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Full-Text Articles in Computer Engineering
Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito
Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito
Electronic Thesis and Dissertation Repository
Science autonomy onboard spacecraft can optimize image return by prioritizing downlink of meaningful data. Martian polygonally cracked ground is actively studied by planetary geologists and may be indicative of subsurface water. Filtering images containing these polygonal features can be used as a case study for science autonomy and to reduce the overhead associated with parsing through Martian surface images. This thesis demonstrates the use of deep learning techniques in the classification of Martian polygonally patterned ground from HiRISE images. Three tasks are considered, a binary classification to identify images containing polygons, multiclass classification distinguishing different polygon types and semantic segmentation …
Behavioral Biometrics-Based Continuous User Authentication, Sanket Vilas Salunke
Behavioral Biometrics-Based Continuous User Authentication, Sanket Vilas Salunke
Electronic Thesis and Dissertation Repository
The field of cybersecurity is exploring new ways to defend against cyber-attacks, including a technique called continuous user authentication. This method uses keystroke (typing) data to continuously match the user's typing pattern with patterns previously recorded using artificial intelligence (AI) to identify the user. While this approach has the potential to improve security, it also has some challenges, including the time it takes to register a user, the performance of machine learning algorithms on real-world data, and latency within the system. In this study, the researchers proposed solutions to these issues by using transfer learning to reduce user registration time, …
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Electronic Thesis and Dissertation Repository
Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …