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Wisdom quotient as a basic index of human cognitive categories and wisdom attributes

Krawczyk Henryk, Targowski Andrew

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2022

Vol. 26, No 1

48--74

Various understanding of human wisdom is discussed, from common-sense approaches through cognitive considerations to scientific analysis. The main cognitive levels are considered such as: filtering the available information about the appearing situation, comprehensive assessment of the situation, awareness of the consequences about the existing situation and reaction to the existing situation. The basic set of essential a4ributes like knowledge, skills, and inspirations are also analyzed. Based on these considerations some relations between cognitive levels and wisdom atributes are presented. It leads to a definition of the wisdom quotient which is a representative measure of human wisdom behaviors. Some representative cases of such behavior are specified and discussed as human attitudes. It is also shown how computer science approaches can support calculation of some wisdom indexes and, in consequence, allow understanding human wisdom.

An intelligent multimodal framework for identifying children with autism spectrum disorder

Chen Jingying, Liao Mengyi, Wang Guangshuai, Chen Chang

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 3

435--448

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.