Business Intelligence Commons^™

The Impact Of The Covid-19 Pandemic On National Hockey League Fan Attendance, Ramon E. Rivera

Electronic Theses, Projects, and Dissertations

The COVID-19 pandemic struck swiftly around the world, causing several societal functions and normalcies to adapt or shut down completely. This culminating project explores the effects of COVID-19 on arena fan attendance of the National Hockey League (NHL). This project was guided by the following three research questions: Q1 – How did the NHL arena closures affect fan attendance during the COVID-19 pandemic? Q2 – What factors influenced NHL fan arena attendance before the start of the COVID-19 pandemic? Q3 – What factors impacted the NHL arena fan attendance after the conclusion of the COVID-19 pandemic? An empirical model developed …

Challenges The Sporting Industry Faced During Covid-19 On Fan Attendance: The Case Of The National Basketball Association, Marlon Long

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the challenges the sporting industry faced during the COVID-19 pandemic, with a focused look at the National Basketball Association. The research questions are: (Q1) How did the NBA arena shutdowns impact fan attendance for each team during the COVID-19 pandemic? (Q2) What factors influenced NBA arena attendance before the COVID-19 shutdowns? (Q3) What factors influenced NBA arena attendance after the COVID-19 shutdown? The data collected includes all 30 NBA teams from 2019 through the 2024 seasons. The research questions were analyzed using multilinear regression analysis and comparison of attendance data over 6 seasons including the …

An Analysis Of Security Risks Posed By Text-Based Generative Ai And Corporate Security Weaknesses Leading To Data Leaks, Tashya Rakshana Byreddy

Electronic Theses, Projects, and Dissertations

ABSTRACT

Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing …

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 …

Using Ai Tools To Unmask Sarcasm, Sidra Tehniyath Lnu

Electronic Theses, Projects, and Dissertations

Sarcasm can be identified in newspaper headlines in digital communication, as it is contextual and has low inter- and intra-observer reliability. This research aims to improve sarcastic comment identification using a natural language processing approach, especially the BI-LSTM. The primary concern is to use fine-tuning methods to enhance the ways that strengthen the sarcasm identification rate, which regards the factors that complicate an automatic identification process. The study explores the impact of techniques such as early stopping, optimal loss function selection, and hyperparameter tuning to enhance the model's performance. The research questions are: Q1) How can fine-tuning techniques for BI-LSTM …

Corn Leaf Disease Prediction Using Deep Learning, Meghana Varayuri

Electronic Theses, Projects, and Dissertations

Corn is a widely cultivated agricultural product, serving as a cornerstone in food production and industrial applications such as biofuels, playing a crucial role in the global economy. This study explores the application of deep transfer learning to accurately classify major corn diseases from leaf images, aiming to enhance disease management strategies for improved agricultural productivity and sustainability. The customized Dense net 201 model achieved 95% prediction accuracy on an untrained dataset. Data augmentation improved the model’s accuracy from 91% to 95%. This supervised learning approach enhances the model’s performance by increasing the diversity, leading to better generalization and accuracy. …

Understanding The Dynamics Of Chatbot Design, Transparency, And Ethical Implications In Customer–Brand Relationship In Hospitality And Tourism Industry, Shakera Parveen

Electronic Theses, Projects, and Dissertations

This culminating experience project investigated the impact of chatbot interactions on customer perceptions, ethical considerations, and transparency within the hospitality and tourism industry. The research questions are: Q1: how do the specific visual and verbal aspects contribute to shaping customer perceptions within the context of chatbot marketing efforts? Q2: what steps can companies take to address and mitigate ethical concerns surrounding interactions with chatbots? Q3: To what extent does the level of transparency and explainability in chatbot interactions influence customer trust and satisfaction? The findings are: Q1: the study revealed that visual and verbal aspects of chatbot design significantly shape …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Association Between Food Insecurity And Chronic Health Condition Among Adults (18 To 48), Prasanth Reddy Guda

Electronic Theses, Projects, and Dissertations

Uncertainty or limited access to safe food, known as food insecurity, can affect the health and healthcare needs of individuals with several chronic conditions. During 2022, 12.8 percentage (around 17.0 million) of households in the United States experienced food insecurity. The main objective of this study was to examine the Association between food insecurity and chronic health conditions adults. We collected data from NHIS 2022 sample Adults’ Interview (The National Health Interview Survey). The research question asked are: (Q1) Are there certain long term health issues that are more common among adults facing food insecurity age in between the ages …

