Sijian Lv

TL;DR: The proposed hierarchical deployment and inference acceleration model meets the minimum latency and accuracy of neural network inference in multiple fog computing scenarios and greatly reduces the performance occupation and case cost of the cloud under the traditional cloud computing model.

TL;DR: This paper studies the evolutionary dynamics of symbolic social networks, proposes the energy function of weak structural equilibrium theory, and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.

TL;DR: This article compares and analyzes the performance of the organizational behavior prediction model established by four typical cost-sensitive learning methods based on six classifiers and proposes an effective personalized solution to the problem of class disequilibrium and nonconsistent misclassification cost in organizational behavior Prediction modeling.

TL;DR: Simulation experiment results show that compared with the method of deploying neural network models in the cloud, the model prediction delay of the distributed neural network model based on fog computing is reduced by an average of 44.79%.