Deep Neural Network-Embedded Internet of Social Computing Things for Sustainability Prediction

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.