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Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

Journal of Data Analysis and Information Processing 2024年第1期12卷 49-75页

作者： Somtobe Olisah Clement Odooh Oghenekome Efijemue Echezona Obunadike Jane Onwuchekwa omoshola owolabi Saheed Akintayo Callistus Obunadike Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Data Science Carolina University Winston-Salem NC USA

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

关键词： Human Trafficking Global Trends Data Analysis Victim Demographics Policy Implications Technological Advancements Socioeconomic Factors Forecasting Regional Disparities Transnational Crime

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The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

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Journal of Data Analysis and Information Processing 2024年第3期12卷 370-387页

作者： Clement Odooh Somtobe Olisah Jane Onwuchekwa omoshola owolabi Sevidzem Simo Yufenyuy Oluwadare Aderibigbe Echezona Obunadike Oghenekome Efijemue Saheed Akintayo Samson Edozie Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Data Science Carolina University Winston-Salem NC USA

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

关键词： Crime Analysis Chicago Data Visualization Crime Trends Power BI Urban Safety

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Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

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Journal of Data Analysis and Information Processing 2024年第3期12卷 388-418页

作者： omoshola S. owolabi Prince C. Uche Nathaniel T. Adeniken Oghenekome Efijemue Samuel Attakorah Oluwabukola G. Emi-Johnson Emmanuel Hinneh Department of Data Science Carolina University Winston-Salem NC USA Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Statistical Sciences Wake Forest University Winston-Salem NC USA

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

关键词： Churn Prediction Machine Learning Economic Impact Industry-Specific Insights Logistic Regression Random Forest Neural Networks

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Blockchain-Based System for Secure and Efficient Cross-Border Remittances: A Potential Alternative to SWIFT

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Journal of Software Engineering and Applications 2024年第8期17卷 664-712页

作者： omoshola S. owolabi Emmanuel Hinneh Prince C. Uche Nathaniel T. Adeniken Jennifer A. Ohaegbulem Samuel Attakorah Oluwabukola G. Emi-Johnson Chinaza S. Belolisa Harold Nwariaku Department of Data Science Carolina University Winston Salem NC USA Patterson School of Business Carolina University Winston Salem NC USA Department of Statistical Sciences Wake Forest University Winston Salem NC USA Department of Computer Science Fisk University Nashville TN USA Harold & Co Procurement and Supply Chain Consulting Lagos Nigeria

This paper proposes a blockchain-based system as a secure, efficient, and cost-effective alternative to SWIFT for cross-border remittances. The current SWIFT system faces challenges, including slow settlement times, high transaction costs, and vulnerability to fraud. Leveraging blockchain technology’s decentralized, transparent, and immutable nature, the proposed system aims to address these limitations. Key features include modular architecture, implementation of microservices, and advanced cryptographic protocols. The system incorporates Proof of Stake consensus with BLS signatures, smart contract execution with dynamic pricing, and a decentralized oracle network for currency conversion. A sophisticated risk-based authentication system utilizes Bayesian networks and machine learning for enhanced security. Mathematical models are presented for critical components, including transaction validation, currency conversion, and regulatory compliance. Simulations demonstrate potential improvements in transaction speed and costs. However, challenges such as regulatory hurdles, user adoption, scalability, and integration with legacy systems must be addressed. The paper provides a comparative analysis between the proposed blockchain system and SWIFT, highlighting advantages in transaction speed, costs, and security. Mitigation strategies are proposed for key challenges. Recommendations are made for further research into scaling solutions, regulatory frameworks, and user-centric designs. The adoption of blockchain-based remittances could significantly impact the financial sector, potentially disrupting traditional models and promoting financial inclusion in underserved markets. However, successful implementation will require collaboration between blockchain innovators, financial institutions, and regulators to create an enabling environment for this transformative system.

关键词： Interoperability Tokens AML/KYC Compliance Peer-to-Peer Transfers Financial Inclusion

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