Digital Commons Network^™

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 …

Security-Oriented Voice Authentication Using Machine Learning, Ifeoluwa Stella Elegbe

College of Graduate Studies: Theses & Dissertations

This study develops and evaluates a machine learning and deep learning-based voice authentication system for secure identity verification. As traditional authentication methods such as passwords, PINs, and security tokens continue to face challenges, including identity theft, forgetting, and unauthorized access, voice biometrics offers a more secure, convenient, and user-friendly alternative, especially for remote, hands-free, and accessibility-focused applications. The study adopts a closed-set speaker identification framework, where the system determines the most likely speaker from a predefined group of enrolled users. A structured methodology is implemented, beginning with audio preprocessing and feature extraction. Key acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), …

Identifying Potentially Illicit Money Laundering And Terrorism Financing Transactions Through Machine Learning Techniques, Shiuh Tong Lim, Rou Qing Khoo, Khai Wah Khaw, Xinying Chew

International Journal of Management, Finance and Accounting

Financial institutions worldwide face significant challenges in detecting and preventing illicit financial activities, such as money laundering and terrorism financing. Traditional rule-based methods often generate high false positive rates, increasing manual verification efforts and higher operational costs. This research explores machine learning techniques to enhance the detection of suspicious transactions. Several algorithms, including K-Nearest Neighbors, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, and Naïve Bayes, are applied and evaluated using a dataset from a financial institution. After a comprehensive performance assessment, the Random Forest model is the most effective, exhibiting the highest accuracy of 0.9333 in identifying suspicious …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Design And Development Of Machine Learning And Deep Learning Based Algorithms For Cyberattacks Detection, Abiramasundari S

Theses and Dissertations

E-mail is the fastest mode of communication. It is amongst the most commonly used modes of communication and can be used for both legal and illegal purposes. Many elements that could be useful in detecting email fraud are constantly being investigated. Phishing assaults in which thieves use Internet to trick consumers into visiting fake websites are very common and are causing significant harm to victims. Several methods of filtering phishing emails have been devised but a solution to the problem still evades. One of the attacks addressed in this research is email phishing. Machine learning is a popular and efficient …

Revisiting Android Permission Evolution Through Temporal Analysis: A Data-Driven Study Of Behaviors And Implications, Ali Al Kinoon

Graduate Thesis and Dissertation post-2024

Malicious applications continue to pose significant privacy and security risks within the Android ecosystem, often exploiting user permissions and obscuring data collection practices behind opaque privacy policies. To address these challenges, this dissertation presents a comprehensive framework for enhancing Android app security through dataset construction, permission behavior analysis, and privacy policy classification. The framework systematically investigates key issues such as dataset fidelity, permission misuse, model performance, and the alignment between declared and actual data practices. Through a combination of empirical analysis and machine learning techniques, this work advances the development of more transparent and secure mobile applications. The first part …

The Effectiveness Of Using Machine Learning Techniques To Improve Online Education Advantages, Challenges, And Barrier, M. Samir Abou El-Seoud

Computer Science

In this paper, we are going to analyze the effectiveness of using machine learning (ML) techniques to improve online education. Nowadays, numerous universities and online education providers have expanded their offerings. For the uninitiated, we can simply refer to online education as distance learning facilitated through the Internet and a computer. Online education, as a component of distance learning, is gaining popularity due to its offerings. In recent years, online education has become an important part of contemporary education. It's important because it allows students to learn or get access to study materials without attending a conventional class at a …

Enhancing Ai-Driven Automation For Object Detection And Computer Vision, Nafeeul Alam Walee

College of Graduate Studies: Theses & Dissertations

In recent years, AI-driven automation has revolutionized the field of object detection and computer vision, enabling sophisticated and efficient solutions across various industries. This research explores the latest advances and techniques in improving AI-driven automation for object detection and computer vision applications. We examine state-of-the-art deep learning models and frameworks that have contributed to significant improvements in accuracy and speed and highlight the generative results. The focus is on exploring the real-time processing capabilities that have expanded the applicability of these technologies in real-world scenarios. Furthermore, we investigate image integration and video data to improve precision detection and contextual understanding. …

Unveiling The Efficacy Of Ai-Based Algorithms In Phishing Attack Detection, Tajamul Shahzad, Kashif Aman

Journal of Informatics and Web Engineering

Phishing poses a significant challenge in an ever-evolving world. The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. The term cybercrime describes the act of invading privacy and illegitimately obtaining personal information using digital platform. Primarily an approach named phishing is employed, which involves the use of spoof emails or bogus websites by the attackers to get the victim's personal information like their account credentials, debit, or credit card’s number, etc. To give the brief knowledge of phishing attacks and their types of the objective of this work …

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 …

Deciphering Trends And Tactics: Data-Driven Techniques For Forecasting Information Spread And Detecting Coordinated Campaigns In Social Media, Kin Wai Ng Lugo

USF Tampa Graduate Theses and Dissertations

The main objective of this dissertation is to develop models that predict and investigate the spread of information in social media over time. In this context, we consider topics of discussions as the information that spreads. Thus, we are interested in forecasting the number of messages per day in a future interval of time. We take a data-driven approach, in which we compare our results with real datasets from a multitude of socio-political contexts and from multiple social media platforms, specifically, Twitter and YouTube.

