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  • A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

    • Authors: Ma, Z.;  Advisor: -;  Participants: Ge, H.; Liu, Y.; Zhao, M.; Ma, J. (2019)

  • Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control ow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy an...
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  • Machine Learning Assessment for Severity of Liver Fibrosis for Chronic HBV Based on Physical Layer With Serum Markers

    • Authors: Li, N.;  Advisor: -;  Participants: Zhang, J.; Wang, S.; Jiang, Y.; Ma, J.; Dong, L.; Gong, G. (2019)

  • Noninvasive assessment of severity of liver brosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. We aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classi er based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree Classi er (DTC), Random Forest Classi er (RFC), Logistic Regression Classi er (LRC), and Support Vector Classi er (SVC) for liver brosis severity assessment. Training and testing samples account for 50% of the data set, respectively. The best indicator combinations were selected in random combinations of 24 indicators includin...
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  • Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods

    • Authors: Tao, R.;  Advisor: -;  Participants: Zhang, S.; Huang, X.; Tao, M.; Ma, J.; Ma, S.; Zhang, C.; Zhang, T.; Tang, F.; Lu, J.; Shen, C.; Xie, X. (2019)

  • Objective: This study focused on developing a fast and accurate automatic ischemic heart disease detection/localization methodology. Methods: Twavewas segmented from averaged Magnetocardiography (MCG) recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and informa-tion theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 features were used in this stage. To localize ischemia, we classified IHD group according to stenosis ...
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