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  1. ; ( , BMC Health Services Research)
    Abstract Background

    Acute neurological complications are some of the leading causes of death and disability in the U.S. The medical professionals that treat patients in this setting are tasked with deciding where (e.g., home or facility), how, and when to discharge these patients. It is important to be able to predict potential patient discharge outcomes as early as possible during the patient’s hospital stay and to know what factors influence the development of discharge planning. This study carried out two parallel experiments: A multi-class outcome (patient discharge targets of ‘home’, ‘nursing facility’, ‘rehab’, ‘death’) and binary class outcome (‘home’ vs. ‘non-home’). The goal of this study is to develop early predictive models for each experiment exploring which patient characteristics and clinical variables significantly influence discharge planning of patients based on the data that are available only within 24 h of their hospital admission. 

     Method

    Our methodology centers around building and training five different machine learning models followed by testing and tuning those models to find the best-suited predictor for each experiment with a dataset of 5,245 adult patients with neurological conditions taken from the eICU-CRD database.

     Results

    The results of this study show XGBoost to be the most effective model for predicting between four common discharge outcomes of ‘home’, ‘nursing facility’, ‘rehab’, and ‘death’, with 71% average c-statistic. The XGBoost model was also the best-performer in the binary outcome experiment with a c-statistic of 76%. This article also explores the accuracy, reliability, and interpretability of the best performing models in each experiment by identifying and analyzing the features that are most impactful to the predictions.

     Conclusions

    The acceptable accuracy and interpretability of the predictive models based on early admission data suggests that the models can be used in a suggestive context to help guide healthcare providers in efforts of planning effective and equitable discharge recommendations.

     
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  2. ; ; ; ( , Sensors)
    While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization. 
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  3. ; ; ; ( , 2022 International Conference on Computer Communications and Networks (ICCCN))
    Generating paths of a mobile device in indoor space by sensing its Bluetooth RSSI value is challenging but has real-world applications. Although Bluetooth RSSI suffers from different factors that limit its usability, this research shows that it can still be used to detect mobility and, over a duration of time, can be used to form paths. This poster presents algorithms that can create a path of a moving mobile device by sensing its RSSI values over time and then presents early results of the algorithm's effectiveness while tracking health practitioners' movement within a community care clinic setting. 
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  4. ; ( , 19th IEEE International Conference on Machine Learning and Applications (ICMLA))
    null (Ed.)
    With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training. 
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  5. ; ; ; ( , 2019 14th International Conference on Computer Science & Education (ICCSE))
    It is believed that if students are well engaged in the learning process within the classroom, they will continue the learning process independently outside the classroom. To facilitate such out-of-class learning, there is a plethora of traditional techniques with a variety of learning theoretical backgrounds. While out-of-class activities based on these techniques have shown to improve a student’s overall quality of learning, traditional activities lack the supervision, instant feedback, and personalization that the current generation of students expects. With the rising cost of college tuition, many of today’s students are working more hours outside of an educational setting and therefore need more supervision and encouragement than their predecessors. These factors make traditional out-of-class activities not effective to achieve the desired level of student learning and engagement outside the classroom. The faculty needs to rethink ways to redesign traditional out-of-class activities to make these activities more effective for this generation of students. This paper presents a review of the literature on and categorization of traditional out-of-class activities. The paper also discusses the results of a survey of what the faculty is doing to engage and continue student learning outside the classroom. Finally, the paper presents a new way of designing and delivering out-of-class activities that have the potential to increase student engagement with the help of instructional scaffolding, interactive activities, and personalization and adaptation. 
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  6. ; ; ( , 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))
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  7. ; ; ; ( , Educational Technology Research and Development)
    Mobile devices are being used profusely in the classrooms to improve passive learning environments and to enhance student comprehension. However, with respect to students’ active involvement in problem solving activities, the typical usage of the mobile devices in answering multiple choice and true/false questions is not adequate and the use of mobile devices need to be expanded to include dynamic and interactive problem-solving activities to better satisfy students’ learning needs. To facilitate such interactive problem solving using mobile devices, a comprehensive software environment is necessary. This paper details the design, deployment and evaluation of Mobile Response System (MRS) software that facilitates execution and assessment of multi-step in-class interactive problem-solving activities using mobile devices. MRS is an active learning tool, which engages students with the visual representation of a problem that spans on multiple screens, allows them to interact with that, and makes them realize the consequences of their actions instantly and visually. The immediate and automated grading feature of MRS enables a feedback-driven and evidence-based teaching methodology, which is important to improve the quality of classroom learning. MRS is designed to be independent of any interactive problem or its domain. Therefore, it allows easier integration of interactive activity Apps developed by others and can be used in any discipline. The results obtained from software metrics and runtime performance data verified the quality of the software. Additionally, the in-class assessment data verified that the MRS software is a helpful intervention for improving student comprehension and satisfaction. 
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  8. ; ; ; ( , 49th ACM Technical Symposium on Computer Science Education)
    Classroom formative assessment augmented with timely and frequent feedback has become one of the most prominent teaching practices in education research. On the context of Computer Science (CS) courses that expose students to the functionality and dynamic aspects of various algorithms, traditionally, students are evaluated by exploring in-class paper-based exercises. In these exercises, they simulate the steps of an algorithm by drawing several instances of a diagram. This traditional approach is time consuming, is inherently difficult for students to express the dynamics of an algorithm, does not allow timely feedback, and restricts the number of exercises that students can practice and receive feedback on. Mobile Response System (MRS) is a software environment that facilitates in-class exercises and their real-time assessment using mobile devices and therefore focuses on addressing many of the above-mentioned problems. In this paper, we present results of eight semester-long studies using MRS in two of the required CS courses at Winston-Salem State University (WSSU). Our experimental evaluation shows the educational benefits of the proposed approach in terms of enhanced student retention of covered concepts, reduced failing rate, and increased student engagement and satisfaction. 
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  9. ( , Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education)
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  10. ; ( , 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud))
    Mobile learning environments have the benefit of facilitating real time student learning and assessment. However, most of such learning environments only support static or traditional learning activities. In STEM disciplines, we need more active and engaging activities and mobile learning environments should be able to support such dynamic activities. By designing such learning environments to run completely on the cloud will limit its extensibility and will not accommodate interactive activities developed by anyone other than the developer of such learning environment. Instead, we argue that by incorporating cloud services in a traditional software architecture will allow the flexibility to develop and deploy interactive problem solving activities along with versatility that cloud computing brings. This paper presents Mobile Response System (MRS) that facilitates in-class interactive problem solving using mobile devices. MRS uses cloud services in the infrastructure to minimize instructor’s workload, gives students transparent access, and makes the system failsafe alongside providing extensibility to any discipline. 
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