We identified a number of challenges related to forecasting social media time series per topic. First, …

Application Of Machine Learning Algorithm For Creating Sustainable Omni-Channel Retail Ecosystem, Somedip Karmakar, Anuja Shukla

Management Dynamics

The retail ecosystem has evolved from simple Kirana stores to large omni-channel retail systems. The advent of omni-channel retailing has seen an increased count of fraudsters who can abuse the gap between the online and offline channels to perform different types of fraudulent activities. Fraud can be related to payment, account take over, refund and cancellation abuse, collusion of customers with associates. Waste can be due to over-production, sub-optimal pricing or discounts, damaged products, throwaways, availability issues, packaging waste. The paper tries to identify the need for better research on improvement in fraud detection systems for sparse data, improvement of …

Balancing Privacy And Accuracy In Iot Using Domain-Specific Features For Time Series Classification, Pranshul Lakhanpal

Master's Theses

ε-Differential Privacy (DP) has been popularly used for anonymizing data to protect sensitive information and for machine learning (ML) tasks. However, there is a trade-off in balancing privacy and achieving ML accuracy since ε-DP reduces the model’s accuracy for classification tasks. Moreover, not many studies have applied DP to time series from sensors and Internet-of-Things (IoT) devices. In this work, we try to achieve the accuracy of ML models trained with ε-DP data to be as close to the ML models trained with non-anonymized data for two different physiological time series. We propose to transform time series into domain-specific 2D …

Ai Approaches To Understand Human Deceptions, Perceptions, And Perspectives In Social Media, Chih-Yuan Li

Dissertations

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens' perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups.

This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine …

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 …

A Qualitative Study On Predictive Models In Accounting Fraud Detection, Anthony Cecil

Doctoral Dissertations and Projects

Companies lose an estimated 5% of revenue each year due to occupational fraud. This level of fraud can significantly disrupt the capital markets and cause companies to go bankrupt. Unless organizations, the government, and the accounting profession develop a systematic approach for accounting fraud detection, investors and employees will continue to lose money. This study explored subject matter experts’ perceptions of building and deploying artificial intelligence and predictive models to detect accounting fraud. This case study consisted of interviews with 10 participants with expertise in predictive modeling, auditing, and investigating, as well as a systematic literature review of research and …

Machine Learning Techniques For Credit Card Fraud Detection, Hossam Eldin Mohammed Abd El-Hamid Ahmed Abdou, Wael Khalifa, Mohamed Ismail Roushdy, Abdel-Badeeh M. Salem

Future Computing and Informatics Journal

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they …

Handling Highly Imbalanced Output Class Label: A Case Study On Fantasy Premier League (Fpl) Virtual Player Price Changes Prediction Using Machine Learning, Muhammad Muhaimin Khamsan, Ruhaila Maskat

Malaysian Journal of Computing (MJoC)

In practice, a balanced target class is rare. However, an imbalanced target class can be handled by resampling the original dataset, either by oversampling/upsampling or undersampling/downsampling. A popular upsampling technique is Synthetic Minority Over-sampling Technique (SMOTE). This technique increases the minority class by generating synthetic class labels and assigned the class based on the K-Nearest Neighbour (K-NN). SMOTE upsampling can only upsample at most one minority class at a time, which means for a multiclass dataset, it needs to undergo multilayer SMOTE to balance the class label distribution. This paper aims to find a suitable method in handling imbalanced class …

Df 2.0: An Automated, Privacy Preserving, And Efficient Digital Forensic Framework That Leverages Machine Learning For Evidence Prediction And Privacy Evaluation, Robin Verma, Jayaprakash Govindaraj Dr, Saheb Chhabra, Gaurav Gupta

Journal of Digital Forensics, Security and Law

The current state of digital forensic investigation is continuously challenged by the rapid technological changes, the increase in the use of digital devices (both the heterogeneity and the count), and the sheer volume of data that these devices could contain. Although data privacy protection is not a performance measure, however, preventing privacy violations during the digital forensic investigation, is also a big challenge. With a perception that the completeness of investigation and the data privacy preservation are incompatible with each other, the researchers have provided solutions to address the above-stated challenges that either focus on the effectiveness of the investigation …

Automated Essay Evaluation Using Natural Language Processing And Machine Learning, Harshanthi Ghanta

Theses and Dissertations

The goal of automated essay evaluation is to assign grades to essays and provide feedback using computers. Automated evaluation is increasingly being used in classrooms and online exams. The aim of this project is to develop machine learning models for performing automated essay scoring and evaluate their performance. In this research, a publicly available essay data set was used to train and test the efficacy of the adopted techniques. Natural language processing techniques were used to extract features from essays in the dataset. Three different existing machine learning algorithms were used on the chosen dataset. The data was divided into …