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- All Subjects: deep learning
- Creators: Computer Science and Engineering Program
- Member of: Theses and Dissertations
- Resource Type: Text
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
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.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.