Business Intelligence Commons^™

U.S. Energy Information Administration Information Resources, Bert Chapman

Libraries Faculty and Staff Presentations

Provides information about the resources produced by U.S. Department of Energy's Energy Information Administration. These resources cover energy statistics for U.S., states, the United States, and foreign countries. They also cover energy products as varied as coal, natural gas, nuclear energy, petroleum, and renewable energy.

Application Of Big Data Technology, Text Classification, And Azure Machine Learning For Financial Risk Management Using Data Science Methodology, Oluwaseyi A. Ijogun

College of Graduate Studies: Theses & Dissertations

Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated …

Optimal Design And Operation Of Integrated Hydrogen Generation And Utilization Plants, Ijiwole Solomon Ijiyinka

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are considerable efforts worldwide for reducing the use of fossil fuel for energy production. While renewable energy sources are being increasingly used, fossil fuel still contribute about 80% of the energy used worldwide. As a result, the level of CO₂ is still increasing fast in the atmosphere currently exceeding about 410 parts per million (ppm). For reducing CO₂ build up in the atmosphere, various approaches are being investigated. For the electric power generation sector, two key approaches are post-combustion CO₂ capture and use of hydrogen as a fuel for power generation. These two solutions can also …

The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska

Karbala International Journal of Modern Science

Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.

A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen

SMU Data Science Review

The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …

The Significance Of Sonic Branding To Strategically Stimulate Consumer Behavior: Content Analysis Of Four Interviews From Jeanna Isham’S “Sound In Marketing” Podcast, Ina Beilina

Student Theses and Dissertations

Purpose:
Sonic branding is not just about composing jingles like McDonald’s “I’m Lovin’ It.” Sonic branding is an industry that strategically designs a cohesive auditory component of a brand’s corporate identity. This paper examines the psychological impact of music and sound on consumer behavior reviewing studies from the past 40 years and investigates the significance of stimulating auditory perception by infusing sound in consumer experience in the modern 2020s.

Design/methodology/approach:
Qualitative content analysis of audio media was used to test two hypotheses. Four archival oral interview recordings from Jeanna Isham’s podcast “Sound in Marketing” featuring the sonic branding experts …

Prospects For Legal Analytics: Some Approaches To Extracting More Meaning From Legal Texts, Kevin D. Ashley

University of Cincinnati Law Review

No abstract provided.

Active Learning With Cybersecurity, Carole Shook

TFSC Publications and Presentations

A global campus grant was obtained in Spring 2020 to develop modules for Cybersecurity. This presentation encompasses the use of Cyberciege and case studies that require active learning of students.

How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar

Information Systems Undergraduate Honors Theses

Since the founding of computers, data scientists have been able to engineer devices that increase individuals’ opportunities to communicate with each other. In the 1990s, the internet took over with many people not understanding its utility. Flash forward 30 years, and we cannot live without our connection to the internet. The internet of information is what we called early adopters with individuals posting blogs for others to read, this was known as Web 1.0. As we progress, platforms became social allowing individuals in different areas to communicate and engage with each other, this was known as Web 2.0. As Dr. …

Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore

SDSU Data Science Symposium

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …

Application Of Competitive Intelligence For Insular Territories: Automatic Analysis Of Scientific And Technology Trends To Fight The Negative Effects Of Climate Change, Henri Dou, Pierre Fournie

International Journal of Islands Research

Islands are fragile territories because of their geographical position. As a result, climate impacts can have serious consequences, of which some are irreversible. Therefore, it is necessary to allow insular territories to benefit from the latest scientific and technological advances in combating climate effects. The current article shows how to deal with automatic analysis of scientific information on the one hand, but also its applications via patents. We will analyse the latest scientific results as well as their possible applications using patent analysis. We will also focus on experts, laboratories, and leading companies, that are active on the field. The …

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Prediction Of Iraqi Stock Exchange Using Optimized Based-Neural Network, Ameer Al-Haq Al-Shamery, Prof. Dr. Eman Salih Al-Shamery

Karbala International Journal of Modern Science

Stock market prediction is an interesting financial topic that has attracted the attention of researchers for the last years. This paper aims at improving the prediction of the Iraq-Stock-Exchange (ISX) using a developed method of feedforward Neural-Networks based on the Quasi-Newton optimization approach. The proposed method reduces the error factor depending on the Jacobian vector and Lagrange multiplier. This improvement has led to accelerating convergence during the learning process. A sample of companies listed on ISX was selected. This includes twenty-six banks for the years from 2010 to 2020. To evaluate the proposed model, the research findings are compared with …

Machine Learning Models Predicting The Prices Of Houses In Boston, Sheikh-Sedat Touray

Senior Honors Projects

No abstract provided.

