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- All Subjects: Machine Learning
- All Subjects: Arduinos
- Creators: Meuth, Ryan
- Resource Type: Text
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.
This work details the process of designing and implementing an embedded system
utilized to take measurements from a water cooler and post that data onto a publicly accessible web server. It embraces the Web 4.0, Internet of Things, mindset of making everyday appliances web accessible. The project was designed to satisfy the needs of a local faculty member who wished to know the water levels available in his office water cooler, potentially saving him the disappointment of discovering an empty container.
This project utilizes an Arduino microprocessor, an ESP 8266 Wi-Fi module, and a variety of sensors to detect water levels in filtered water unit located on the fourth floor of the the Brickyard Building, BYENG, at Arizona State University. This implementation will not interfere with the system already set in place to store and transfer water. The level of accuracy in water levels is expected to give the ability to discern +/- 1.5 liters of water. This system will send will send information to a created web service from which anyone with internet capabilities can gain access. The interface will display current water levels and attempt to predict at what time the water levels will be depleted. In the short term, this information will be useful for individuals on the floor to discern when they are able to extract water from the system. Overtime, the information this system gathers will map the drinking trends of the floor and can allow for a scheduling of water delivery that is more consistent with the demand of those working on the floor.
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.