Fractional cross entropy-based loss function for classification of IoT services with semantic graph based on IFTTT recipes

Recently, learning through multi-classifiers is of huge interest in economic as well as industrial domains. In addition, the neural network becomes an emerging technique for learning. Nevertheless, the accuracy of the neural network is imperfect due to its loss function. Hence, a new cross entropy-based function is devised. The aim is to develop a model to classify IoT services and build a semantic graph network using a fractional cross entropy-based loss function (FCEBLF). Originally, the service recipe of IFTTT (if-this-then-that) is considered to extract the title and description. Here, the edge and nodes are used for constructing a semantic graph, where semantic features are obtained. Meanwhile, term frequency and inverse document frequency (TF-IDF) are accomplished using IFTTT recipes. Moreover, the natural language processing (NLP) features are extracted to further increase the efficiency. In addition, the obtained features are fused. Thus, the fusion of features is executed by using deep neural network (DNN) based on Hellinger distance. Lastly, the classification of IoT service is attained based on deep residual network (DRN) in which loss function is enhanced using FCEBLF. The proposed FCEBLF + DRN outperformed with high micro-F1 of 91.2%, precision of 91%, and recall of 91.4%.