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  • Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

    • Authors: Zhang, S.;  Advisor: -;  Participants: Bamakan, S. M. H.; Qu, Q.; Li, S. (2019)

  • With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to cons...
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  • A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System

    • Authors: Kasongo, S. M.;  Advisor: -;  Participants: Sun, Y. (2019)

  • In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a lter-based feature selection algorithm. The FFDNN-IDS is evaluated usin...
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