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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

In a community question answering (CQA) system, the new questions are appeared endlessly which have no tags. And the questions must be marked as some labels. Therefore, the question classification is very important for CQA. In the traditional task of question classification, a mass of labeled questions are required. In the real world, it is effortless to obtain a large number of unlabeled question samples and the vast labeled question samples are fairly expensive to obtain. Therefore, how to utilize the unlabeled samples to improve the question classification accuracy has been the core question of the question classification. In this paper, a kind of semi-supervised question classification method based on ensemble learning is proposed. Firstly, several classifiers are combined as one, i.e. ensemble classifier. The ensemble classifier is trained firstly to utilize a small number of labeled question samples. Secondly, the trained preliminary classifier gives each of the unlabeled question samples a pseudo label. Then, the ensemble classifier is trained again to use the labeled question samples and a large number of unlabeled question samples which have pseudo labels. Finally, to verify the effectiveness of the method through the experiments on question samples of 15 classes extracted from the community question answering system. The experiments demonstrate that the method could effectively utilize a large number of unlabeled question samples to improve the question classification accuracy.