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Engineering Management and Systems Engineering Faculty Research & Creative Works

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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke Dec 2019

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P ...


Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke Dec 2019

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication ...


Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo May 2019

Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans' actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators' actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on ...


System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur Nov 2018

System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur

Engineering Management and Systems Engineering Faculty Research & Creative Works

The system of systems architecting has many applications in transportation, healthcare, and defense systems design. This study first presents a short review of system of systems definitions. We then focus on capability-based system of systems architecting. In particular, capability-based system of systems architecting problems with various settings, including system flexibility, fund allocation, operational restrictions, and system structures, are presented as Multi-Objective Nonlinear Integer Programming problems. Relevant solution methods to analyze these problems are also discussed.


Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of ...


Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis ...


Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns Nov 2018

Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify ...


Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Nov 2018

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Engineering Management and Systems Engineering Faculty Research & Creative Works

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential ...


Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum Oct 2018

Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum

Engineering Management and Systems Engineering Faculty Research & Creative Works

Defense and Aerospace Systems Acquisition projects, just like any other Large-Scale Complex Engineered Systems (LSCES) experience delays and cost overrun during the acquisition process. Cost overrun and delays in LSCES are due, in part, to high complexity, size of the project, involvement of various stakeholders, organizations, political disruptions, changes in requirements and scope. These uncertainties, due to the exogenous factors, have cost the federal government billions of dollars and delays in completion of the programs. Cost estimation of federal programs is usually based on previous generations of systems produced and almost all the time the costs are underestimated. Underestimation of ...


A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney Jun 2018

A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

The number of students attending community colleges that take advantage of transfer pathways to universities continues to rise. Therefore, there is a need to engage in academic research on these students and their attrition in order to identify areas to improve retention. Community colleges have a very diverse population and provide entry into science, technology, engineering, and math (STEM) programs, regardless of student high school preparedness. It is essential for these students to successfully transfer to universities and finish their STEM degrees to meet the global workforce demands. This research develops a predictive model for community college students for degree ...


Analysis Of Autonomous Unmanned Aerial Systems Based On Operational Scenarios Using Value Modelling, Akash Vidyadharan, Robert Philpott Iii, Benjamin J. Kwasa, Christina L. Bloebaum Nov 2017

Analysis Of Autonomous Unmanned Aerial Systems Based On Operational Scenarios Using Value Modelling, Akash Vidyadharan, Robert Philpott Iii, Benjamin J. Kwasa, Christina L. Bloebaum

Engineering Management and Systems Engineering Faculty Research & Creative Works

In recent years, the use of UAS (Unmanned Aerial Systems) has moved beyond the realm of military operations and has made its way into the hands of consumers and commercial industries. Although the applications of UAS in commercial industries are virtually endless, there are many issues regarding their operations that need to be considered before these valuable pieces of equipment are allowed for widespread civil use. Currently, UAS operations in the public domain are guided and controlled by the FAA Part 107 rules after overwhelming public pressure caused by the earlier 333 exemption. In order to approach such larger issues ...


Reward/Penalty Design In Demand Response For Mitigating Overgeneration Considering The Benefits From Both Manufacturers And Utility Company, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli Nov 2017

Reward/Penalty Design In Demand Response For Mitigating Overgeneration Considering The Benefits From Both Manufacturers And Utility Company, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The high penetration of renewable sources in electricity grid has led to significant economic, environmental, and societal benefits. However, one major side effect, overgeneration, due to the uncontrollable property of renewable sources has also emerged, which becomes one of the major challenges that impedes the further large-scale adoption of renewable technology. Electricity demand response is an effective tool that can balance the supply and demand of the electricity throughout the grid. In this paper, we focus on the design of reward/penalty mechanism for the demand response programs aiming to mitigate the overgeneration. The benefits for both manufacturers and utility ...


Classification Of Rest And Active Periods In Actigraphy Data Using Pca, Isaac W. Muns, Yogesh Lad, Ivan G. Guardiola, Matthew S. Thimgan Nov 2017

Classification Of Rest And Active Periods In Actigraphy Data Using Pca, Isaac W. Muns, Yogesh Lad, Ivan G. Guardiola, Matthew S. Thimgan

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this paper we highlight a clustering algorithm for the purpose of identifying sleep and wake periods directly from actigraphy signals. The paper makes use of statistical Principal Component Analysis to identify periods of rest and activity. The aim of the proposed methodology is to develop a quick and efficient method to determine the sleep duration of an individual. In addition, a robust method that can identify sleep periods in the accelerometer data when duration, time of day varies by individual. A selected group of 10 individual's sensor data consisting of actigraphy from an accelerometer (3-axis), near body temperature ...


