Eman M. G. Younis

Papers

Total Citations

411

H-Index

About

Eman M. G. Younis is a prominent researcher specializing in affective computing, emotion recognition, and machine learning, with a particular focus on leveraging multimodal sensor data to decode and monitor human emotional states. Her work sits at the intersection of deep learning, physiological signal processing, and human-computer interaction, addressing one of the most challenging frontiers in artificial intelligence. Younis's most influential contribution, "Deep Learning Analysis of Mobile Physiological, Environmental and Location Sensor Data for Emotion Detection" (2018), has garnered an impressive 291 citations, establishing her as a leading voice in mobile-based emotion detection. This work demonstrated how diverse data streams — including EEG, galvanic skin response, and environmental signals — could be synthesized through deep learning architectures to enable naturalistic emotion recognition in real-world settings. Building on this foundation, her 2022 study on ensemble learning methods for multi-modal emotion recognition (59 citations) advanced sensor data fusion techniques, while her comprehensive 2024 review of machine learning for emotion recognition (61 citations) has quickly become a key reference for researchers entering the field. Collectively, her research has meaningfully shaped how scientists and engineers approach automated human emotion recognition, with broad applications spanning mental health monitoring, human-robot interaction, and personalized computing systems.