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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Machine Learning Approach To Stability Analysis Of Semiconductor Memory Element, Ravindra Thanniru, Gautam Kapila, Nibhrat Lohia
Machine Learning Approach To Stability Analysis Of Semiconductor Memory Element, Ravindra Thanniru, Gautam Kapila, Nibhrat Lohia
SMU Data Science Review
Memory stability analysis traditionally relied heavily on circuit simulation-based approaches that run Monte Carlo (MC) analysis over various manufacturing and use condition parameters. This paper researches application of Machine Learning approaches for memory element failure analysis which could mimic simulation-like accuracy and minimize the need for engineers to rely heavily on simulators for their validations. Both regressor and classifier algorithms are benchmarked for accuracy and recall scores. A high recall score implies fewer escapes of fails to field and is the metric of choice for comparing algorithm. The paper identifies that recall score in excess of 0.97 can be achieved …
Modeling Electric Energy Generation In Ercot During Extreme Weather Events And The Impact Renewable Energy Has On Grid Reliability, Adam R. Ruthford, Bivin Sadler
Modeling Electric Energy Generation In Ercot During Extreme Weather Events And The Impact Renewable Energy Has On Grid Reliability, Adam R. Ruthford, Bivin Sadler
SMU Data Science Review
This paper shows the inadequacy of current grid backup supplies, their ongoing threat to grid reliability, and the increased risk of customer blackouts. This paper also examines reliability concerns caused by renewable energy sources under more normal operating conditions. This paper seeks to model the impacts of extreme weather on ERCOTs grid. Unusual weather patterns require that the electrical grid operates in conditions outside normal operating parameters. Electrical system demand can spike well above normal levels. At the same time, weather influences can change the operating envelope of electrical generation equipment. The ERCOT grid has changed from its’ traditional mix …
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark
SMU Data Science Review
In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing …
Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler
Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler
SMU Data Science Review
This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …
Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater
Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater
SMU Data Science Review
Machine reading comprehension and question answering are topics of considerable focus in the field of Natural Language Processing (NLP). In recent years, language models like Bidirectional Encoder Representations from Transformers (BERT) [3] have been very successful in language related tasks like question answering. The difficulty of the question answering task lies in developing accurate representations of language and being able to produce answers for questions. In this study, the focus is to investigate how to train and fine tune a BERT model to improve its performance on BioASQ, a challenge on large scale biomedical question answering. Our most accurate BERT …