Abstract
Social computing, exploiting utilization of advanced computational techniques to overcome typical problems in social science, has been a more visualized conception in academia. However, existing researches still suffer from two aspects of challenges: 1) lack of reliable multi-source data acquisition and management; 2) absence of high-performance algorithmic approaches. Fortunately, some newly-emerged cross-discipline technologies offer more opportunities to enhance conventional solutions. For the former, characterized by its property of information collection and integration, Internet of Things (IoT) can be introduced to produce a novel architecture named Internet of Social Computing Things (IoSCT). For the latter, specific neural network models can be set up to manipulate complicated calculation. Thus, taking the issue of sustainability prediction as objective situation, deep neural network-embedded Internet of Social Computing Things (NeSoc) is proposed in this paper. Firstly, IoSCT is put forward as bottom support platform, guaranteeing comprehensive resource involvement of social computing. Secondly, a hybrid neural network mechanism is formulated and embedded into IoSCT for centralized modeling. Finally, a series of experiments are conducted on a real-world dataset to evaluate performance of the proposed NeSoc.
Highlights
The Internet of Things (IoT) [1] is a huge network that combines various information with Internet to realize the interconnection among people, machines and things [2]
With parameters z and λ setting to initial values, and proportion of training data setting to 60%, 70% and 80% in order, we evaluate networkembedded Internet of Social Computing Things (NeSoc) and baselines with the learning rate in Droupout changing to three values: 0.01, 0.008 and 0.003
Two main obstacles exist in general problems of social computing: 1) lack of multi-source data acquisition and management; 2) absence of high-performance algorithmic approaches
Summary
The Internet of Things (IoT) [1] is a huge network that combines various information with Internet to realize the interconnection among people, machines and things [2]. For the former, its goal is to formulate a hybrid neural network-based prediction model and make it trained through multi-source data from IoSCT For the latter, it is expected to conduct a series of experiments to evaluate the former part of works. The whole feature space is divided into two subspaces: urbanization subspace and ecological civilization subspace, both of whom are low-dimensional real-valued vectors The latter is modeled using CNN model, which is described in Section III.B. On the basis of setting up the global feature space with the use of hybrid neural network mechanism, two feature subspaces are concatenated to construct a data mining-driven predictor to output the predicted coordination degree values
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