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Authors: Bao, T.; Advisor: -; Participants: Klatt, B. N.; Whitney, S. L.; Sienko, K. H.; Wiens, J. (2019) - Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to
the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1–5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing the performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SV...
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Authors: Otoum, S.; Advisor: -; Participants: Kantarci, B.; Mouftah, H. T. (2019) - In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for
IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously
proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS. Index Terms—Wireless sensor network, cybersecurity,
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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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Authors: Alimi, R.; Advisor: -; Participants: Ivry, A.; Fisher, E.; Weiss, E. (2019) - Modern magnetic sensor arrays conventionally use state-of-the-art low-power magnetometers such as parallel
and orthogonal fluxgates. Low-power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened out during production, the conventional noise measurement does not always catch the dc jumps because of their sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps could be easily setected in a shielded environment, when deployed in the presence of natural noise and clutter, it can be hard to positively detect ...
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Authors: Khalajzadeh, Hourieh; Advisor: -; Participants: Abdelrazek, Mohamed; Grundy, John; Hosking, John; He, Qiang (2019) - There has been a very large growth in interest in big data analytics to discover patterns and insights. A major challenge in this domain is the need to combine domain knowledge – what the data means (semantics) and what it is used for – with advanced data analytics and visualization techniques to mine and communicate important information from the huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics applications. However, delivering such big data analytics applications requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillsets usually take a long time to build and have high running costs. ...
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Authors: Ma, S.; Advisor: -; Participants: Dai, J.; Lu, S.; Li, H.; Zhang, H.; Du, C.; Li, S. (2019) - In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a exible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Speci cally, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classi cation. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulat...
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Authors: Li, W.; Advisor: -; Participants: Ni, L.; Li, Z.; Duan, S.; Wu, H. (2019) - Land surface temperature (LST) is described as one of the most important environmental parameters of the land
surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs),
support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m,
and downscaling results would be validated by the r...
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Authors: Xie, J.; Advisor: -; Participants: Yu, F. R.; Huang, T.; Xie, R.; Liu, J.; Wang, C.; Liu, Y. (2019) - In recent years, with the rapid development of current Internet and mobile communication technologies, the
infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN)
brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to ...
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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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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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