Integration Of Blockchain Technology Into Automobiles To Prevent And Study The Causes Of Accidents, John Kim

Electronic Theses, Projects, and Dissertations

Automobile collisions occur daily. We now live in an information-driven world, one where technology is quickly evolving. Blockchain technology can change the automotive industry, the safety of the motoring public and its surrounding environment by incorporating this vast array of information. It can place safety and efficiency at the forefront to pedestrians, public establishments, and provide public agencies with pertinent information securely and efficiently. Other industries where Blockchain technology has been effective in are as follows: supply chain management, logistics, and banking. This paper reviews some statistical information regarding automobile collisions, Blockchain technology, Smart Contracts, Smart Cities; assesses the feasibility …

Integration Of Internet Of Things And Health Recommender Systems, Moonkyung Yang

Electronic Theses, Projects, and Dissertations

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch …

The Maritime Domain Awareness Center– A Human-Centered Design Approach, Gary Gomez

Political Science & Geography Faculty Publications

This paper contends that Maritime Domain Awareness Center (MDAC) design should be a holistic approach integrating established knowledge about human factors, decision making, cognitive tasks, complexity science, and human information interaction. The design effort should not be primarily a technology effort that focuses on computer screens, information feeds, display technologies, or user interfaces. The existence of a room with access to vast amounts of information and wall-to-wall video screens of ships, aircraft, weather data, and other regional information does not necessarily correlate to possessing situation awareness. Fundamental principles of human-centered information design should guide MDAC design and technology selection, and …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman

SMU Data Science Review

Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …

The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability, Almuth D. Merkel

Doctor of International Conflict Management Dissertations

The social market economy was developed in Germany during the interwar period amidst political and economic turmoil. With clear demarcation lines differentiating it from socialism and laissez-faire capitalism, the social market economy became a formula for peace and prosperity for post WWII Germany. Since then, the success of the social market economy has inspired many other countries to adopt its principles. Drawing on evidence from economic history and the history of economic thought, this thesis first reviews the evolution of the fundamental principles that form the foundation of social-market economic thought. Blending the micro-economic utility maximization framework with traditional growth …

Analysis On Suicidal Ideation Among Adolescents (12-17 Years) In The Usa, Himani Raturi

Electronic Theses, Projects, and Dissertations

Suicide is one of the leading health concerns in United States among adolescents and the presence of suicidal ideation (SI) is quite high, with ~20-30% of adolescents reporting it at some point. Though we have seen growth and development in the prevention of suicide, there is limited research on the ability to identify the adolescents which might be at risk for SI. The objective behind the project is to identify adolescents with SI using machine learning.

The project shows statistics from different articles on adolescents in the U.S. For this study, adolescent data was taken from NSDUH 2018. Moreover, detailed …

A Credit Analysis Of The Unbanked And Underbanked: An Argument For Alternative Data, Edwin Baidoo

Doctor of Data Science and Analytics Dissertations

The purpose of this study is to ascertain the statistical and economic significance of non-traditional credit data for individuals who do not have sufficient economic data, collectively known as the unbanked and underbanked. The consequences of not having sufficient economic information often determines whether unbanked and underbanked individuals will receive higher price of credit or be denied entirely. In terms of regulation, there is a strong interest in credit models that will inform policies on how to gradually move sections of the unbanked and underbanked population into the general financial network.

In Chapter 2 of the dissertation, I establish the …

Big-Data Talent Analytics In The Public Sector: A Promotion And Firing Model Of Employees At Federal Agencies, Rabih Neouchi

Operations Research and Engineering Management Theses and Dissertations

Talent analytics is a relatively new area of focus to researchers working in analytics and data science. Talent Analytics has the potential to help companies make many informed critical decisions around talent acquisition, promotion and retention. This work investigates data science to predict “shiny star” employees in the U.S. public sector, defined as top-notch performers over the years of a given time span. Its scope falls within talent analytics, also called people analytics, a relatively new research area.

We clean a data set made available by the U.S. Office of Personnel Management (OPM) and present two models to predict the …

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), …

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 …

Improving Shewhart Control Chart Performance In The Presence Of Measurement Error Using Multiple Measurements And Two-Stage Sampling, Kenneth W. Linna

Journal of International & Interdisciplinary Business Research (2014-2019)

The usual Shewhart control chart efficiently detects large shifts in the mean of a quality characteristic and has been extensively studied in the literature. Most proposed alternatives to the Shewhart chart aim to improve either the signal performance for smaller mean shifts or reduce the sampling effort required to detect a larger shift. Measurement error has been shown in the literature to result in reduced power to detect process shifts. The combination of multiple measurements and two-stage sampling is considered here as a strategy for both regaining power lost due to measurement error and specifically tuning the charts for shifts …

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …

Perfect Ratings With Negative Comments: Learning From Contradictory Patient Survey Responses, Andrew S. Gallan, Marina Girju, Roxana Girju

Patient Experience Journal

This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we start by building …