In this position paper, we analyze ways that a human can best be involved in interactive artificial learning against a backdrop of traditional AI programming and conventional artificial learning. Our primary claim is that interactive artificial learning can produce a higher return on human investment than conventional methods, meaning that performance of the agent exceeds performance of traditi...

conclusions: these findings demonstrated that cognitive failure and alexithymia had important roles in strengthening and appearance of high-risk behaviors in students with learning disabilities. therefore, considering those problems, precautionary actions might be necessary. results: the results showed that high-risk behaviors had significant positive correlations with difficulty identifying fe...

Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype a...

Management scientists state that for the purpose of surviving and developing, it is necessary to be engaged in learning forever. Learning can help' organizations with any changes, innovations, and creations. According to studies, the learning capacity in nationwide administrative organizations is rather low. In this article, first the concept and also the necessary characteristics for learning ...

Background: Given importance to learning and given priority to this point that human beings acquire more of their abilities and competencies through learning, learning styles are focused as a pivotal factor in realization of mental and potential abilities. Empowering academic members in the areas of teaching, scholarship, and leadership leads to the achievement of the missions and goals of the ...

Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses...