Engineering Commons^™

An Explainable Recommender System Based On Semantically-Aware Matrix Factorization., Mohammed Sanad Alshammari

Electronic Theses and Dissertations

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less …

Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous …

Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown …

Supervised Machine Learning Techniques For Short-Term Load Forecasting, Harish Amarasundar

Electronic Theses and Dissertations

Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been in the forefront for prediction algorithms. This Thesis is mainly aimed to provide utility companies with a better insight about the wide range of Techniques available to forecast the load demands based on different scenarios. Supervised Machine Learning Algorithms were used to come up with the best possible solution for Short-Term Electric Load forecasting. The input Data set has the hourly load values, Weather data set and other details of a Day. The models were evaluated using MAPE and …

Applied Deep Learning In Orthopaedics, William Stewart Burton Ii

Electronic Theses and Dissertations

The reemergence of deep learning in recent years has led to its successful application in a wide variety of fields. As a subfield of machine learning, deep learning offers an array of powerful algorithms for data-driven applications. Orthopaedics stands to benefit from the potential of deep learning for advancements in the field. This thesis investigated applications of deep learning for the field of orthopaedics through the development of three distinct projects.

First, algorithms were developed for the automatic segmentation of the structures in the knee from MRI. The resulting algorithms can be used to accurately segment full MRI scans in …