The utilization of machine learning techniques on Twitter data to discriminate between positive and negative tweets is evaluated in [17],[18], suggesting that classification accuracies of 60 80% can be obtained.
Three sub-level indicators of family learning environment are significantly related to the utilization of the museum for learning indicator, which indicates that families with better family learning environment are more willing to access to museum.
Moreover, utilization of community's information platform (PlatCom) was correlated with learning engagement in workplace (EngWork) and utilization of the museum for learning (UtilMus), these two coefficients all up to.506.
Similarly, participation of learning activities in community (ActCom) was also correlated with learning engagement in workplace (EngWork) (rho = .416, p < .01) and utilization of the museum for learning (UtilMus) (rho = .414, p < .01), as well related to family digital devices (FamDig) (rho = .436, p < .01).01
As Table 7 shown, utilization of learning places in community(PlaceCom) was significantly correlated with family digital devices (FamDig) (rho = .533, p < .01), utilization of the museum for learning (UtilMus) (rho = .529, p < .01).01
Recently, several studies have reported utilization of machine learning techniques for differential classification of leukemia.
For community learning environment, utilization of learning places in community, utilization of community's information platform, participation of learning activities in community, and development of community education were chosen.
The effect of utilization of the E-learning program was evaluated through a post-test assessment.
When analyzing the utilization of learning media, we found a stable 3-factor-model.
In this paper, firstly, we investigate the mechanisms of updating the related families and attribute reducts by the utilization of previously learned results in dynamic covering decision information systems with variations of attributes.
