Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Data Science
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Electronic Theses and Dissertations
The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …
Beyond Accuracy In Machine Learning., Aneseh Alvanpour
Beyond Accuracy In Machine Learning., Aneseh Alvanpour
Electronic Theses and Dissertations
Machine Learning (ML) algorithms are widely used in our daily lives. The need to increase the accuracy of ML models has led to building increasingly powerful and complex algorithms known as black-box models which do not provide any explanations about the reasons behind their output. On the other hand, there are white-box ML models which are inherently interpretable while having lower accuracy compared to black-box models. To have a productive and practical algorithmic decision system, precise predictions may not be sufficient. The system may need to have transparency and be able to provide explanations, especially in applications with safety-critical contexts …
New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene
New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene
Electronic Theses and Dissertations
Recommender Systems are widely used to personalize the user experience in a diverse set of online applications ranging from e-commerce and education to social media and online entertainment. These State of the Art AI systems can suffer from several biases that may occur at different stages of the recommendation life-cycle. For instance, using biased data to train recommendation models may lead to several issues, such as the discrepancy between online and offline evaluation, decreasing the recommendation performance, and hurting the user experience. Bias can occur during the data collection stage where the data inherits the user-item interaction biases, such as …
New Accurate, Explainable, And Unbiased Machine Learning Models For Recommendation With Implicit Feedback., Khalil Damak
New Accurate, Explainable, And Unbiased Machine Learning Models For Recommendation With Implicit Feedback., Khalil Damak
Electronic Theses and Dissertations
Recommender systems have become ubiquitous Artificial Intelligence (AI) tools that play an important role in filtering online information in our daily lives. Whether we are shopping, browsing movies, or listening to music online, AI recommender systems are working behind the scene to provide us with curated and personalized content, that has been predicted to be relevant to our interest. The increasing prevalence of recommender systems has challenged researchers to develop powerful algorithms that can deliver recommendations with increasing accuracy. In addition to the predictive accuracy of recommender systems, recent research has also started paying attention to their fairness, in particular …
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Electronic Theses and Dissertations
Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The …