Breast cancer is one of the leading causes of death among women
worldwide. There are a number of techniques used for diagnosing this disease:
mammography, ultrasound, and biopsy, among others. Each of these has
well-known advantages and disadvantages. A relatively new method, based
on the temperature a tumor may produce, has recently been explored:
thermography. In this paper, we will evaluate the diagnostic power of thermography
in breast cancer using Bayesian network classifiers. We will show
how the information provided by the thermal image can be used in order to
characterize patients suspected of having cancer. Our main contribution is the
proposal of a score, based on the aforementioned information, that could help
distinguish sick patients from healthy ones. Our main results suggest the potential
of this technique in such a goal but also show its main limitations that
have to be overcome to consider it as an effective diagnosis complementary
tool.
Abstract. This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with its two main limitations: i) The lack of learning competences; and ii) The lack of explicit multiagent functionality. From the multi-agent learning perspective, we propose a BDI agent architecture extended with learning competences for MAS context. Induction of Logical Decision Trees, a first order method, is used to enable agents to learn when their plans are successfully executable. Our implementation enables multiple agents executed as parallel functions in a single Lisp image. In addition, our approach maintains consistency between learning and the theory of practical reasoning.
Appropriate teaching–learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain. The review highlights existing commercial and non-commercial wearables for student engagement monitoring and identifies key physiological signals involved in engagement detection. Our findings reveal that common physiological signals used to measure student engagement include heart rate, skin temperature, respiratory rate, oxygen saturation, blood pressure, and electrocardiogram (ECG) data. Similarly, stress and surprise are key features of student engagement.
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