10 citations · 18 across the 7 of their papers we have counts for
7 papers
AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities
Elaheh Jafarigol, Behnoud Alaghband, Azadeh Gilanpour +2
We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We rep…
Using Federated Machine Learning in Predictive Maintenance of Jet Engines
Asaph Matheus Barbosa, Thao Vy Nhat Ngo, Elaheh Jafarigol +2
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 d…
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
Elaheh Jafarigol, Theodore Trafalis, Talayeh Razzaghi +1
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning la…
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
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 priv…
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs
Elaheh Jafarigol, Theodore Trafalis
The classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions for various sectors su…
Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey
Elaheh Jafarigol, William Keely, Tess Hartog +3
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 belie…
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
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…