Browsing by Subject "Classification Tree"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Machine Vision Assisted In Situ Ichthyoplankton Imaging System
    (2013-07-12) Iyer, Neeraj; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran; Fang, Shiaofen
    Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.
  • Thumbnail Image
    Item
    Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    As experiencing pandemic related hardships (social isolation, financial distress, and health anxiety) and racial discrimination worsened Asian American’s mental health, a study examined unique behavioral health characteristics of Asian Americans compared to White and Black Americans in behavioral health treatment. Assessment data was analyzed using descriptive and chi-squared automatic interaction detection (CHAID), a machine learning approach, to detect additional differences among groups. Asian Americans had distinct patterns of behavioral health needs compared to White and African Americans. Key takeaways inform culturally responsive practice.