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  • Evaluation of Machine Learning Approaches for Android Energy Bugs Detection With Revision Commits

    • Authors: Zhu, C.;  Advisor: -;  Participants: Zhu, Z.; Xie, Y.; Jiang, W.; Zhang, G. (2019)

  • Performances of smartphones are profoundly affected by battery life. Maximizing the amount of usage of energy is essential to extend battery life. However, developers might concentrate more on the functionality of applications while ignoring the energy bugs that drain the battery during the development process. There are no quantitative approaches to detect these energy bugs introduced in this fast-paced development process. In this paper, we employ a system-call-based approach to develop a power consumption model for Android devices. Data that measure the energy consumption of mobile devices under different testing scenarios with the number of triggered system calls are utilized in the model training process. A balanced recursive feature elimination with cross-validation approach i...
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  • Comparison of Feature Selection Methods and Machine Learning Classifiers for Radiomics Analysis in Glioma Grading

    • Authors: Sun, P.;  Advisor: -;  Participants: Wang, D.; Mok, V. C.; Shi, L. (2019)

  • Radiomics-based researches have shown predictive abilities with machine-learning approaches. However, it is still unknown whether different radiomics strategies affect the prediction performance. The aim of this study was to compare the prediction performance of frequently utilized radiomics feature selection and classi cation methods in glioma grading. Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects. Then, the diagnostic performance of sixteen feature selection and fteen classi cation methods were evaluated by using two different test modes: ten-fold cross-validation and percentage split. Balanced accuracy and area under the curve (AUC) of the receiver operating characteristic were used to evalu...
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