Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application. - Elaheh Jafarigol; Theodore B. Trafalis (SN Comput. Sci., 2020)

A Review of Machine Learning Techniques in Imbalanced Data and Future Trends. - Elaheh Jafarigol; Theodore B. Trafalis (CoRR, 2023)

Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey. - Elaheh Jafarigol; William Keely; Tess Hartog; Tom Welborn; Peyman Hekmatpour; Theodore B. Trafalis (CoRR, 2023)

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning. - Elaheh Jafarigol; Theodore B. Trafalis (CoRR, 2023)

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations. - Elaheh Jafarigol; Theodore B. Trafalis; Talayeh Razzaghi; Mona Zamankhani (CoRR, 2023)

A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs. - Elaheh Jafarigol; Theodore B. Trafalis (Comput. Manag. Sci., 2024)

AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities. - Elaheh Jafarigol; Behnoud Alaghband; Azadeh Gilanpour; Saeid Hosseinipoor; Mirhamed Mirmozafari (CoRR, 2025)

Using Federated Machine Learning in Predictive Maintenance of Jet Engines. - Asaph Matheus Barbosa; Thao Vy Nhat Ngo; Elaheh Jafarigol; Theodore B. Trafalis; Emuobosa P. Ojoboh (CoRR, 2025)