EDBT 2026 Demo / reviewers in the wild / expert
Saeid Nahavandi
dblp:55/1059
· DBLP profile ↗
6ranked-venue papers in the field
0as first author
2since 2021 · last 2021
0000-0002-0360-5270ORCID · corroborated
Domains — venue-derived; a paper can count in several
Knowledge Engineering, Semantic Web & Information Systems · 4Data Mining & Knowledge Discovery · 2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification
Moloud Abdar, Mohammad Amin Fahami, Satarupa Chakrabarti, Abbas Khosravi, Pawel Plawiak, U. Rajendra Acharya, Ryszard Tadeusiewicz, Saeid Nahavandi |
Inf. Sci. | 8 |
| 2021 | Automated detection of shockable ECG signals: A review
Mohamed Hammad, Kandala N. V. P. S. Rajesh, Amira Abdelatey, Moloud Abdar, Mariam Zomorodi Moghadam, Ru-San Tan, U. Rajendra Acharya, Joanna Plawiak, Ryszard Tadeusiewicz, Vladimir Makarenkov, Nizal Sarrafzadegan, Abbas Khosravi, Saeid Nahavandi, Ahmed A. Abd El-Latif 0001, Pawel Plawiak |
Inf. Sci. | 13 |
| 2019 | A scalarization-based dominance evolutionary algorithm for many-objective optimization
Burhan Khan, Samer Hanoun, Michael Johnstone, Chee Peng Lim, Douglas C. Creighton, Saeid Nahavandi |
Inf. Sci. | 6 |
| 2015 | Hidden Markov models for cancer classification using gene expression profiles
Thanh Thi Nguyen 0001, Abbas Khosravi, Douglas C. Creighton, Saeid Nahavandi |
Inf. Sci. | 4 |
| 2013 | Optometry training simulation with augmented reality and hapticsabstractOptometry is an essential health care profession that has existed for many centuries and is still evolving. However, the training approaches for optometrists are not yet on par with the latest technological evolution. The traditional supervisor-student training mode could not provide good immersion and repeatability, while most existing vision-based computer-assisted simulations provide even worse immersion on screens. In this paper, we propose an effective system for optometry training simulation with two major components: augmented reality and haptics. These components are integrated with the actual slit lamp and are able to greatly enhance the immersion for typical optometry training tasks such as foreign body removal. Medical doctors are also involved in suggesting configurations and validating visual and haptic rendering results. Preliminary user studies show very positive feedbacks from optometry students. Lei Wei 0002, Saeid Nahavandi, Harrison Weisinger |
ASONAM | 2 |
| 2013 | Lab-on-a-chip turns soft: computer-aided, software-enabled microfluidics designabstractThe current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique. Aung K. Soe, Michael Fielding, Saeid Nahavandi |
ASONAM | 3 |