Browsing by Author "Tan, Benjamin"
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- ItemOpen AccessA voxel-level approach to brain age prediction: A quantitative method to assess regional brain aging(2023-12-05) Gianchandani, Neha; Souza, Roberto; MacDonald, Ethan; Bayat, Sayeh; Pike, Bruce; Harris, Ashley; Tan, BenjaminGlobal brain age has been used as an effective biomarker to study the correlation between brain aging and neurological disorders. However, brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide granular insights into the regional aging processes. This is essential to understand the differences in aging trajectories in healthy versus diseased subjects. In this work, a deep learning- based multitask model is proposed for voxel-level brain age prediction. The proposed model outperforms the model existing in the literature and yields valuable clinical insights when applied to both healthy and diseased populations. Most findings from the analysis align with existing studies on aging, whereas other findings are intriguing and could be potential biomarkers of early-stage neurodegeneration detection. Regional analysis is performed on the voxel-level brain age predictions to understand aging trajectories of known anatomical regions in the brain and show that there exist disparities in regional aging trajectories of healthy subjects compared to ones with underlying neurological disorders such as dementia and more specifically, Alzheimer’s disease. A comparative analysis with traditional deep learning interpretability methods showed that the proposed voxel-level approach to brain age prediction is an effective way to understand regional aging trajectories while being quantitative in nature. The source code is publicly available at https://github.com/nehagianchandani/Voxel-level-brain-age-prediction.
- ItemOpen AccessAutomated Bug Severity Prediction using Source Code Metrics, Static Analysis, and Code Representation(2022-09-12) Mashhadi, Ehsan; Hemmati, Hadi; Barcomb, Ann; Tan, BenjaminIn the past couple of decades, significant research efforts are devoted to the prediction of software bugs. However, most existing work in this domain treats all bugs the same, which is not the case in practice. It is important for a defect prediction method to estimate the severity of the identified bugs so that the higher severity ones get immediate attention. In this thesis, we provide a quantitative and qualitative study on two popular datasets (Defects4J and Bugs.jar), using 10 common source code metrics, and also two popular static analysis tools (SpotBugs and Infer) for analyzing their capability in predicting defects and their severity. We studied 3,358 buggy methods with different severity labels from 19 Java open-source projects. Results show that although code metrics are powerful in predicting buggy code, they cannot estimate the severity level of the bugs. In addition, we observed that static analysis tools have weak performance in both predicting bugs (F1 score range of 3.1%-7.1%) and their severity label (F1 score under 2%). We also manually studied the characteristics of the severe bugs to identify possible reasons behind the weak performance of code metrics and static analysis tools. Also, our categorization shows that Security bugs have high severity in most cases while Edge/Boundary faults have low severity. Furthermore, we show that code metrics and static analysis methods can be complementary in terms of estimating bug severity. For finding the effectiveness of machine learning models in predicting bug severity, we train 8 different models on code metrics only as a baseline and evaluate them based on different evaluation metrics. The overall result was not promising, but the Decision Tree and Random Forest models have better results. Then, we leveraged the pre-trained CodeBERT model to use code representation by feeding the source code input only, and the results improved significantly in the range of 29%-140% for different metrics. We also integrated code metrics into the CodeBERT model by providing two architectures named ConcatInline and ConcatCLS which enhance the CodeBERT model efficacy.
- ItemOpen AccessFeasibility of Mapping Brain Activity to the Levels of Task Complexity within Environments of Virtual Reality(2023-09-21) Perez Vite, Yobbahim Javier Israel; Hu, Yaoping; Fear, Elise; Tan, BenjaminMapping brain activity to certain levels of task complexity is essential for creating environments of Virtual Reality (VR), which could adapt to the mental states of human users. To investigate the feasibility of such mapping, the research work of this thesis took an approach of two steps. At first, the levels of task complexity were defined according to the geometric and appearance parameters of objects that the users interacted with for executing a task. By associating the parameters to the execution of the task, this step remedied qualitative descriptions of the levels in current state-of-the-art. Secondly, an empirical study of two experiments was conducted within a VR to collect brain activities (as brainwaves) of human participants (i.e., users) during the execution involving various task complexity. Using a device of encephalography (EEG) to collect the brainwaves, this step assessed several existing features derived from the brainwaves as potential indicators of feasibility. This thesis produced two significant findings: (1) the definition of task complexity is quantitative and could be suitable for describing object-oriented tasks, and (2) specific EEG features – such as engagement ratio – could indicate increased or decreased levels of task complexity. Hence, the work indicates the feasibility of mapping brain activity to the levels of task complexity. Future investigations are needed to refine the definition, and EEG features for optimizing cognitive engagement and performance by modulating the levels of task complexity. The outcomes of the investigations could have implications for training, simulation, and user experience in various VR-based applications.