Impact Of Seasonality On Demand Forecasting Techniques For Small-Scale Food Retailers, Indu Sree Guturu

Electronic Theses, Projects, and Dissertations

This study explores the impact of seasonality on SKU (Stock Keeping Unit) demand forecasting in small-scale food retailers through the analysis of historical sales data, focusing on four key products, labelled: Product A, Product B, Product C & Product D. The research aims to assess the accuracy of different forecasting models in capturing seasonal fluctuations. The research questions are: (Q1) What is the impact of seasonality on SKU demand forecasting in case of small-scale food retailers. (Q2) How does the efficacy of Traditional model compare to alternative technologically driven forecasting techniques? (Q3) To what extent can the impact of seasonality …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …

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. …

An Exploration Of Synergy Evaluation Application Model To Support Implementation On Merger And Acquisition, Jieping Mei

Electronic Theses, Projects, and Dissertations

ABSTRACT

The project focuses on a comprehensive system’s analysis and design of the front-end of the Synergy Evaluation Application Model (SEAM) system for mergers and acquisitions (M&As). The research questions asked are: Q1. How did the SEAM system incorporate the system requirements and design that incorporated the strategic goals and priorities of both the acquirer and the acquiree? Q2. What data sources will the SEAM system rely on, and how does it overcome data integration, automation, visualization challenges? Q3. How will the model identify build in potential synergies, both quantitative and qualitative? The research questions were analyzed through the SEAM …

Data-Driven Forecasting For Effective Demand Management In The E-Commerce Ecosystem, Chirag Pandey

Electronic Theses, Projects, and Dissertations

This culmination project investigated and analyzed the impact of factors influencing sales forecasting models in the e-commerce ecosystem. The research questions are: Q1) To what extent are sales forecasting models in the e-commerce ecosystems influenced by customer demographic variables such as gender, age, and geographic locations? Q2) To what extent are sales forecasting models in the e-commerce ecosystems influenced by product specific factors? The datasets used were from Kaggle, and the Worldometer websites. The findings are: Q1) Individuals aged '55 or over' significantly impact total sales in both the USA and Brazil. Male consumers consistently accounted for a higher proportion …

Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …

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 …

Identifying Effective Attributes And Trends In The Evolution Of Enterprise Architecture In Healthcare, Brian Gaul

Electronic Theses, Projects, and Dissertations

The purpose of this study was to determine which attributes within existing Enterprise Architecture frameworks are trending in recent, successful implementations within healthcare. The research questions were: Q1. What attributes were used within Enterprise Architecture in the healthcare industry? Q2. What are the limitations of those attributes? Q3. How can those attributes assist in successful Enterprise Architecture implementations? To uncover these attributes in practical work, this study used a trend analysis of current qualitative data of the healthcare industry and in recent implementations. The findings were as follows: Q1. Eight attributes were identified in practical healthcare work, the two most …

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 …

Analysis For An Efficient Operation Of Solar Power Plants In India Using Different Variables/Parameters, Sonal Bansi Shinde

Electronic Theses, Projects, and Dissertations

Vast renewable energy facilities rely heavily on accurate predictions of future solar power output.  This study investigated the various factors causing poor, inefficient operation of Solar Plants and different methods to identify underperforming equipment. The main questions are: Q1: How can we predict electricity generation over the next several days so that the plant can run at peak efficiency? Q2: How can we figure out the exact maintenance needs of any power plant?  Q3: How do we identify faulty equipment to improve its efficiency to improve overall performance? and Q4: What are the different factors that are causing an inefficient …

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 …

Big Data For Comprehensive Analysis Of Real Estate Market, Yonglin Xiao

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explored the application of big data in the real estate industry in order to address the problem of analyzing the accurate property estimates value. The research questions were: (Q1): What are the benefits and advantages of utilizing big data in the real estate market? (Q2): What are the trends in the application of big data in the real estate market? (Q3): What are the challenges in applying big data in the real estate market? (Q4): What are the methods and processes of applying big data in appraisal of assets in the real estate market? To answer …

The Four Agreements Analysis Los Cuatro Acuerdos De Don Miguel Ruíz, Dulce Morales

