Business Intelligence Commons^™

Escaping The Promotion Trap: A Machine Learning Framework For Brand Equity Preservation In Beverage Cpg, Lucas P. Jones

Data Science Undergraduate Honors Theses

When companies acquire beverage brands, they typically value them based on total sales revenue. This traditional approach treats all sales equally over time, whether they are driven by genuine consumer demand or temporary discounts. This is important because while promotions can boost short-term sales, they tend to erode brand value over long periods of time. The measurement problem extends to acquisitions, where buyers lack the tools to distinguish real consumer demand from artificial promotional inflation.

This thesis develops a framework to separate genuine baseline demand from promotional dependence using Nielsen scanner data covering 189 beverage brands across 188,304 weekly observations …

Leveraging Accounting Analytics To Enhance Payroll Accuracy And Fraud Detection In U.S. Public Sector Institutions: A Case Study Approach, Regina Debrah

Beacom School of Business Student Publications

This study explores how accounting analytics can be leveraged to enhance payroll accuracy and improve fraud detection in U.S. public-sector institutions, addressing persistent irregularities amid rising demands for fiscal transparency. The research employs a qualitative design with secondary sources including academic literature, reports, and case studies. The literature identifies successful analytics implementation, such as Treasury OPI’s machine learning for data integration for unemployment claims. These precedents demonstrate direct transferability to payroll’s high volume and rules-based structure. Findings show that analytics significantly reduce improper payments through real-time screening, data integration, and risk prioritization when embedded in workflows. The findings also show …

Development Of A Personal Health Recommendation System Based On Data And Machine Learning To Improve User Wellbeing, Eliyah Acantha Manapa Sampetoding, Andi Alisha Faiqihah, Yulita Sirinti Pongtambing

Jurnal Administrasi Bisnis Terapan

The development of personal health recommendation systems has become a significant focus of research, along with the increasing need to improve user well-being. This research critically examines options in data and data mining and Machine Learning to support the establishment of systems to recommend better and improved healthcare services. Drawing on the results of a systematic review of past literature, the study shows that the application of Machine Learning algorithms in disease opinion learning, health condition classification, and demographics is emerging as a prospect for driving clinical decisions. Various models, from Deep Learning to traditional machine learning models such as …

Consumer Financial Data And Non-Horizontal Mergers, Linda Jeng, Jon Frost, Elisabeth Noble, Chris Brummer

Fordham Journal of Corporate & Financial Law

This Article explores the potential competitive implications of non-horizontal mergers where they involve extensive consumer data, including consumer financial data. As data become increasingly central to firm strategy, mergers between data-rich firms, while potentially leading to positive outcomes, can also create market power in ways not entirely accounted for by traditional antitrust theory. The Article considers some of these implications. It introduces new metrics for valuing data sets held by merging firms that could help competition authorities evaluate market impacts more effectively. The Article then suggests potential tools to mitigate anti-competitive effects of data-rich mergers. It advocates for further research …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Comparative Assessment Of Machine Learning And Deep Learning Models For Drug Effectiveness Using Sentiment Analysis, Blessing Ogechukwu Nwogu

Electronic Theses, Projects, and Dissertations

In recent years, the proliferation of online patient-generated drug reviews has created a valuable resource for assessing drug effectiveness and patient satisfaction, with sentiment analysis emerging as a powerful tool for extracting insights from this unstructured data.

This culminating research project conducted a comparative analysis of traditional Machine Learning (ML) and Deep Learning (DL) models for assessing drug effectiveness using sentiment analysis of participant reviews. The research aimed to evaluate the performance of Support Vector Machine (SVM), XGBoost, Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models in this context. This culminating research project addressed …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

Study Of Brain Tumor Prediction By Using Machine Learning, Vishaya Ummaneni

Electronic Theses, Projects, and Dissertations

Technological advancements in deep learning and machine learning have greatly improved the diagnosis and analysis of medical images. This culminating experience project utilized the EfficientNetV2B3 model to predict brain tumors. The research questions are: (Q1) Does the study's deep learning model perform better than current methods when it comes to predicting brain tumor? (Q2) How much does the model's performance change when using different optimizers such as Adagrad, Adam, and SGD? (Q3) Can the regularization method, such as dropout, enhance the neural network model's generalization? The findings are as follows: (Q1) Yes; the EfficientNetV2B3 model performs better than current methods. …

Airbnb Valuation: A Machine Learning Approach, Katherine Wyatt

Graduate Theses and Dissertations

This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Accounting And Financial Statements Auto Analysis System, Zhen Jia

Electronic Theses, Projects, and Dissertations

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning?

The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

A Study Of Heart Disease Diagnosis Using Machine Learning And Data Mining, Intisar Ahmed

Electronic Theses, Projects, and Dissertations

Heart disease is the leading cause of death for people around the world today. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Machine learning can help process medical big data and provide hidden knowledge which otherwise would not be possible with the naked eye. The aim of this project is to explore how machine learning algorithms can be used in predicting heart disease by building an optimized model. The research questions are; 1) What Machine learning algorithms are used in the diagnosis of heart …

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 Application Of The Right To Be Forgotten In The Machine Learning Context: From The Perspective Of European Laws, Zeyu Zhao

Catholic University Journal of Law and Technology

The right to be forgotten has been evolving for decades along with the progress of different statutes and cases and, finally, independently enacted by the General Data Protection Regulation, making it widely applied across Europe. However, the related provisions in the regulation fail to enable machine learning systems to realistically forget the personal information which is stored and processed therein.

This failure is not only because existing European rules do not stipulate standard codes of conduct and corresponding responsibilities for the parties involved, but they also cannot accommodate themselves to the new environment of machine learning, where specific information can …

Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef

Theses and Dissertations

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …

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 …

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 …

Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He

Journal of International Technology and Information Management

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …

Digital Forensic Tools & Cloud-Based Machine Learning For Analyzing Crime Data, Majeed Kayode Raji

College of Graduate Studies: Theses & Dissertations

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between …