An Alternative-Service Recommending Algorithm Based on Semantic Similarity

With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.

Deep Learning-Based Emotion Detection

In order to make artificial intelligence smarter by detecting user emotions, this project analyzes and determines the current type of human emotions through computer vision, semantic recognition and audio feature classification. In facial expression recognition, for the problems of large number of parameters and poor real-time performance of expression recognition methods based on deep learning, Wang Weimin and Tang Yang Z. et al. proposed a face expression recognition method based on multilayer feature fusion with light-weight convolutional networks, which uses an improved inverted residual network as the basic unit to build a lightweight convolutional network model. Based on this method, this experiment optimizes the traditional CNN MobileNet model and finally constructs a new model framework ms_model_M, which has about 5% of the number of parameters of the traditional CNN MobileNet model. ms_model_M is tested on two commonly used real expression datasets, FER-2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets. This network structure well balances the recognition accuracy and recognition speed of the model. For semantic emotion detection and audio emotion detection, the existing models and APIs are used in this experiment.