- ItemOpen AccessSport and Recreational Activity Participation and Injury Risk in Elementary School Children with Probable Developmental Coordination Disorder or Attention Deficit Hyperactivity Disorder(2014-09-29) Tan, Benjamin; Emery, Carolyn; Dewey, DeborahObjective: To examine if probable Developmental Coordination Disorder (pDCD) or Attention Deficit Hyperactivity Disorder (pADHD) in elementary school children aged 8 – 13 years is associated with sport and recreation (S&R) participation and injury. Methods: Cross-sectional design. Recruitment from elementary schools in Calgary, Alberta, Canada led to 681 participants completing an anonymous questionnaire. Primary Outcome Measure: S&R participation and injury. Results: S&R participation was reported by 82.7% (95%CI; 79.8, 85.5) of children; those with co-occurring pDCD and pADHD participated less than their typically developing peers. The injury incidence rate was 2.43 injuries/1000 participation hours (95%CI; 2.06, 2.85), with no differences between study groups. Ethnicity, stressful life events and coaching were potential risk factors for injury identified by exploratory risk factor analysis. Conclusions: Children with pDCD and/or pADHD were not at greater risk of S&R injury than their typically developing peers, but those with co-occurring pDCD and pADHD participated in S&R less.
- ItemOpen AccessThe Canadian Registry for Pulmonary Fibrosis: Design and Rationale of a National Pulmonary Fibrosis Registry(2016-04-05) Ryerson, Christopher J.; Tan, Benjamin; Fell, Charlene D.; Manganas, Hélène; Shapera, Shane; Mittoo, Shikha; Sadatsafavi, Mohsen; To, Teresa; Gershon, Andrea; Fisher, Jolene H.; Johannson, Kerri A.; Hambly, Nathan; Khalil, Nasreen; Marras, Theodore K.; Morisset, Julie; Wilcox, Pearce G.; Halayko, Andrew J.; Khan, Mohammad Adil; Kolb, MartinBackground. The relative rarity and diversity of fibrotic interstitial lung disease (ILD) have made it challenging to study these diseases in single-centre cohorts. Here we describe formation of a multicentre Canadian registry that is needed to describe the outcomes of fibrotic ILD and to enable detailed healthcare utilization analyses that will be the cornerstone for future healthcare planning. Methods. The Canadian Registry for Pulmonary Fibrosis (CARE-PF) is a prospective cohort anticipated to consist of at least 2,800 patients with fibrotic ILD. CARE-PF will be used to (1) describe the natural history of fibrotic ILD, specifically determining the incidence and outcomes of acute exacerbations of ILD subtypes and (2) determine the impact of ILD and acute exacerbations of ILD on health services use and healthcare costs in the Canadian population. Consecutive patients with fibrotic ILD will be recruited from five Canadian ILD centres over a period of five years. Patients will be followed up as clinically indicated and will complete standardized questionnaires at each clinic visit. Prespecified outcomes and health services use will be measured based on self-report and linkage to provincial health administrative databases. Conclusion. CARE-PF will be among the largest prospective multicentre ILD registries in the world, providing detailed data on the natural history of fibrotic ILD and the healthcare resources used by these patients. As the largest and most comprehensive cohort of Canadian ILD patients, CARE-PF establishes a network for future clinical research and early phase clinical trials and provides a platform for translational and basic science research.
- ItemOpen AccessUtilization of Natural Language Processing for Extracting Smart Cities Requirements from Large Social Media Text(2024-05-14) Mirshafiee Khoozani, Mitra Sadat; Barcomb, Ann; Tan, Benjamin; Messier, Geoffrey; Fapojuwo, AbrahamMajor organizations such as urban centers worldwide face challenges from rapid population growth and evolving demands, requiring innovative approaches to stay responsive to residents' needs. This challenge is exemplified by the city of Calgary, where an automated system for aggregating and categorizing resident feedback could improve city planning. What people find important and useful can be seen in the articles they post on social media. One method for determining the performance of urban services and assets for citizens is paying attention to these data generated by the residents. In this regard, we need to examine datasets wherein writing is the primary form of citizen engagement (direct messages, requests, comments, complaints, etc.). To interpret this data, it is necessary to use appropriate tools and techniques for data processing and analysis of large volumes of unstructured text. Some of the most effective tools used by researchers nowadays falls into the scope of computational linguistics, specifically Natural language processing (NLP). Furthermore, Twitter is one of the primary platforms where individuals freely voice their opinions and concerns. In this study, we develop an automated workflow that can scrape, classify, and display tweets in a simplistic view. With the help of this system, local officials will be able to speed up the decision-making process when considering citizens' current problems. Following our research question, we look into the optimal scraping criteria, explore a variety of methods for topic and emotions analysis, and validate these methods both using automatic evaluation and manual assessment. As a result, we are able to identify issues related to city development, senior citizens, taxes, and unemployment using our best performing models (BERTopic for topic modeling and few-shot learning using Setfit for emotion analysis.) Afterward, we collect city employees' opinion regarding our research to determine the usefulness and applicability of this approach. Overall, we demonstrate how delving into these analyses can complement the current systems in place for urban planning.