Theodore B. Trafali̇s

Elaheh Jafarigol, William Keely, Tess Hartog et al.

This paper is a quantitative analysis of the data collected globally by the World Value Survey. The data is used to study the trajectories of change in individuals' religious beliefs, values, and behaviors in societies. Utilizing random forest, we aim to identify the key factors of religiosity and classify respondents of the survey as religious and non religious using country level data. We use resampling techniques to balance the data and improve imbalanced learning performance metrics. The results of the variable importance analysis suggest that Age and Income are the most important variables in the majority of countries. The results are discussed with fundamental sociological theories regarding religion and human behavior. This study is an application of machine learning in identifying the underlying patterns in the data of 30 countries participating in the World Value Survey. The results from variable importance analysis and classification of imbalanced data provide valuable insights beneficial to theoreticians and researchers of social sciences.

Elaheh Jafarigol, Theodore Trafalis, Talayeh Razzaghi et al.

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided.

Elaheh Jafarigol, Theodore Trafalis, Neshat Mohammadi

For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers in an attempt to provide an in-depth review of various approaches in imbalanced learning from technical and application perspectives. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains and create a general guideline for researchers in academia or industry who want to dive into the broad field of machine learning using large-scale imbalanced data.

Elaheh Jafarigol, Theodore Trafalis

In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of deep learning models in the presence of additive noise in federated learning frameworks. Our main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. The paper introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion strategies to enhance privacy without compromising performance. Our research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. By leveraging noise as a tool for regularization and privacy enhancement, we aim to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy.

Asaph Matheus Barbosa, Thao Vy Nhat Ngo, Elaheh Jafarigol et al.

The goal of this paper is to predict the Remaining Useful Life (RUL) of turbine jet engines using a federated machine learning framework. Federated Learning enables multiple edge devices/nodes or servers to collaboratively train a shared model without sharing sensitive data, thus preserving data privacy and security. By implementing a nonlinear model, the system aims to capture complex relationships and patterns in the engine data to enhance the accuracy of RUL predictions. This approach leverages decentralized computation, allowing models to be trained locally at each device before aggregating the learned weights at a central server. By predicting the RUL of jet engines accurately, maintenance schedules can be optimized, downtime reduced, and operational efficiency improved, ultimately leading to cost savings and enhanced performance in the aviation industry. Computational results are provided by using the C-MAPSS dataset which is publicly available on the NASA website and is a valuable resource for studying and analyzing engine degradation behaviors in various operational scenarios.

Karen Roberts-Licklider, Theodore Trafalis

The aim of this study is to look at predicting whether a person will complete a drug and alcohol rehabilitation program and the number of times a person attends. The study is based on demographic data obtained from Substance Abuse and Mental Health Services Administration (SAMHSA) from both admissions and discharge data from drug and alcohol rehabilitation centers in Oklahoma. Demographic data is highly categorical which led to binary encoding being used and various fairness measures being utilized to mitigate bias of nine demographic variables. Kernel methods such as linear, polynomial, sigmoid, and radial basis functions were compared using support vector machines at various parameter ranges to find the optimal values. These were then compared to methods such as decision trees, random forests, and neural networks. Synthetic Minority Oversampling Technique Nominal (SMOTEN) for categorical data was used to balance the data with imputation for missing data. The nine bias variables were then intersectionalized to mitigate bias and the dual and triple interactions were integrated to use the probabilities to look at worst case ratio fairness mitigation. Disparate Impact, Statistical Parity difference, Conditional Statistical Parity Ratio, Demographic Parity, Demographic Parity Ratio, Equalized Odds, Equalized Odds Ratio, Equal Opportunity, and Equalized Opportunity Ratio were all explored at both the binary and multiclass scenarios.