Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Emotion Recognition from Facial Expression and Brain Signals
Published in Undergraduate Thesis, University of Asia Pacific, 2022
This thesis explores emotion recognition using facial expressions and EEG signals for improved accuracy.
M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity
Published in Journal: MDPI Sensors, 2022
This paper proposes two CNN models (M1 and M2) for emotion recognition from brain signals using EEG data, achieving nearly perfect accuracy.
Recommended citation: Akter, S., Prodhan, R. A., Pias, T. S., Eisenberg, D., & Fresneda Fernandez, J. (2022). M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity. *Sensors*, 22(21), 8467.
Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis
Published in Conference: Sentiment Analysis and Deep Learning, ICSADL 2022, 2023
This paper evaluates the effectiveness of various machine learning algorithms and deep learning models for classifying sentiment from EEG signals.
Recommended citation: Akter, S., Prodhan, R. A., Mujib, M. B., Adnan, M. A., & Pias, T. S. (2023). Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis. In *Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022* (pp. 195-212). Singapore: Springer Nature Singapore.
Emotion Recognition from Brain Wave using Multitask Machine Learning Leveraging Residual Connections
Published in Conference: International Conference on Machine Intelligence and Emerging Technologies, 2023
This paper presents a multitask learning approach for emotion recognition from brain waves leveraging residual connections.
Recommended citation: Prodhan, R. A., Akter, S., Mujib, M. B., Adnan, M. A., & Pias, T. S. (2023, June). Emotion recognition from brain wave using multitask machine learning leveraging residual connections. In *International Conference on Machine Intelligence and Emerging Technologies* (pp. 121-136). Cham: Springer Nature Switzerland.
Optimal EEG Electrode Set for Emotion Recognition from Brain Signals: An Empirical Quest
Published in Conference: 4th International Conference on Activity and Behavior Computing, 2024
This study empirically analyzes the contribution of different brain regions to emotion recognition using an optimal EEG electrode set.
Recommended citation: Prodhan, R. A., Akter, S., Adnan, M. A., & Pias, T. S. (2024). Optimal EEG Electrode Set for Emotion Recognition from Brain Signals: An Empirical Quest. In *4th International Conference on Activity and Behavior Computing* (pp. 67-88), October 27-29, 2022, London, United Kingdom. CRC Press.
Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs
Published in 27th IEEE International Conference on Computer and Information Technology (ICCIT 2024), 2024
This paper introduces a custom SegUNet architecture with a VGG19 backbone for improved segmentation of pediatric dental panoramic radiographs.
Recommended citation: Ovi, Md Ohiduzzaman, Maliha Sanjana, Fahad Fahad, Mahjabin Runa, Zarin Tasnim Rothy, Tanmoy Sarkar Pias, A. M. Islam, and Rumman Ahmed Prodhan. "Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs." arXiv preprint arXiv:2503.06321 (2025).
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching Experience
Lectures and Research Supervision, Northern University Bangladesh, Department of Computer Science & Engineering, 2022