Nowadays, thyroid disease is increasing rapidly all over the world. Significantly, one out of ten people is affected by the thyroid in India. In recent years, many researchers have done various research works on thyroid disease detection. Therefore, the early stage of thyroid disease prediction is difficult to protect and avoid the worst health condition. In this regard, the machine learning plays a crucial role to detect the disease accurately. We consider the UC Irvin knowledge discovery dataset. So, this paper proposes the XGBoost algorithm to predict thyroid disease accurately. The best features are selected using XGBoost function. The proposed XGBoost algorithm’s efficacy is compared to decision tree, logistic regression, k-Nearest Neighbor (kNN) methods. The performance of all four algorithms is compared and analyzed. It is observed that the accuracy of the XGBoost algorithm increases by 2% than the KNN algorithm.
Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has low computational complexity for large-scale graphs. The contributions of the paper are summarized as follows. Firstly, a transformer is utilized to downsample the first-order proximities of the graph into a latent space, which can result in the structural properties and eventually assist in detecting the communities. Secondly, the fine-tuning task is conducted by tuning variant hyperparameters cautiously, which is applied to multiple social networks (Facebook and Twitch). Furthermore, the objective function (crossentropy) is tuned by L 0 regularization. Lastly, the reconstructed model forms communities that present the relationship between the groups. The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification. The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67 ± 0.04, 0.198 ± 0.02,
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