Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia
Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia
SMU Data Science Review
In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that …
A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen
A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen
SMU Data Science Review
The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …
Analysis Of The Electric Power Outage Data And Prediction Of Electric Power Outage For Major Metropolitan Areas In Texas Using Machine Learning And Time Series Methods, Renfeng Wang, Venkata Leela 'Mg' Vanga, Zachary B. Zaiken, Jonathan Bennett
Analysis Of The Electric Power Outage Data And Prediction Of Electric Power Outage For Major Metropolitan Areas In Texas Using Machine Learning And Time Series Methods, Renfeng Wang, Venkata Leela 'Mg' Vanga, Zachary B. Zaiken, Jonathan Bennett
SMU Data Science Review
With growing energy usage, power outages affect millions of households. This case study focuses on gathering power outage historical data, modifying the data to attach weather attributes, and gathering ERCOT energy market conditions for Dallas-Fort Worth and Houston metropolitan areas of Texas. The transformed data is then analyzed using machine learning algorithms including, but not limited to, Regression, Random Forests and XGBoost to consider current weather and ERCOT features and predict power outage percentage for locations. The transformed data is also trained using time series models and serially correlated models including Autoregression and Vector Autoregression. This study also focuses on …