Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- File Type
Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Fire Progression, Ashwini Badgujar, L. Zheng, S. Hu, Aj Purdy
Fire Progression, Ashwini Badgujar, L. Zheng, S. Hu, Aj Purdy
Creative Activity and Research Day - CARD
Fires have grown up to 70% in recent years. Fire Progression is a Machine Learning research project wherein we are trying to predict the direction in which the fire might grow in future. We are using Machine Learning technique and features like surface temperature, air temperature, moisture, precipitation and other additional parameters to predict the progression.
Active/Transfer Learning With Medical Imaging, Nicholas Kebbas, Maggie Lu
Active/Transfer Learning With Medical Imaging, Nicholas Kebbas, Maggie Lu
Creative Activity and Research Day - CARD
We've developed a robust web application to assist researchers in improving the accuracy of their Machine Learning Models. The system provides an image labeling web interface so that researchers can label and classify the models, and multiple data visualizations that help researchers identify and resolve inconsistencies between the classifications determined by their models and user feedback.
Evaluation Of Visualization Techniques For Communicating Off-Screen Data, Tony Jimenez
Evaluation Of Visualization Techniques For Communicating Off-Screen Data, Tony Jimenez
Creative Activity and Research Day - CARD
Worldwide, the use of mobile devices like tablets has begun to integrate themselves in people’s daily lives. People have thus begun to bring over desktop applications, more specifically visualization applications, into the mobile atmosphere. However, this brings forth some challenges, like how to manage screen space and how the visualizations should present the relevant information. Our take on this was to use various aggregations that would allow users to see data elements that would otherwise be off-screen. We thought it was best to create a variety of different aggregations, allowing us to figure out the best method of the group. …
Yelp Improved : Aggregating Restaurant Reviews, Kunal Sonar
Yelp Improved : Aggregating Restaurant Reviews, Kunal Sonar
Creative Activity and Research Day - CARD
In the near future, online food delivery service companies would occupy a big market share in the food industry. This project aims to provide factual information from customer reviews as part of the numerous innovations in place to drive business and demands. Natural Language Processing is used to provide a comprehensive view of individual restaurants using technologies like NLTK, SpaCy, Gensim and Sklearn. Data of one million Las Vegas restaurant customer reviews is curated from the Yelp Dataset Challenge. Reviews are pre-processed, split into chunks of phrases and mapped to attributes like food, budget, service etc. These attributes are derived …
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Creative Activity and Research Day - CARD
With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …
Forecasting Smart Meter Energy Usage Using Distributed Systems And Machine Learning, Feiran Ji, Chris Dong, Lingzhi Du, Zizhen Song, Yuedi Zheng, Paul Intrevado
Forecasting Smart Meter Energy Usage Using Distributed Systems And Machine Learning, Feiran Ji, Chris Dong, Lingzhi Du, Zizhen Song, Yuedi Zheng, Paul Intrevado
Creative Activity and Research Day - CARD
In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million(10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems …
Cerebral Necrosis Research With Machine Learning Techniques, Sangyu Shen
Cerebral Necrosis Research With Machine Learning Techniques, Sangyu Shen
Creative Activity and Research Day - CARD
Cerebral necrosis after radiotherapy for patients with brain metastases is being recognized as a problem more common than previously estimated. To better understand the onset of necrosis and reduce its occurrence, we studied the relationships between features of patients and necrosis onset with machine learning techniques.
An Optimization Approach To Automate The Generation Of Radiotherapy Treatment Plans, Qian Li
An Optimization Approach To Automate The Generation Of Radiotherapy Treatment Plans, Qian Li
Creative Activity and Research Day - CARD
The main goal of radiotherapy is to deliver a specified dose of radiation directly to the tumor while minimizing radiation damage to healthy tissues. Currently, the treatment plan is being developed by professional planners using a commercial treatment planning system. In this treatment planning system, the planner modifies the objectives and weights of the objectives until an ideal combination of doses is achieved. This arbitrary process can cost a few hours or a day to finish. My research aims to automate the generation of the plans by implementing an optimization algorithm on top of the treatment planning system using gradient …