2021 Systems and Information Engineering Design Symposium (SIEDS)最新文献

{"title":"Neutrosophic Fuzzy Weighted Saving Heuristic for COVID-19 Vaccination","authors":"Esra Çakır, M. Taş, Z. Ulukan","doi":"10.1109/SIEDS52267.2021.9483794","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483794","url":null,"abstract":"Vaccination procedures, which are the most effective way to deal with the COVID-19 pandemic, have started worldwide. Since hospitals and health centers are used as vaccination centers, collecting people in one place can lead to the spread of other diseases. Therefore, the use of temporary vaccination clinics is encouraged for mass vaccination. In this study, the number of temporary clinics that need to be placed in candidate locations and the regions they serve are investigated. While the weights of the candidate places are determined by a single-valued neutrosophic fuzzy multi criteria decision-making method, the temporary vaccination clinics are assigned to the candidate locations via savings heuristic. The proposed neutrosophic fuzzy MCDM integrated saving heuristic methodology is applied on an illustrative example. The results are thought to be helpful in future multi-facility layout models.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123902460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Room-Level Localization and Automated Contact Tracing via Internet of Things (IoT) Nodes and Machine Learning Algorithm","authors":"Zachary Yorio, Samy El-Tawab, M. Heydari","doi":"10.1109/SIEDS52267.2021.9483667","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483667","url":null,"abstract":"Contact tracing has become a vital practice in reducing the spread of COVID-19 among staff in all industries, especially those in high-risk occupations such as healthcare workers. Our research team has investigated how wearable IoT devices can alleviate this problem by utilizing 802.11 wireless beacon frames broadcasted from pre-existing access points in a building to achieve room-level localization. Notable improvements to this low-cost localization technique’s accuracy are achieved via machine learning by implementing the random forest algorithm. Using random forest, historical data can train the model and make more informed decisions while tracking other nodes in the future. In this project, employees’ and patients’ locations while in a building (e.g., a healthcare facility) can be time-stamped and stored in a database. With this data available, contact tracing can be automated and accurately conducted, allowing those who have been in contact with a confirmed positive COVID-19 case to be notified and quarantined immediately. This paper presents the application of the random forest algorithm on broadcast frame data collected in February of 2020 at Sentara RMH in Harrisonburg, Virginia, USA. Our research demonstrates the combination of affordability and accuracy possible in an IoT beacon frame-based localization system that allows for historical recall of room-level localization data.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Application of the Partially Observed Markov Process in the Analysis of Transmission Dynamics of COVID-19 via Wastewater","authors":"Sihang Jiang, Kristen Maggard, Heman Shakeri, Michael D. Porter","doi":"10.1109/SIEDS52267.2021.9483793","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483793","url":null,"abstract":"As the ongoing outbreak of Coronavirus Disease 2019 (COVID-19) is severely affecting all over the world, analysis of the transmission of COVID-19 is of more and more interest. We focus on the application of compartmental models in the analysis of transmission of COVID-19 based on the detected viral load in wastewater and the reported number of cases. The measurement of COVID-19 RNA concentrations in primary sludge gives us information about the virus prevalence on a population level. Since the transmission of COVID-19 is a partially observed Markov process including different states, we consider a likelihood-based approach to our statistical inference to understand the inner relationship between different states and how COVID-19 actually transmits. Understanding the transmission dynamics of COVID-19 could give suggestions on public policies.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121808621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Schema to Formalize Education Research & Development Using Natural Language Processing","authors":"Hannah Frederick, Haizhu Hong, Margaret Williams, Amanda West, Briana K. Wright","doi":"10.1109/SIEDS52267.2021.9483781","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483781","url":null,"abstract":"Our work aims to aid in the development of an open source data schema for educational interventions by implementing natural language processing (NLP) techniques on publications within What Works Clearinghouse (WWC) and the Education Resources Information Center (ERIC). A data schema demonstrates the relationships between individual elements of interest (in this case, research in education) and collectively documents elements in a data dictionary. To facilitate the creation of this educational data schema, we first run a two-topic latent Dirichlet allocation (LDA) model on the titles and abstracts of papers that met WWC standards without reservation against those of papers that did not, separated by math and reading subdomains. We find that the distributions of allocation to these two topics suggest structural differences between WWC and non-WWC literature. We then implement Term Frequency-Inverse Document Frequency (TF-IDF) scoring to study the vocabulary within WWC titles and abstracts and determine the most relevant unigrams and bigrams currently present in WWC. Finally, we utilize an LDA model again to cluster WWC titles and abstracts into topics, or sets of words, grouped by underlying semantic similarities. We find that 11 topics are the optimal number of subtopics in WWC with an average coherence score of 0.4096 among the 39 out of 50 models that returned 11 as the optimal number of topics. Based on the TF-IDF and LDA methods presented, we can begin to identify core themes of high-quality literature that will better inform the creation of a universal data schema within education research.