1. AN API FOR AUTHOR NAME DISAMBIGUATION Open Access Author: Dudhbhate, Gauravi Uday Title: AN API FOR AUTHOR NAME DISAMBIGUATION Graduate Program: Computer Science and Engineering Keywords: APIDisambiguationAuthor Name DisambiguationMachine LearningWeb service information extractionscholarly big dataWeb serviceinformation extractionWeb ServiceInformation ExtractionRandom ForestClustering File: Download Dudhbhate-Dissertation.pdf Committee Members: Dr. Lee Giles, Thesis Advisor/Co-Advisor
2. HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK Open Access Author: Xia, Zhiyue Title: HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK Graduate Program: Forest Resources Keywords: Gypsy mothRandom ForestRandom ForestsForest disturbanceSpatial modelingEcological modeling File: Download Thesis_Xia_FinalVersion.pdf Committee Members: Douglas A Miller, Thesis Advisor/Co-AdvisorLaura P Leites, Thesis Advisor/Co-AdvisorShelby Fleischer, Committee Member
3. using real-time speed data to quantify impacts of weather on travel speeds Open Access Author: Yu, Yinghai Title: using real-time speed data to quantify impacts of weather on travel speeds Graduate Program: Civil Engineering Keywords: WeatherTravel SpeedLinear RegressionRandom ForestPartial Dependence Plot File: Download Thesis_Yinghai_Yu_07182019.pdf Committee Members: Vikash Varun Gayah, Thesis Advisor/Co-AdvisorMartin T Pietrucha, Committee MemberSukran Ilgin Guler, Committee Member
4. Empirical Evaluation of the Efficient Market Hypothesis: A Machine Learning Approach Open Access Author: Wright, Isaac Title: Empirical Evaluation of the Efficient Market Hypothesis: A Machine Learning Approach Graduate Program: Statistics Keywords: Machine LearningFinancial MarketsEfficient Market HypothesisRandom ForestBoostingGLMstocks File: Download IsaacWright_Thesis.pdf Committee Members: Matthew Logan Reimherr, Thesis Advisor/Co-AdvisorJia Li, Committee MemberJohn C Liechty, Committee MemberEphraim Mont Hanks, Program Head/Chair
5. Insights on the use of Machine Learning to Predict Retention of Career Soldiers in the United States Army Open Access Author: Garcia, Miguel Title: Insights on the use of Machine Learning to Predict Retention of Career Soldiers in the United States Army Graduate Program: Data Analytics Keywords: Artificial IntelligenceMachine LearningAIDeep LearningDecision TreeLogistic RegressionRandom ForestXGBoostArtificial Neural NetworkDeep Neural NetworkU.S. ArmyMilitaryAttritionRetentionPerson-Event Data EnvironmentPDEDepartment of DefenseDoDArmy Analytics GroupAAGNaive BayesActive DutyEnlistmentCommissioned OfficersSoldiersArea Under the CurveAUCData AnalyticsRetirementMEDPROSPHAMEPCOMDTMSATMSROC CurveVariable ImportancePreprocessingData Quality File: Download Insights_on_the_use_of_AI_to_Predict_Retention_of_Career_Soldiers_in_the_US_Army.pdf Committee Members: Colin Neill, Program Head/ChairPartha Mukherjee, Thesis Advisor/Co-AdvisorGuanghua Qiu, Committee MemberYouakim Badr, Thesis Advisor/Co-Advisor