A General Algorithm For Assessing Product Architecture Performance Considering Architecture Extension In Cyber Manufacturing, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli Nov 2017

A General Algorithm For Assessing Product Architecture Performance Considering Architecture Extension In Cyber Manufacturing, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In modern manufacturing, the product architecture design options are usually restricted to those that can be produced with 100% confidence using those proven technologies to satisfy the existing customer requirement. As a result, the inefficiencies of architecture design are considerable due to such limitations. This issue is of particular interests in cyber manufacturing when exploring the tradeoff between generality and feasibility in product design and manufacturing. It can be expected that the improvement and extension of the existing product architecture may be required to meet new customer requirement when new technologies become available. An effective system performance assessment algorithm is ...


Energy Consumption Modeling Of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability, Yiran Yang, Lin Li, Yayue Pan, Zeyi Sun Nov 2017

Energy Consumption Modeling Of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability, Yiran Yang, Lin Li, Yayue Pan, Zeyi Sun

Engineering Management and Systems Engineering Faculty Research & Creative Works

Additive manufacturing (AM), also referred as three-dimensional printing or rapid prototyping, has been implemented in various areas as one of the most promising new manufacturing technologies in the past three decades. In addition to the growing public interest in developing AM into a potential mainstream manufacturing approach, increasing concerns on environmental sustainability, especially on energy consumption, have been presented. To date, research efforts have been dedicated to quantitatively measuring and analyzing the energy consumption of AM processes. Such efforts only covered partial types of AM processes and explored inadequate factors that might influence the energy consumption. In addition, energy consumption ...


Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam Nov 2017

Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam

Engineering Management and Systems Engineering Faculty Research & Creative Works

Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another ...


Rethinking The Design Of Low-Cost Point-Of-Care Diagnostic Devices, Faith W. Kimani, Samuel M. Mwangi, Benjamin J. Kwasa, Abdi M. Kusow, Benjamin K. Ngugi, Jiahao Chen, Xinyu Liu, Rebecca Cademartiri, Martin M. Thuo Oct 2017

Rethinking The Design Of Low-Cost Point-Of-Care Diagnostic Devices, Faith W. Kimani, Samuel M. Mwangi, Benjamin J. Kwasa, Abdi M. Kusow, Benjamin K. Ngugi, Jiahao Chen, Xinyu Liu, Rebecca Cademartiri, Martin M. Thuo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Reducing the global diseases burden requires effective diagnosis and treatment. In the developing world, accurate diagnosis can be the most expensive and time-consuming aspect of health care. Healthcare cost can, however, be reduced by use of affordable rapid diagnostic tests (RDTs). In the developed world, low-cost RDTs are being developed in many research laboratories; however, they are not being equally adopted in the developing countries. This disconnect points to a gap in the design philosophy, where parameterization of design variables ignores the most critical component of the system, the point-of-use stakeholders (e.g., doctors, nurses and patients). Herein, we demonstrated ...


Engineering Cyber Physical Systems: Preface, Cihan H. Dagli Oct 2017

Engineering Cyber Physical Systems: Preface, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multi-faceted systems of the future will entail complex logic with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized.

Complex Adaptive Systems have dynamically changing meta-architectures. Finding an optimal architecture for these systems is a multi-criteria decision making problem often involving many objectives in the order of 20 or more. This creates "Pareto Breakdown" which prevents ordinary multi-objective optimization approaches from effectively searching for an optimal solution; saturating the decision maker with large sets ...


Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos Oct 2017

Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper ...


Establishing Rules For Self-Organizing Systems-Of-Systems, David M. Curry, Cihan H. Dagli Oct 2017

Establishing Rules For Self-Organizing Systems-Of-Systems, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Self-organizing systems-of-systems offer the possibility of autonomously adapting to new circumstances and tasking. This could significantly benefit large endeavors such as smart cities and national defense by increasing the probability that new situations are expediently handled. Complex self-organizing behaviors can be produced by a large set of individual agents all following the same simple set of rules. While biological rule sets have application in achieving human goals, other rules sets may be necessary as these goals are not necessarily mirrored in nature. To this end, a set of system, rather than biologically, inspired rules is introduced and an agent-based model ...


Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke Oct 2017

Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose ...


Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias Sep 2017

Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this study, we investigate truck-to-door assignment problem for loading outgoing trucks in a cross-docking system with flexible handling times. Specifically, a truck's loading time depends on the number of workers assigned to the outbound door, where the truck is being loaded. An optimization problem is formulated to jointly determine the number of workers and the trucks to be loaded at each door. The resulting problem is a nonlinear integer programming model. Due to the complexity of this model, two evolutionary heuristic methods are proposed for solution. First heuristic method is based on truck assignments while the second heuristic ...


Using The Voice Of The Student To Evaluate Learning Management Systems, Elizabeth A. Cudney, Susan L. Murray, Brittany Groner, Katie M. Kaczmarek, Bonnie Wilt, Kamaria Blaney, Julie Phelps Jun 2017

Using The Voice Of The Student To Evaluate Learning Management Systems, Elizabeth A. Cudney, Susan L. Murray, Brittany Groner, Katie M. Kaczmarek, Bonnie Wilt, Kamaria Blaney, Julie Phelps

Engineering Management and Systems Engineering Faculty Research & Creative Works

A learning management system is an educational tool employed in higher education to organize, document, track, report, and deliver courses. Selecting the appropriate learning management system is a critical decision for a university. This study explores the usability of two leading systems, Blackboard and Canvas, from the students’ perspective. The goal is to gather and analyze user preferences in order to select an appropriate learning management system. Data was collected through surveys of student’s experience with the two learning management systems. The survey evaluated the ease of the following tasks: finding course documents, viewing grades, ease of navigation, intuitiveness ...


Work Zone Simulator Analysis: Driver Performance And Acceptance Of Missouri Alternate Lane Shift Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Thind, H. Nadathur Feb 2017

Work Zone Simulator Analysis: Driver Performance And Acceptance Of Missouri Alternate Lane Shift Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Thind, H. Nadathur

Engineering Management and Systems Engineering Faculty Research & Creative Works

The objective of this project is to evaluate MoDOT’s alternate lane shift sign configuration for work zones. The single signproposed by MoDOT provides the traveler with enough information to let them know that all lanes are available to shift around thework zone, whereas the MUTCD signs require drivers to see two signs. This research simulation project evaluates the drivers’ laneshifting performance and acceptance of the alternate lane shift sign proposed by MoDOT to be used on work zones as compared tothe MUTCD lane shift signs. Based on the study results, no difference was observed between MUTCD lane shift sign ...


Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns Nov 2016

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their ...


Application Of Neural Network In Shop Floor Quality Control In A Make To Order Business, Rajkamal Kesharwani, Cihan H. Dagli, Zeyi Sun Nov 2016

Application Of Neural Network In Shop Floor Quality Control In A Make To Order Business, Rajkamal Kesharwani, Cihan H. Dagli, Zeyi Sun

Engineering Management and Systems Engineering Faculty Research & Creative Works

A make to order business has to produce the products that are customized to the customer's current need. The customization can be realized by assembling different standard parts with various 'configurations'. The oil field service industry is a typical example where most products produced are cylindrical assemblies made up of standard parts customized in their size, material specifications, coating specifications, and threading suited for the particular load rating and environment. As business cycles go up and down, hiring and firing of personnel is the routine of the day. Thus, it is very hard to keep experienced inspectors due to ...


Combining Max-Min And Max-Max Approaches For Robust Sos Architecting, Hadi Farhangi, Dincer Konur, Cihan H. Dagli Nov 2016

Combining Max-Min And Max-Max Approaches For Robust Sos Architecting, Hadi Farhangi, Dincer Konur, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A System of Systems (SoS) architecting problem requires creating a selection of systems in order to provide a set of capabilities. SoS architecting finds many applications in military/defense projects. In this paper, we study a multi-objective SoS architecting problem, where the cost of the architecture is minimized while its performance is maximized. The cost of the architecture is the summation of the costs of the systems to be included in the SoS. Similarly, the performance of the architecture is defined as the sum of the performance of the capabilities, where the performance of a capability is the sum of ...


Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke Nov 2016

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 ...


Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli Nov 2016

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 ...


Engineering Cyber Physical Systems: Applying Theory To Practice Preface, Cihan H. Dagli Nov 2016

Engineering Cyber Physical Systems: Applying Theory To Practice Preface, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.