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122362985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Checkbox Detection on Rwandan Perioperative Flowsheets using Convolutional Neural Network","authors":"Emily Murphy, Swathi Samuel, Joseph Cho, W. Adorno, Marcel Durieux, Donald Brown, Christian Ndaribitse","doi":"10.1109/SIEDS52267.2021.9483723","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483723","url":null,"abstract":"Millions of surgical operations are performed every year in African countries and the lack of digitization of data associated with them inhibit the ability to study the linkages of perioperative data with perioperative moralities [1]. Contrary to American operating rooms, where medical personnel are assisted by technologies that record and analyze patient vitals and other surgical data, low-income African operating rooms lack these resources and require their personnel to manually scribe this information onto paper flowsheets. In order to provide perioperative data to health care providers in Rwanda, the team designed and implemented image processing and machine learning techniques to automate checkbox detection for the digitization of surgical flowsheet data. A checkbox image is cropped based on its location with template matching and then processed through a trained convolutional neural network (CNN) to classify it as checked or unchecked. The template matching and CNN process were tested using 18 flowsheets. Of the 666 possible images, the template matching achieved an accuracy of 99.8%, and 96.7% of the cropped images were correctly classified using the CNN model.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123779705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data","authors":"Anna Bonaquist, Meredith Grehan, Owen Haines, Joseph Keogh, Tahsin Mullick, Neil Singh, Samy Shaaban, A. Radovic, Afsaneh Doryab","doi":"10.1109/SIEDS52267.2021.9483755","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483755","url":null,"abstract":"Mobile sensing and analysis of data streams collected from personal devices such as smartphones and fitness trackers have become useful tools to help health professionals monitor and treat patients outside of clinics. Research in mobile health has largely focused on feasibility studies to detect or predict a health status. Despite the development of tools for collection and processing of mobile data streams, such approaches remain ad hoc and offline. This paper presents an automated machine learning pipeline for continuous collection, processing, and analysis of mobile health data. We test this pipeline in an application for monitoring and predicting adolescents’ mental health. The paper presents system engineering considerations based on an exploratory machine learning analysis followed by the pipeline implementation.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Design and Integration of a Comprehensive Measurement System to Assess Trust in Automated Driving","authors":"Anna Madison, Abigail Arestides, Stephen Harold, Tyler Gurchiek, Kai Chang, Anthony J. Ries, N. Tenhundfeld, Elizabeth Phillips, E. D. de Visser, Chad C. Tossell","doi":"10.1109/SIEDS52267.2021.9483758","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483758","url":null,"abstract":"With the increased availability of commercially automated vehicles, trust in automation may serve a critical role in the overall system safety, rate of adoption, and user satisfaction. We developed and integrated a novel measurement system to better calibrate human-vehicle trust in driving. The system was designed to collect a comprehensive set of measures based on a validated model of trust focusing on three types: dispositional, learned, and situational. Our system was integrated into a Tesla Model X to assess different automated functions and their effects on trust and performance in real-world driving (e.g., lane changes, parking, and turns). The measurement system collects behavioral, physiological (eye and head movements), and self-report measures of trust using validated instruments. A vehicle telemetry system (Ergoneers Vehicle Testing Kit) uses a suite of sensors for capturing real driving performance data. This off-the-shelf solution is coupled with a custom mobile application for recording driver behaviors, such as engaging/disengaging automation, during on-road driving. Our initial usability evaluations of components of the system revealed that the system is easy to use, and events can be logged quickly and accurately. Our system is thus viable for data collection and can be used to model user trust behaviors in realistic on-road conditions.