TEDU Computer Engineering Students Presented Three Research Papers at IEEE-ICHORA 2026
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Our faculty member Eren Ulu supervised students who participated in the 8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (IEEE-ICHORA 2026), held in Ankara, Türkiye on May 21–23, 2026. Within the scope of the conference, three research papers were presented focusing on artificial intelligence, computer vision, and natural language processing topics. The presented studies include: “Attention Comparison in Task-Free vs. Task-Driven Viewing: AI vs. Real Images” by İrem Şimşek and Eren Ulu, “Facial Emotion Detection Using Pre-Trained CNN Models: A Study on Fine-Tuning and Robustness in Real-Time Settings” by Rovshana Haji-Hasanli and Eren Ulu, and “Fake News Detection Capability Across Contemporary LLMs: Comparative Study of Zero-Shot, Few-Shot, and Parameter-Efficient Fine-Tuning Across LIAR and ISOT” by Emre Karacakurt, Ecem Sıla Gök, and Eren Ulu. These studies reflect the active involvement of our students in current research areas including deep learning, computer vision, and large language models.Our faculty member Eren Ulu supervised students who participated in the 8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (IEEE-ICHORA 2026), held in Ankara, Türkiye on May 21–23, 2026. Within the scope of the conference, three research papers were presented focusing on artificial intelligence, computer vision, and natural language processing topics. The presented studies include: “Attention Comparison in Task-Free vs. Task-Driven Viewing: AI vs. Real Images” by İrem Şimşek and Eren Ulu, “Facial Emotion Detection Using Pre-Trained CNN Models: A Study on Fine-Tuning and Robustness in Real-Time Settings” by Rovshana Haji-Hasanli and Eren Ulu, and “Fake News Detection Capability Across Contemporary LLMs: Comparative Study of Zero-Shot, Few-Shot, and Parameter-Efficient Fine-Tuning Across LIAR and ISOT” by Emre Karacakurt, Ecem Sıla Gök, and Eren Ulu. These studies reflect the active involvement of our students in current research areas including deep learning, computer vision, and large language models.