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Full-Text Articles in Library and Information Science
Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri
Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri
Wayne State University Dissertations
Predicting time-to-event from longitudinal data where different events occur at different time points is an extremely important problem in several domains such as healthcare, economics, social networks and seismology, to name a few. A unique challenge in this problem involves building predictive models from right censored data (also called as survival data). This is a phenomenon where instances whose event of interest are not yet observed within a given observation time window and are considered to be right censored. Effective models for predicting time-to-event labels from such right censored data with good accuracy can have a significant impact in these …
A Prediction Modeling Framework For Noisy Welding Quality Data, Junheung Park
A Prediction Modeling Framework For Noisy Welding Quality Data, Junheung Park
Wayne State University Dissertations
Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction …
A Framework For Personalized Dynamic Cross-Selling In E-Commerce Retailing, Arun K. Timalsina
A Framework For Personalized Dynamic Cross-Selling In E-Commerce Retailing, Arun K. Timalsina
Wayne State University Dissertations
Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and …