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An Engineering Analytics Based Framework for Computational Advertising Systems
Title
An Engineering Analytics Based Framework for Computational Advertising Systems.
Creator
Chen, Mengmeng, Rabelo, Luis, Lee, Gene, Keathley, Heather, Rahal, Ahmad, University of Central Florida
Abstract / Description
Engineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of...
Show moreEngineering analytics is a multifaceted landscape with a diversity of analytics tools which comes from emerging fields such as big data, machine learning, and traditional operations research. Industrial engineering is capable to optimize complex process and systems using engineering analytics elements and the traditional components such as total quality management. This dissertation has proven that industrial engineering using engineering analytics can optimize the emerging area of Computational Advertising. The key was to know the different fields very well and do the right selection. However, people first need to understand and be experts in the flow of the complex application of Computational Advertising and based on the characteristics of each step map the right field of Engineering analytics and traditional Industrial Engineering. Then build the apparatus and apply it to the respective problem in question.This dissertation consists of four research papers addressing the development of a framework to tame the complexity of computational advertising and improve its usage efficiency from an advertiser's viewpoint. This new framework and its respective systems architecture combine the use of support vector machines, Recurrent Neural Networks, Deep Learning Neural Networks, traditional neural networks, Game Theory/Auction Theory with Generative adversarial networks, and Web Engineering to optimize the computational advertising bidding process and achieve a higher rate of return. The system is validated with an actual case study with commercial providers such as Google AdWords and an advertiser's budget of several million dollars.
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Date Issued
2018
Identifier
CFE0007319, ucf:52118
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007319
The Challenges and Barriers to Employment for Female in Riyadh and Tabuk
Title
The Challenges and Barriers to Employment for Female in Riyadh and Tabuk.
Creator
Almutairi, Sultan, O'Neal, Thomas, Garibay, Ivan, Keathley, Heather, Jahani, Shiva, University of Central Florida
Abstract / Description
Women labor force participation plays an important role in economic. The developing in economy in Saudi Arabia depends on men rather than women, more than 50 years the Saudi women participation in the labor force extremely is low, this dissertation seeks to identify the challenges and barriers to employment for women in Riyadh and Tabuk. This study examines three research questions. The first question explored the difference between the rate of women unemployment in Tabuk and the rate of...
Show moreWomen labor force participation plays an important role in economic. The developing in economy in Saudi Arabia depends on men rather than women, more than 50 years the Saudi women participation in the labor force extremely is low, this dissertation seeks to identify the challenges and barriers to employment for women in Riyadh and Tabuk. This study examines three research questions. The first question explored the difference between the rate of women unemployment in Tabuk and the rate of women unemployment in Riyadh. The second question investigated ways in which a logistic regression using demographics data could be used to predict the women unemployment rates in two cities. The third question investigated the challenges faced by unemployed women in two cites. An online survey was administrated to both groups. The survey included demographic information and Women Labor Force Participation Instrument. A Chi-Square test was developed from the data to test the differences of the unemployed women in two cites. In order to analyze the second question, the researcher utilized two statistical analysis tests. A logistic regression equation was developed from the data to predict unemployment rates in two cites. Additionally, Partial least squares structural equation modeling were used to analyze the exploratory research question. Content analysis was also used to analyze the challenges faced by unemployed women.
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Date Issued
2019
Identifier
CFE0007597, ucf:52561
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007597