the knowledge. It is even better when the data is obtained from experts. In this case, data mining methods are sufficient. However, when the data is not able to cover the whole experience, we need to implement a learning algorithm. Therefore, Fuzzy cognitive maps are a good fit for this task.
Why Fuzzy Cognitive Maps?
The main reason to use the Fuzzy Cognitive Maps
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Fissure sealing, at the time of tooth cavity preparation in endodontic treatment, is a non-invasive procedure that is being increasingly investigated in an attempt to reduce or prevent root caries. However, there are relatively few studies on the long-term effect of fissure sealing on the susceptibility to root caries after restoration. In the present investigation, an experimental model was devised to determine the effect of fissure sealing on the susceptibility to root caries after restoration in vitro. After one year, the restored teeth had considerably more dentinal caries than the unrefined teeth. Fissure sealing treatment had no effect on the severity of root caries after the restoration. The following conclusions were drawn: (a) when filled, fissure sealed teeth are not protected from dentinal caries; (b) when unfilled, fissure sealed teeth are not protected from dentinal caries; (c) when unfilled, fissure sealed teeth are protected from pulpal caries but not from dentinal caries; (d) when filled, fissure sealed teeth are not protected from pulpal caries; (e) when filled, the presence of a smear layer between tooth structure and restorative material protects the restoration from pulpal caries; (f) when filled, the presence of a smear layer between tooth structure and restorative material protects the restoration from dentinal caries; (g) the sealing ability of root canals can be improved by using products that are easily dissolved by
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