activity
20232026
most citedArtificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

25 citations · 46 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP2026

Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection

Stefanos Gkikas, Thomas Kassiotis, Yang Guo +5

Electrodermal activity (EDA) is widely used in automatic stress detection, yet most pipelines treat it only as a raw one-dimensional waveform. This study examines whether complemen…

cs.AI2026

Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks

Christian Arzate Cruz, Stefanos Gkikas, Houshyar Asadi

Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweigh…

cs.LG2026

A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification

Mehshan Ahmed Khan, Houshyar Asadi, Li Zhang +8

Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differen…

eess.IV2024★ 21 cited

Current and future roles of artificial intelligence in retinopathy of prematurity

Ali Jafarizadeh, Shadi Farabi Maleki, Parnia Pouya +9

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. Wh…

q-bio.QM2023★ 25 cited

Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Mirsaeed Abdollahi, Ali Jafarizadeh, Amirhosein Ghafouri Asbagh +7

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods - in particular, deep learning (DL) - has been o…