Business Analytics Commons^™

A Descriptive Study Of Variable Discretization And Cost-Sensitive Logistic Regression On Imbalanced Credit Data, Lili Zhang, Jennifer Priestley, Herman Ray, Soon Tan

Published and Grey Literature from PhD Candidates

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit scoring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with …

Black Swamp Pub And Bistro Analysis, Sara Aniol

Honors Projects

The Black Swamp Pub and Bistro is a full-service restaurant located in the Union on the Bowling Green State University Campus. We mainly do sit-down service, but we also do take-out orders and have a full bar with draft beers as well as mixed drinks. Our menu tends to change a lot, with new additions as well as some of the items being deleted. My goal of this project is to try to give some insight on the patterns that are too big to see with day-to-day operations as well as give some recommendations for the future that is backed …

Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith

SMU Data Science Review

In this paper, we compare demand forecasting methods used by the supply chain department at Bilports to open-source forecasting methods. The design and implementation of the open-source forecasting system also attempts to use several external datasets such as consumer sentiment, housing permit starts, and weather to improve prediction quality. Additionally, the performance of the forecast is evaluated by the reduction of shipment lead times from China, the company’s primary vendor. The objective of our paper is to improve Bilports’s forecasting capabilities. The primary motivation of this paper is to increase forecasting accuracy and identify the weaknesses of the methods used …

Leveraging Reviews To Improve User Experience, Anthony Schams, Iram Bakhtiar, Cristina Stanley

SMU Data Science Review

In this paper, we will explore and present a method of finding characteristics of a restaurant using its reviews through machine learning algorithms. We begin by building models to predict the ratings of individual reviews using text and categorical features. This is to examine the efficacy of the algorithms to the task. Both XGBoost and logistic regression will be examined. With these models, our goal is then to identify key phrases in reviews that are correlated with positive and negative experience. Our analysis makes use of review data publicly made available by Yelp. Key bigrams extracted were non-specific to the …

Distilling Managerial Insights And Lessons From Ai Projects At Singapore's Changi Airport (Part 2), Steve Lee, Steven M. Miller

Asian Management Insights

Since 2017, Changi Airport group (CAG) has initiated a host of pilot projects that use connective and intelligent technologies to enable its move towards digital transformation and SMART Airport Vision. This has resulted in a first wave of deployment of AI and Machine Learning-enabled applications across various functions that can better sense, analyse, predict, and interact with people.

Ai Gets Real At Singapore's Changi Airport (Part 1), Steve Lee, Steven M. Miller

Asian Management Insights

Ranked as the best airport for seven consecutive years, Singapore’s Changi Airport is lauded the world over for the efficient, safe, pleasurable and seamless service it offers the millions of passengers that pass through its facilities annually. Much of Changi Airport’s success can be attributed to the organisation’s customer-oriented business focus and deeply embedded culture of service excellence, combined with a host of advanced technologies operating invisibly in the background. The framework for this technology enablement is Changi Airport Group’s (CAG’s) SMART Airport Vision—an enterprise-wide approach to connective technologies that leverages sensors, data fusion, data analytics, and artificial intelligence (AI), …

Cyanotech: A Strategic Audit, Trent Hoppe

Honors Program: Senior Projects (Public)

Microalgae is a fascinating group of organisms that possess a diverse array of interesting traits and benefits relevant to food, medicine, and biofuel. Extensive research behind the viability of microalgae to disrupt the market has sparked an emergent microalgae industry. Founded in 1983, one of the top microalgae companies in the world today is Cyanotech. With a 90-acre algae farm in Kailua-Kona, Hawaii and two flagship microalgae products that are world leaders in their categories, Cyanotech is well- positioned be setting the course for the industry and revolutionizing the use microalgae commercially. Despite these favorable attributes, Cyanotech has been trapped …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Ample Provision: A Preliminary Study Relating Budget Composition And High School Graduation Rates In Select Washington State Public School Districts, Gregory P. Gadow

All Undergraduate Projects

How to allocate scarce resources for an optimal outcome is of keen interest to those who set the budgets in public education. Simply throwing money at schools is not enough; it is important that money is spent where it will do the most good. This study considers Washington State public school districts and examines how the share of per-student expenditures in seven budget categories relates to on-time high school graduation rates. It is an investigative study, exploring whether there is enough evidence to merit further, more in-depth research. Using budget and graduation information from academic years 1997-98 through 2016-17 for …

Open Source Foundations For Spatial Decision Support Systems, Jochen Albrecht

Publications and Research

Spatial Decision Support Systems (SDSS) were a hot topic in the 1990s, when researchers tried to imbue GIS with additional decision support features. Successful practical developments such as HAZUS or CommunityViz have since been built, based on commercial desktop software and without much heed for theory other than what underlies their process models. Others, like UrbanSim, have been completely overhauled twice but without much external scrutiny. Both the practical and the theoretical foundations of decision support systems have developed considerably over the past 20 years. This article presents an overview of these developments and then looks at what corresponding tools …

Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels

SMU Data Science Review

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …

Is Information Systems Misuse Always Bad? A New Perspective On Is Misuse In Hospitals Under The Context Of Disasters, Dheyaaldin Alsalman

Masters Theses & Doctoral Dissertations

Although the extant literature has investigated how individuals engage in inappropriate behaviors based on the rational choice theory (RCT) (e.g., computer misconduct), the neutralization theory (e.g., IS security policies violation), and workarounds under normal situations, it has given little consideration to how individuals are involved in misuse of information systems with a good intention under the context of disasters. To fill this research gap, we propose a selfless misuse model, which offers a theoretical explanation for the concept of individuals’ selfless misuse intention under uncertainty caused by disasters. In this study, we show why employees make decisions to misuse the …

Radical Social Ecology As Deep Pragmatism: A Call To The Abolition Of Systemic Dissonance And The Minimization Of Entropic Chaos, Arielle Brender

Student Theses 2015-Present

This paper aims to shed light on the dissonance caused by the superimposition of Dominant Human Systems on Natural Systems. I highlight the synthetic nature of Dominant Human Systems as egoic and linguistic phenomenon manufactured by a mere portion of the human population, which renders them inherently oppressive unto peoples and landscapes whose wisdom were barred from the design process. In pursuing a radical pragmatic approach to mending the simultaneous oppression and destruction of the human being and the earth, I highlight the necessity of minimizing entropic chaos caused by excess energy expenditure, an essential feature of systems that aim …

A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.

Published and Grey Literature from PhD Candidates

Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.

3rd Analytics Without Borders Conference Program, Bentley University

Analytics Without Borders

No abstract provided.

Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni

Published and Grey Literature from PhD Candidates

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared …

Food Choice Priorities Change Over Time And Predict Dietary Intake At The End Of The First Year Of College Among Students In The U.S., Melissa J. Vilaro, Sarah E. Colby, Kristin Riggsbee, Wenjun Zhou, Carol Byrd-Bredbenner, Melissa D. Olfert, Tracey E. Barnett, Tanya Horacek, Morgan Sowers, Anne E. Matthews

Faculty & Staff Scholarship

This study assessed food choice priorities (FCP) and associations with consumption of fruits and vegetables (FV), fiber, added sugars from non-beverage sources, and sugar-sweetened beverages (SSB) among college students. Freshmen from eight U.S. universities (N = 1149) completed the Food Choice Priorities Survey, designed for college students to provide a way to determine the factors of greatest importance regarding food choices, and the NCI Dietary Screener Questionnaire. Changes in FCP and dietary intake from fall 2015 to spring 2016 were assessed. Multiple regression models examined associations between FCP and log-transformed dietary intake, controlling for sex, age, race, and BMI. Participant …

Neck Circumference Positively Relates To Cardiovascular Risk Factors In College Students, Oluremi A. Famodu, Makenzie L. Barr, Sarah E. Colby, Wenjun Zhou, Ida Holaskova, Miriam P. Leary, Carol Bryd-Bredbenner, Anne E. Matthews, Melissa D. Olfert

Faculty & Staff Scholarship

The objective of this study was to determine the relationship between neck circumference (NC) and other anthropometric measures and examine cut-off points for males and females according to existing waist circumference cut-off levels in this age group. Across 8 universities, 1562 students underwent a physical assessment. Spearman rho correlations (ρ) were calculated to determine associations between NC and other continuous variables of health. Receiving operating characteristic curves were constructed to assess the optimal cut-off levels of NC of males and females with central obesity. Participants were predominantly Caucasian (67%), female (70%), and outside of Appalachia 2 (82%). Forty-one percent of …

Self-Reported Vs. Measured Height, Weight, And Bmi In Young Adults, Melissa D. Olfert, Makenzie L. Barr, Camille M. Charlier, Oluremi A. Famodu, Wenjun Zhou, Anne E. Mathews, Carol Byrd-Bredbenner, Sarah E. Colby

Faculty & Staff Scholarship

Self-reported height and weight, if accurate, provide a simple and economical method to track changes in body weight over time. Literature suggests adults tend to under-report their own weight and that the gap between self-reported weight and actual weight increases with obesity. This study investigates the extent of discrepancy in self-reported height, weight, and subsequent Body Mass Index (BMI) versus actual measurements in young adults. Physically measured and self-reported height and weight were taken from 1562 students. Male students marginally overestimated height, while females were closer to target. Males, on average, closely self-reported weight. Self-reported anthropometrics remained statistically correlated to …

Data Warehousing Class Project Report, Gaya Haciane, Chuan Chieh Lu, Rassaniya Lerdphayakkarat, Rudraxi Mitra

Engineering and Technology Management Student Projects

Data mining is widely described or defined as the discipline of: “making sense of the data”. In today’s day and age, the rise of ubiquity of information calls for more advanced and developed techniques to mine the data and come up with insights. Data mining finds applications in many different fields and industries: Whether it is in Embryology, Crops, Elections, or Business Marketing...etc. It is not a wild assumption to consider that every organization in the world has some data mining capabilities or its main activity necessitates it and they have some third party organization doing that for them. One …

