Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Data Mining (2)
- ARIMA (1)
- Anomaly Detection (1)
- Anomaly detection (1)
- AutoML; Intelligent binning; Regularized regression; Binary classification; Data preparation (1)
-
- COVID-19 modeling; ARIMA time series; Data analysis; Gender differences; Forecasting accuracy (1)
- Change point detection; Support vector methods; Online anomaly detection; Sequential data streams; Kernel functions (1)
- Classification (1)
- Clothing Retail Sales (1)
- Clustering (1)
- Deep learning (1)
- Economy (1)
- Feature Reduction (1)
- Feature Selection (1)
- Forecasting (1)
- GDP (1)
- Generative Adversarial Networks (GANs); Fraud Detection; Bayesian Model; Variable Selection; Data Imbalance; Synthetic Data Generation (1)
- Graph neural networks; Interpretability; Efficiency; Anomaly detection; Sampling strategies (1)
- Instruction Sequences (1)
- Intrusion Detection Systems (1)
- Machine Learning (1)
- Malware Detection (1)
- Network Modeling (1)
- Neural network (1)
- Nintendo (1)
- One-class classification (1)
- One-class support vector machine (1)
- Predictive Modeling (1)
- Regression (1)
- Seasonality (1)
Articles 1 - 11 of 11
Full-Text Articles in Categorical Data Analysis
From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins
From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins
Honors Undergraduate Theses
With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …
Bayesian Variable Selection With Shrinkage Priors And Generative Adversarial Networks For Fraud Detection, Amina Issoufou Anaroua
Bayesian Variable Selection With Shrinkage Priors And Generative Adversarial Networks For Fraud Detection, Amina Issoufou Anaroua
Graduate Thesis and Dissertation 2023-2024
This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with Uni and Multi Variate Bayesian Model with Shrinkage Priors (BMSP). The problem addressed is the need for accurate and advanced fraud detection techniques due to the increasing sophistication of fraudulent activities. The methodology involves the implementation of GANs and the application of BMSP for variable selection to generate synthetic fraud samples for fraud detection using the augmented dataset. Experimental results demonstrate the effectiveness of the BMSP GAN approach in detecting fraud with improved performance compared to other methods. The conclusions drawn …
Deep Learning One-Class Classification With Support Vector Methods, Hayden D. Hampton
Deep Learning One-Class Classification With Support Vector Methods, Hayden D. Hampton
Graduate Thesis and Dissertation 2023-2024
Through the specialized lens of one-class classification, anomalies–irregular observations that uncharacteristically diverge from normative data patterns–are comprehensively studied. This dissertation focuses on advancing boundary-based methods in one-class classification, a critical approach to anomaly detection. These methodologies delineate optimal decision boundaries, thereby facilitating a distinct separation between normal and anomalous observations. Encompassing traditional approaches such as One-Class Support Vector Machine and Support Vector Data Description, recent adaptations in deep learning offer a rich ground for innovation in anomaly detection. This dissertation proposes three novel deep learning methods for one-class classification, aiming to enhance the efficacy and accuracy of anomaly detection in …
Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu
Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu
Electronic Theses and Dissertations, 2020-2023
Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to …
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Graph Neural Networks For Improved Interpretability And Efficiency, Patrick Pho
Electronic Theses and Dissertations, 2020-2023
Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation …
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Change Point Detection For Streaming Data Using Support Vector Methods, Charles Harrison
Electronic Theses and Dissertations, 2020-2023
Sequential multiple change point detection concerns the identification of multiple points in time where the systematic behavior of a statistical process changes. A special case of this problem, called online anomaly detection, occurs when the goal is to detect the first change and then signal an alert to an analyst for further investigation. This dissertation concerns the use of methods based on kernel functions and support vectors to detect changes. A variety of support vector-based methods are considered, but the primary focus concerns Least Squares Support Vector Data Description (LS-SVDD). LS-SVDD constructs a hypersphere in a kernel space to bound …
An Evaluation Of The Performance Of Proc Arima's Identify Statement: A Data-Driven Approach Using Covid-19 Cases And Deaths In Florida, Fahmida Akter Shahela
An Evaluation Of The Performance Of Proc Arima's Identify Statement: A Data-Driven Approach Using Covid-19 Cases And Deaths In Florida, Fahmida Akter Shahela
Electronic Theses and Dissertations, 2020-2023
Understanding data on novel coronavirus (COVID-19) pandemic, and modeling such data over time are crucial for decision making at managing, fighting, and controlling the spread of this emerging disease. This thesis work looks at some aspects of exploratory analysis and modeling of COVID-19 data obtained from the Florida Department of Health (FDOH). In particular, the present work is devoted to data collection, preparation, description, and modeling of COVID-19 cases and deaths reported by FDOH between March 12, 2020, and April 30, 2021. For modeling data on both cases and deaths, this thesis utilized an autoregressive integrated moving average (ARIMA) times …
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang
Honors Undergraduate Theses
This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past …
A Simulation-Based Task Analysis Using Agent-Based, Discrete Event And System Dynamics Simulation, Anastasia Angelopoulou
A Simulation-Based Task Analysis Using Agent-Based, Discrete Event And System Dynamics Simulation, Anastasia Angelopoulou
Electronic Theses and Dissertations
Recent advances in technology have increased the need for using simulation models to analyze tasks and obtain human performance data. A variety of task analysis approaches and tools have been proposed and developed over the years. Over 100 task analysis methods have been reported in the literature. However, most of the developed methods and tools allow for representation of the static aspects of the tasks performed by expert system-driven human operators, neglecting aspects of the work environment, i.e. physical layout, and dynamic aspects of the task. The use of simulation can help face the new challenges in the field of …
Data Mining Methods For Malware Detection, Muazzam Siddiqui
Data Mining Methods For Malware Detection, Muazzam Siddiqui
Electronic Theses and Dissertations
This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval …
Session-Based Intrusion Detection System To Map Anomalous Network Traffic, Bruce Caulkins
Session-Based Intrusion Detection System To Map Anomalous Network Traffic, Bruce Caulkins
Electronic Theses and Dissertations
Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal -- firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports …