Deep learning models for human centered computing in fog and mobile edge networks

From down to above is a process of the unsupervised learning, which automatically learns useful features, and expresses the low-level features as advanced features and from top to bottom is supervised learning process that through the labeled data to the whole network parameter optimization and adjustment of the whole network which has the characteristics of better learning ability. [...]human centered computing in fog and mobile edge networks is one of the serious concerns now-a-days. [...]it is expected that the development of deep learning based solutions will play an important role for human centered computing in fog and mobile edge networks. Topics for this special issue include, but are not limited to (Li et al. 2018a, b, c; Chong-zhi; Gao et al. 2018; Gupta et al. 2016): Deep learning for information revelation and privacy in human centered computing in fog and mobile edge networks Deep learning for industrial system in fog and mobile edge networks Deep learning for security protocols in human centered computing in fog and mobile edge networks Deep learning for fog and mobile edge network modelling and security issues Deep learning for security, privacy and management of multimedia data in fog and mobile edge networks Deep learning to gain novel insightson in human centered computing in fog and mobile edge networks Human centered computing and deep learning concepts and applications Deep learning algorithms for learning the behavior analysis in human centered computing in fog and mobile edge networks Deep learning for dynamic processes in human centered computing in fog and mobile edge networks Deep learning for multimedia data management in fog computing. [...]based on the landmark information of labeled samples provided by standard database and experts, authors realize an improved semi-supervised FCM clustering to guide the tumor identification.