Browsing by Subject "SVM"

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    Machine Learning Classification of Facial Affect Recognition Deficits after Traumatic Brain Injury for Informing Rehabilitation Needs and Progress
    (2020-12) Iffat Naz, Syeda; Christopher, Lauren; King, Brian; Neumann, Dawn
    A common impairment after a traumatic brain injury (TBI) is a deficit in emotional recognition, such as inferences of others’ intentions. Some researchers have found these impairments in 39\% of the TBI population. Our research information needed to make inferences about emotions and mental states comes from visually presented, nonverbal cues (e.g., facial expressions or gestures). Theory of mind (ToM) deficits after TBI are partially explained by impaired visual attention and the processing of these important cues. This research found that patients with deficits in visual processing differ from healthy controls (HCs). Furthermore, we found visual processing problems can be determined by looking at the eye tracking data developed from industry standard eye tracking hardware and software. We predicted that the eye tracking data of the overall population is correlated to the TASIT test. The visual processing of impaired (who got at least one answer wrong from TASIT questions) and unimpaired (who got all answer correctly from TASIT questions) differs significantly. We have divided the eye-tracking data into 3 second time blocks of time series data to detect the most salient individual blocks to the TASIT score. Our preliminary results suggest that we can predict the whole population's impairment using eye-tracking data with an improved f1 score from 0.54 to 0.73. For this, we developed optimized support vector machine (SVM) and random forest (RF) classifier.
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    Predicting transit times for outbound logistics
    (2020-08) Cochenour, Brooke R.; Ben Miled, Zina; King, Brian; Kim, Dongsoo Stephen
    On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIXTM improves the accuracy of the model. Sample INRIXTM data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.