The Joint Symposium Computational Intelligence (JSCI) is an event which was first organised in 2016. The event was initiated by IEEE Computational Intelligence Society Thailand Chapter (IEEE-CIS Thailand), that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event is open to all researchers who want to broaden their knowledge in the field of computational intelligence.
The event will be co-located with the 11th International Conference on Information Technology and Electrical Engineering (ICITEE 2019), on 10-11 October 2019 at Holiday Inn Pattaya, Thailand. The symposium will feature paper presentations as well as an invited talk. JSCI7 will be held on October 10, 2019.
This is a very good opportunity to share and discuss your work with other researchers in the field of computational intelligence. If you are interested in presenting your work at this symposium, please submit a paper at [EDAS]. The paper must conform to the standard of IEEE Manuscript Templates for Conference Proceedings which is available to download at [IEEE]. The papers are 4-6 pages. The paper will be double-blind peer-reviewed. Accepted papers will be published in ICITEE2019 proceedings (IEEExplore).
Important Dates
Submission Deadline: 30 August 2019
Notification of Acceptance: 9 September 2019
Camera Ready Submission: 20 September 2019
Registration: 9-20 September 2019
Symposium Date: 10 October 2019
Organizing Committee
Advisory
Chair
Organising Committee
Final Program
| Time | Activities | Topic/Speaker |
| 15:15 – 15.45 | Invited Talk |
Empirical Monocomponent Image Decomposition
Kuntpong Woraratpanya Abstract: Monocomponent image decomposition plays an important role in image analysis and related areas, such as image denoising, object detection, and texture segmentation. Existing image decomposition methods can extract monocomponents but their performances are insufficiently accurate because of interference and redundancy component problems caused by inaccurate spectrum segmentation. In this paper, an empirical monocomponent image decomposition (EMID) is proposed for fully recoverable monocomponents. The EMID method empirically decomposes an image into monocomponents based on energy concentration in Fourier support. This method is composed of two main processes: 1) energy concentration-based segmentation and 2) empirical image filter bank construction. In the former process, the base of a mountain-shaped energy concentration that can perfectly represent the monocomponent spectrum boundary is detected and identified. This process provides a more accurate spectrum segmentation which helps prevent serious problems from interference and redundancy components. In the latter process, an empirical image filter bank is constructed in accordance with the actual monocomponent boundaries by means of an ellipse and Gaussian functions and used to decompose an image into monocomponent images with fewer ringing artefacts. The experimental results show that the proposed EMID method achieves a better decomposition than the state-of-the-art methods in terms of the quality of monocomponent images that are evaluated by peak signal-to-noise ratio and structural similarity index. Furthermore, in a real-world dataset, the EMID method is able to clearly detect text regions, thus significantly improving the efficiency of Thai text character localization in natural scene images. |
| 15.45 – 16.05 | Paper 1 | An Unsupervised Feature Selection by Back-Propagated Weighting the non-Gaussianity Score of Independence Components Wachiravit Modecrua, Praisan Padungweang, and Worarat Krathu (King Mongkut’s University of Technology Thonburi) |
| 16.05 – 16.25 | Paper 2 | A Breakup Machine Learning Approach for Breast Cancer Prediction Sabari Vishnu Jayanthan Jaikrishnan, Orawan Chantarakasemchit, and Phayung Meesad (King Mongkut’s University of Technology North Bangkok) |
| 16.25 – 16.45 | Paper 3 | A Modified Binary Flower Pollination Algorithm: A Fast and Effective Combination of Feature Selection Techniques for SNP Classification Wanthanee Rathasamuth and Kitsuchart Pasupa (King Mongkut’s Institute of Technology Ladkrabang) |
| 16.45 – 17.05 | Paper 4 | A Density Discriminant Index for Cluster Validation Supphawarich Thanarattananakin, Praisan Padungweang and Worarat Krathu (King Mongkut’s University of Technology Thonburi) |
| 17.05 – 17.25 | Paper 5 | Study on Machine Learning Techniques with Conventional Tools for Payment Fraud Detection Treepatchara Tasnavijitvong, Harindu Mudunkotuwa Mudunkotuwe Hitiwadi Vidanelage, Panit Suwimonsatein, and Phayung Meesad (King Mongkut’s University of Technology North Bangkok) |
| 17.25 – 17.45 | Paper 6 | Thai Dependency Parsing with Character Embedding Sattaya Singkul and Kuntpong Woraratpanya (King Mongkut’s Institute of Technology Ladkrabang) |
| 17.45 – 18.05 | Paper 7 | Utilizing Gene Co-expression Network for Identifying Subnetwork Biomarkers for Cancer Narumol Doungpan, Jonathan H. Chan, and Asawin Meechai (King Mongkut’s University of Technology Thonburi) |
| 18.05 – 18.15 | JSCI Closing | |