Electronic Theses, Projects, and Dissertations

The Toltecs were an indigenous Mexican culture of great warriors and artists with a very unique wisdom on life and behavior, that lived around 1000 years ago. Their extended knowledge on life and behavior certainly can be of help on developing a great leader in any organization based on the four agreements they practiced to better themselves spiritually, emotionally and physically. Therefore, Don Miguel Ruiz did some research of the Toltecs Wisdom and learn more about these four agreements:

1. Be impeccable with your word.
2. Don’t take anything personally.
3. Don’t make assumptions.
4. Always do your best.

After learning how to apply …

Human Resource Information Systems: Implementing Data Analytics Techniques In Human Resource Functions, Prachi Tembhekar

Electronic Theses, Projects, and Dissertations

This project investigated analytical techniques used by organizations across many industries for HR functions, and how data analytical techniques can help HR departments work efficiently. The goal was to look into the obstacles and opportunities that companies have when using HR analytics as a tool in their businesses. This project used secondary data acquired from earlier research articles, journals from the years 2016 to 2019, blogs, and websites to investigate theories and applications of HR analytics. It also examined the need of analytics to assist HR leaders in thinking about the implications of these technologies in future work and how …

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 …

Rise Of Facebook, Amazon, Apple, Netflix, Google During Covid-19 Pandemic, Shivraj Pisal

Electronic Theses, Projects, and Dissertations

FAANG is an acronym for Facebook, Apple, Amazon, Netflix, and Alphabet (which was previously known as Google), the five most important technology giants in the United States. The technology breakthroughs of these companies have had a significant impact on global economies, since they create jobs, link people, supply goods, and generate entertainment. As a result, a detailed analysis of FAANG stocks adds to the existing body of knowledge. Furthermore, because stock markets are very volatile and are influenced by a variety of known and unknown causes, it is vital to employ adequate tools for assessing their behavior (Jadhav et al., …

Impact On Revenue Generation Of Olist Ecommerce Company On The Basis Of Various Product Parameters, Trupti Sanjay Niwate

Electronic Theses, Projects, and Dissertations

E-commerce business is the most trending business in the entire world. Due to the COVID-19 pandemic, the global market of e-commerce is much more active than the traditional in-store market. Over the period, people used the internet more, which has incentivized them to shop online because it saves time and effort.

Specifically, in Latin America, the E-Commerce business is expanding at a tremendous rate. In particular, Brazil is a major business hub when we consider an e-commerce business since it has given a major impact on the world‘s total e-commerce business. Olist, the main e-commerce company which helps other marketers …

An Overview On Amazon Rekognition Technology, Raghavendra Kumar Indla

Electronic Theses, Projects, and Dissertations

The Covid-19 pandemic has disrupted the daily operations of many businesses due to which they were forced to follow the guidelines set by the local, state, and federal government to reduce the spread of the Covid-19 virus. This project focused on how few businesses that resumed their operations during the onset of Covid-19 integrated Amazon Web Services Rekognition technology to comply with government rules and regulations.

Docs_On_Blocks – A Defense In Depth Strategy For E-Healthcare, Saad Mohammed

Electronic Theses, Projects, and Dissertations

With the increase in the data breaches and cyber hacks, organizations have come to realize that cyber security alone would not help as the attacks are becoming more sophisticated and complex than ever. E-Healthcare industry has shown a promising improvement in terms of security over the past, but the threat remains. Thus, the E-Healthcare industries are aiming towards a Defense in Depth Strategy approach.

The project here describes how a Defense in Depth Strategy for E-Healthcare system can provide an environment for better security of the data and peer-to-peer interaction with stakeholders. The legacy systems have at some point failed …

Online Strategies For Small Businesses Affected By Covid-19: A Social Media And Social Commerce Approach, Julianne Itliong

Electronic Theses, Projects, and Dissertations

The Covid-19 pandemic has altered the way many US citizens work, live, and interact with one another. Many rules and regulations have been put in place by government officials to slow the spread of the virus because of how contagious it is in social settings. Because of these rules and regulations, many small businesses were forced to temporarily close their doors to the public in order to comply with the rules of social distancing and the ban on large gatherings. For some businesses, this shift from physical sales to digital sales has always been a part of their business models, …