Matching Items (3)

Using Bag of Words Approach for Classifying Native Arizona Snakes in Images as Venomous or Non-Venomous

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

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.

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  • 2017-05

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Deep Learning Application to Improve Quality of Life in Diabetes

Description

Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to

Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.

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Date Created
  • 2021-05

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Using Machine Learning to Objectively Determine Colorimetric Assay Results from Cell Phone Photos Taken Under Ambient Lighting

Description

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

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Date Created
  • 2021-05