SLIIT Journal of Humanities and Sciences [SJHS]

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Author: search.filters.author.Amaratunga, DEnd date: 2024

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    PublicationOpen Access
    Comparing Methods for Detecting Anomalous Values in Automated Laboratory Processes
    (Faculty of Humanities and Sciences, SLIIT, 2024-05-30) Madhushanka, H. M. S; Amaratunga, D
    Outlier detection is used in many domains. In automated laboratory processes, detecting anomalous values is critical for ensuring the reliability of experimental results. This study compares various outlier detection methods, including traditional statistical approaches like Mahalanobis distance, Median and mean absolute deviation (MAD), as well as modern machine learning techniques such as Isolation Forest, Angle Based Outlier Detection (ABOD), and Local Outlier Factor (LOF). The performances of these methods were evaluated using simulated multivariate data, with different types of outliers and levels of contamination. Comparisons are made using sensitivity, precision, and mainly the F2 score, a weighted metric of sensitivity and precision that gives more weight to precision. The results show that in univariate settings, the Median MAD method works consistently well. For multivariate scenarios, Mahalanobis methods with Minimum Covariance Determinant estimates and Minimum Volume Ellipsoid estimates work well even for high contamination percentages. This study highlights the importance of selecting an appropriate outlier detection method for the situation.
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    PublicationOpen Access
    Reference Ranges and Control Limits that are Resistant to Baseline Outliers
    (Faculty of Humanities and Sciences, SLIIT, 2022-12-24) Amaratunga, D
    Reference ranges and control limits are used in many settings – for example, to assess a person’s health or to monitor the stability of a manufacturing process. Such ranges are established based on a baseline sample of what is considered normal data, but it is not possible to always avoid a few outliers being present even in this sample. If, as is common, the range is calculated using statistics, such as the mean and standard deviation, which could be influenced by outliers, then the use of such a range could adversely affect the decisions made. This can be avoided by constructing the reference range using statistics that are resistant to outliers. In this paper, we studied possible approaches and found two methods that had superior performance overall: one based on MM-estimation and one based on a form of Winsorization.
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    PublicationEmbargo
    Comparing Trends in Data (with Applications to COVID and Image Data)
    (Faculty of Humanities and Sciences,SLIIT, 2021-09-25) Amaratunga, D; Cabrera, J
    Many applications involve looking at and comparing trends in data. We will discuss some statistics that can be used to assess the similarity or dissimilarity between pairs of cumulative trends. These statistics can then be used to study sets of trends – for example, to cluster them or to compare them across different groups We will describe one possible approach and illustrate its use in two case studies. In the first case study, we studied the trend over time of COVID-19 in New Jersey in the USA– it was found that areas close to New York City had significantly different (more rapidly increasing) cumulative trends compared to areas further from New York City during the early days of the pandemic, but this difference dissipated as the pandemic progressed and spread within New Jersey itself. In the second case study, we compared two sets of CT scan images of lungs – a significant difference could be detected between COPD-diseased lungs and normal lungs. Overall, the method performed well and detected insightful differences.