Operations Research, Systems Engineering and Industrial Engineering Commons^™

Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent's education status, and time since high school graduation. These inputs and the multi-layer neural network model are used …

Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. Therefore, the main purpose of this …

Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing …

An Examination Of Economic Metrics As Indicators Of Air Force Retention, Helen L. Jantscher

Theses and Dissertations

Fluctuations in the economy can cause military recruitment and retention plans to go awry. By focusing on various economic metrics, it is possible to anticipate changes in retention rates for specific Air Force Specialty Codes (AFSCs). To address the challenge of maintaining a robust and mission capable Air Force, a correlation analysis is employed to determine the relationship between certain economic indicators and AFSC retention rates. As one might suspect, retention rates follow the trend of decreasing when the economy is strong. Of interest, we found two AFSCs which go against this trend. Namely, the retention rates for officers in …

Optimizing Forecasting Methods For Ustranscom Railcar Demands, James M. Park

Theses and Dissertations

The United States military heavily relies on rail freight operations to meet many of its logistical needs during both peacetime and wartime efforts. As the head organization responsible for managing and overseeing all modes of military transportation, United States Transportation Command depends on timely accurate railcar demand forecasts to drive critical decisions on distribution and placement of railcar assets. However, the intermittent nature of railcar demands based on location and commodity make it a challenging task for forecasters. Furthermore, these “lumpy” demands often come without any obvious trends or seasonality. This study explores the utility of both traditional forecasting methods …

A Model Of Ambient Noise Caused By Wind Flow, Jovan Popovich

Theses and Dissertations

The generation of noise caused by wind owing past a human ear is an important yet vastly understudied factor in determining the ambient noise of an environment experienced by a human observer. Sound level measurements were obtained from wind tunnel tests simulating a human experiencing wind ows at various speeds and from various directions. This data set was used in this thesis. This thesis presents a collection of models for predicting wind noise levels across a broad spectrum of frequencies based on wind speed and angle inputs. Graphical approaches are included to characterize the observed data and illustrate the models' …

Aircraft Demand Forecasting, Kayla M. Monahan

Masters Theses

This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time …

Oled Tv Technology Forecasting Using Technology Mining And The Fisher-Pry Diffusion Model, Yonghee Cho, Tugrul U. Daim

Joseph Cho