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132567223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending the Markowitz model with dimensionality reduction: Forecasting efficient frontiers","authors":"Nolan Alexander, W. Scherer, M. Burkett","doi":"10.1109/SIEDS52267.2021.9483775","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483775","url":null,"abstract":"The Markowitz model is an established approach to portfolio optimization that constructs efficient frontiers allowing users to make optimal tradeoffs between risk and return. However, a limitation of this approach is that it assumes future asset returns and covariances will be identical to the asset's historical data, or that these model parameters can be accurately estimated, a notion which often does not hold in practice. Markowitz efficient frontiers are square root second-order polynomials that can be represented by three parameters, thus providing a significant dimensionality reduction of the lookback covariances and growth of the assets. Using this dimensionality reduction, we propose an extension to the Markowitz model that accounts for the nonstationary behavior of the portfolio assets' return and covariance without the necessity to forecast the complex covariance matrix and assets growths, something that has proven to be extremely difficult. Our methodology allows users to forecast the three efficient frontier coefficients using a time-series regression. By observing similar efficient frontiers, this forecasted efficient frontier can be used to select optimal assets mean-variance tradeoffs (asset weights). For exploratory testing we employ a set of assets that span a large portion of the market to demonstrate and validate this new approach.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Brain Computer Interfaces Using Deep Scale-Invariant Temporal History Applied to Scalp Electroencephalogram Data","authors":"Gaurav Anand, A. Ansari, Bev Dobrenz, Yibo Wang, Brandon G. Jacques, P. Sederberg","doi":"10.1109/SIEDS52267.2021.9483789","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483789","url":null,"abstract":"Brain Computer Interface (BCI) applications employ machine learning to decode neural signals through time to generate actions. One issue facing such machine learning algorithms is how much of the past they need to decode the present. DeepSITH (Deep Scale-Invariant Temporal History), is a deep neural network with layers inspired by how the mammalian brain represents recent vs. less-recent experience. A single SITH layer maintains a log-compressed representation of the past that becomes less accurate with older events, unlike other approaches that maintain a perfect copy of events regardless of how far in the past they occurred. By stacking layers of this compressed representation, we hypothesized that DeepSITH would be able to decode patterns of neural activity from farther in the past and combine them efficiently to guide the BCI in the present. We tested our approach with the Kaggle \"Grasp and Lift challenge\" dataset. This motor movement dataset has 12 subjects, 10 series of 30 grasp and lift trials per subject, with 6 classes of events to decode. We benchmark DeepSITH performances on this dataset against another common machine learning technique for integrating features over extended time scales, long short-term memory (LSTM). DeepSITH reproducibly achieves higher accuracy in predicting motor movement events than LSTM, and also takes significantly fewer epochs and less memory to train, in comparison to LSTM. In summary, DeepSITH can efficiently process more data, with increased prediction accuracy and learning speed. This result shows that DeepSITH is an advantageous model to consider when developing BCI technologies.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131010424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining Factors of Heart Quality and Donor Acceptance in Pediatric Heart Transplants","authors":"J. Bullock, M. Grieco, Yin Liu, Ian Pedersen, Wesley Roberson, G. Wright, Peter Alonzi, M. McCulloch, Michael D. Porter","doi":"10.1109/SIEDS52267.2021.9483760","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483760","url":null,"abstract":"There is substantial need to increase donor heart utilization in pediatric heart transplantation. Almost half of pediatric heart donors are discarded, despite nearly 20% waitlist mortality. Physicians have limited time to view heart condition data and decide to accept the donor heart once the heart becomes available. Due to the large amount of data associated with each donor heart and the lack of data-driven guidelines, physicians often do not have adequate metrics to determine acceptable heart quality. This research characterizes the differences in the clinical course between accepted and rejected pediatric donor hearts. A longitudinal study assessing the effect of static and dynamic measurements on the donor heart’s function from the time of declaration of brain death to either disposal or heart procurement is developed by analyzing donor data via DonorNet, the system used by the United Network for Organ Sharing (UNOS) to match donors to a ranked order of recipients based on blood type, heart size, urgency status of the recipient, and other factors. Cardiovascular milieu (i.e. blood pressure, heart rate, medical management) and surrogate markers of organ perfusion, such as kidney and liver function, also inform our analyses and determine whether there are direct or indirect associations between these myriad markers and heart function. It also analyzes the proportion of measurements in stable and acceptable ranges over time, as well as typical minimum, maximum, and final measurements for different functions. All analyses are compared between accepted and rejected hearts using logistic regression and statistical analysis. Using the most recent measurements for each donor at 24 hours after brain death, the analysis identified significant factors in predicting donor heart acceptance: Left Ventricular Valve Dysfunction, Age, Shortening Fraction, and 4 Chamber Ejection Fraction. Additionally, visual tools were created as deliverables to aid physicians to decrease decision time and increase confidence in donor heart acceptance or rejection.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134079628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}