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Osteoporosis (OP) is a kind of bone disease related to aging. Its onset is obscure and development is gradual. Patients are without obvious clinical manifestations in the early stage. However, with the progress of the disease, bone loss and bone tissue microstructure destruction occur, and bone pain, spine deformation, and even osteoporotic fracture (OPF) and other serious complications may develop. Dual-energy X-ray absorption method is the current gold standard for clinical diagnosis of OP. Because its diagnostic accuracy is affected by the factors of body weight, lumbar degenerative changes, and aortic wall calcification, a false negative diagnosis is possible. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. At present, AI has been widely used in the research of OP. Its research in the field of OP screening, diagnosis, and prediction has become a new hot spot. This paper expounds the application status and advantages of AI in OP diagnosis and treatment from four aspects, i.e., OP early screening, medical imaging performance, clinical diagnosis and treatment data, and OPF risk prediction, so as to provide a new direction for the accurate diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor in the gastrointestinal tract, with complex biological characteristics and varying risks, and the treatment methods and prognosis of patients with different risks are quite different; therefore, early diagnosis and risk assessment are crucial for its precision treatment. In recent years, CT radiomics, as an emerging imaging technology, can transform traditional CT image features into a large number of data, thereby reflecting the inherent heterogeneity of GIST and even correlating with its gene expression features. This paper reviews the research progress of CT radiomics in the diagnosis and prediction of GIST with the help of machine learning. The current CT radiomics can not only be used for the differential diagnosis of GIST and other gastric diseases, but also for the risk evaluation of GIST. Furthermore, pathological analysis and gene diagnosis can be performed based on CT images, and then the first-line treatment effect and longterm prognosis can be predicted. At present, various prediction models constructed by combination of CT radiomics and clinical information have been well verified in the specific practice of different clinical problems, showing broad application prospects. However, in the specific clinical application process, different methods of sample data collection and processing, differences in the selection of machine learning algorithms, and the selection of 2D or 3D images all affect the specific effectiveness of CT radiomics. Hence, unified and standardized application rules for radiomics has to be established. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) and machine learning develops new pathways and new modalities for the diagnosis and treatment of hypertension. This paper reviews the progress of AI applied in aspects of hypertension prediction, assessment, and assistance for treatment decision-making. In terms of hypertension prediction, AI could build hypertension risk prediction models based on traditional hypertension risk factors combined with imaging, genomics and other factors, which improves the early diagnosis rate of hypertension. The AI expert system identifies complex etiology of hypertension, which makes up for the limitations of clinicians in diagnosis. In terms of hypertension assessment, the prediction model of hypertension complication constructed by AI could predict complications in the heart and the brain blood vessel, and screen out high-risk population for early intervention. In terms of hypertension treatment, the application of clinical decision-making model, drug factor analysis model, monitoring and early warning system, human-computer interaction, etc. could improve the accuracy, timeliness and patient compliance of hypertension treatment, and assist clinicians in making optimal treatment decisions. The exploration of AI in the field of hypertension diagnosis and treatment has established a new model for the chronic disease management of hypertension. [ABSTRACT FROM AUTHOR]

Ocean mesoscale eddy is an important ocean mesoscale phenomenon that plays an important role in ocean circulation, mat erial and energy transport, and has an important impact on the safety of ship navigation and hydroacoustic communication. Efficient and accurate detection and identification of ocean mesoscale eddies are of great research value for both physical ocean cognition and ocean exploitation. Traditional eddy detection and identification methods rely on a single threshold designed by experts experiences. With the rise of deep learning, the current machine learning methods show certain advantages in the accuracy and automation of eddy detection and identification. This paper summarizes and comparatively analyzes the existing machine learning-based detection and identification methods to provide a systematic knowledge and reference basis for the development of ocean mesoscale eddy detection and identification research. [ABSTRACT FROM AUTHOR]

In order to maintain security and reliability of operating system, this paper developed a universal framework for net-work intrusion detection system based on artificial intelligence( AI) model. This framework was capability of detecting all sorts of network traffic, catching out possible malicious connection or attack from Internet and identifying it. Firstly, this system pre-processed the network traffic data by sampling, one-hot encoding, feature selection and normalization for basic feature extrac-tion. Secondly,it utilized feature distribution in network connection instance to build a scoring mechanism for information ex-traction once again. Thirdly, it applied the different types of ML/DL models for different forms of network traffic to do final classification for extracted features. In experiments, this system applied three benchmark datasets, KDDCup99, UNSW-NB15 and CICIDS2017. The result of experiments shows it possesses exceedingly good performance and has better accuracy and F1 score than related works under these datasets. [ABSTRACT FROM AUTHOR]

With the development of high performance computer and data sharing platform, a large number of high fidelity turbulence data can be obtained. Recently, due to the evolution of artificial intelligence, like deep neural network, data-driven machine learning methods have been adopted to quantify the model uncertainty and improve and construct turbulence models. The combination of big turbulence data and artificial intelligence becomes a new area of turbulence research. Although some encouraging results have been achieved, there are still many difficulties and challenges, such as the generalization ability and robustness of the models, etc. The modeling process involves various aspects including data process, feature selection and selection and optimization of the model framework, etc. This paper analyzes and summarizes the main research progress from two aspects:the implementation methods of machine learning in turbulence modeling and the different model targets. Besides, the challenges and future works in this area are also discussed. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is a particular approach to artificial intelligence. TensorFlow is the second generation machine learning framework of Google. This paper focuses on the basic principles of machine learning and the basic methods of machine learning by using TensorFlow. Its purpose is to explore the possibilities and scenarios of machine learning applications in library. A TensorFlow ML model is established and with the index data from National Index of Newspapers and Magazines, a complete process of automatic classification of records had been accomplished and proved feasible. Through the training process and testing of more than 170 million data records, the experiment has overcome the contradiction between the less comprehension of the index data and the trivial category labels, and reached nearly 80% of the categories and nearly 70% of the accuracy rate. It can be concluded that the approach is capable of taking into practice, at least to carry on a semi-automatic processing of classification, which is expected to significantly save labor costs. The next step will be optimizing the parameters and system tuning. We hope it can strive to achieve an accuracy of 90% by automatic classification. [ABSTRACT FROM AUTHOR]