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- Creators: Computer Science and Engineering Program
Description
Uninformed people frequently kill snakes without knowing whether they are venomous or harmless, fearing for their safety. To prevent unnecessary killings and to encourage people to be safe around venomous snakes, a proper identification is important. This work seeks to preserve wild native Arizona snakes and promote a general interest in them by using a bag of features approach for classifying native Arizona snakes in images as venomous or non-venomous. The image category classifier was implemented in MATLAB and trained on a set of 245 images of native Arizona snakes (171 non-venomous, 74 venomous). To test this approach, 10-fold cross-validation was performed and the average accuracy was 0.7772. While this approach is functional, the results could be improved, ideally with a higher average accuracy, in order to be reliable. In false positives, the features may have been associated with the color or pattern, which is similar between venomous and non-venomous snakes due to mimicry. Polymorphic traits, color morphs, variation, and juveniles that may exhibit different colors can cause false negatives and misclassification. Future work involves pre-training image processing such as improving the brightness and contrast or converting to grayscale, interactively specifying or generating regions of interest for feature detection, and targeting reducing the false negative rate and improve the true positive rate. Further study is needed with a larger and balanced image set to evaluate its performance. This work may potentially serve as a tool for herpetologists to assist in their field research and to classify large image sets.
ContributorsIp, Melissa A (Author) / Li, Baoxin (Thesis director) / Chandakkar, Parag (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Recent advances in quantum computing have broadened the available techniques towards addressing existing computing problems. One area of interest is that of the emerging field of machine learning. The intersection of these fields, quantum machine learning, has the ability to perform high impact work such as that in the health industry. Use cases seen in previous research include that of the detection of illnesses in medical imaging through image classification. In this work, we explore the utilization of a hybrid quantum-classical approach for the classification of brain Magnetic Resonance Imaging (MRI) images for brain tumor detection utilizing public Kaggle datasets. More specifically, we aim to assess the performance and utility of a hybrid model, comprised of a classical pretrained portion and a quantum variational circuit. We will compare these results to purely classical approaches, one utilizing transfer learning and one without, for the stated datasets. While more research should be done for proving generalized quantum advantage, our work shows potential quantum advantages in validation accuracy and sensitivity for the specified task, particularly when training with limited data availability in a minimally skewed dataset under specific conditions. Utilizing the IBM’s Qiskit Runtime Estimator with built in error mitigation, our experiments on a physical quantum system confirmed some results generated through simulations.
ContributorsDiaz, Maryannette (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05