Rough Neutrosophic Digraphs With Application, Florentin Smarandache, Sidra Sayed, Nabeela Ishfaq, Muhammad Akram

Branch Mathematics and Statistics Faculty and Staff Publications

A rough neutrosophic set model is a hybrid model which deals with vagueness by using the lower and upper approximation spaces. In this research paper, we apply the concept of rough neutrosophic sets to graphs. We introduce rough neutrosophic digraphs and describe methods of their construction. Moreover, we present the concept of self complementary rough neutrosophic digraphs. Finally, we consider an application of rough neutrosophic digraphs in decision-making.

Zero Textbook Cost Syllabus For Cis 3367 (Spreadsheet Applications In Business), Soniya Dsouza

Open Educational Resources

The primary focus of this course is to learn how to construct and use powerful spreadsheets for effective managerial decision-making. This course is mostly project- oriented with a dual focus on spreadsheet engineering and quantitative modeling of financial applications. Students will learn to develop powerful spreadsheet models and perform data analysis using Pivot Tables, VLookUp, Data Validation techniques and Sub Total functions. Students will also learn how to enhance spreadsheets by creating dashboards on financial data. The Visual Basic (macro) concepts will also be introduced to students. With the knowledge and hands-on experience of these concepts, students will be prepared …

Mcdm Method For N-Wise Criteria Comparisons And Inconsistent Problems, Florentin Smarandache, Azeddine Elhassouny

Branch Mathematics and Statistics Faculty and Staff Publications

The purpose of this paper is to present an e[xtension and alternative of the hybrid method based on Saaty’s Analytical Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution method (AHP-TOPSIS), that based on the AHP and its use of pairwise comparisons, to a new method called α -D MCDM-TOPSIS( α -Discounting Method for multicriteria decision making-TOPSIS). The new method overcomes limits of AHP which work only for pairwise comparisons of criteria to any-wise (n-wise) comparisons, with crisp coefficients or with interval-valued coefficients. α-D MCDM-TOPSIS is verified by some examples to demonstrate how it allows for consistency, …

Walking Firms Through It Decisions, Singapore Management University

Research@SMU: Connecting the Dots

Is it better to be an early adopter or wait till the technology matures? Professor Ma Dan helps companies make data-driven decisions.

See the papers:

• The pricing strategy analysis for the Software-as-a-Service business model
• Cloud services from a consumer perspective.

1st Analytics Without Borders Conference Program, Bentley University

Analytics Without Borders

No abstract provided.

Arctic Northern Sea Route Prospects, Obstacles & Chinese Stance, Amit Sanwal

World Maritime University Dissertations

No abstract provided.

A Proposed Model For The Approach To Augmented Reality Deployment In Marketing Communications, Stephen O'Mahony

Dept. of Marketing & International Business Conference Material

In the contemporary landscape, human interaction is characterized by the pervasive mediation of intelligent agents. Owing to advances in computer science and engineering, phenomena once limited to fixed locations are metamorphosing from extraneous entities to implicit components of the everyday. Computing power has simultaneously enhanced and miniaturized to the extent that contemporary consumer devices demonstrate power equivalent to or greater than that of personal computers of recent memory. At the same time, network connectivity has proliferated to ubiquitous levels, reflected in the fact that the amount of inanimate objects connected to the Internet has recently surpassed the human population of …

Multi-Criteria Decision Making Based On Dsmt-Ahp, Jean Dezert, Jean Marc Tacnet, Mireille Batton-Hubert, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this paper, we present an extension of the multicriteria decision making based on the Analytic Hierarchy Process (AHP) which incorporates uncertain knowledge matrices for generating basic belief assignments (bba’s). The combination of priority vectors corresponding to bba’s related to each (sub)- criterion is performed using the Proportional Conflict Redistribution rule no. 5 proposed in Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. The method presented here, called DSmT-AHP, is illustrated on very simple examples.

Neutrosophic Parametrized Soft Set Theory And Its Decision Making, Said Broumi, Irfan Deli, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this work, we present definition of neutrosophic parameterized (NP) soft set and its operations. Then we define NP-aggregation operator to form NP-soft decision making method which allows constructing more efficient decision processes. We also dive an example which shows that they can be successfully applied to problem that contain indeterminacy.

Essays On Rethinking African Development: Contextual And Methodological Advances, Olumayokun Soremekun

2013

This research study sets out to provide analytical answers to the questions of African development and inequality. We examine various aspects of development from the issue of inequality among African countries to unravelling the synergies among the MDG goals and finally to investigating the progress if any that African countries have made towards attaining the MDG goals.

This research is broken down into three main studies: measuring inequality of opportunity, examining the synergies between the Millennium development goals at a particular point in time and lastly assessing the progress that has been made towards attaining the MDG goals in Africa. …