The Joint Seminar on Computational Intelligence (JSCI) is a biannual 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 publish their works. The event is open to all researchers who want to broaden their knowledge in the field of computational intelligence.
This is the fourth time and the name of the event is changed to the Joint Symposium on Computational Intelligence (JSCI4) and will be co-locating with Deep Learning and Artificial Intelligence Winter School (Feb 1-4, 2018)–supported by Asia Pacific Neural Network Society (APNNS), CIS-Thailand, and IBM. The symposium will feature paper presentations as well as keynote speech by keynote speaker. JSCI4 will be held on February 2, 2018 at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand.
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 an Extended Abstract at [EasyChair]. The paper must conform to the standard of IEEE Manuscript Templates for Conference Proceedings which is available to downloaded at [IEEE]. The Extended Abstract papers are a maximum of TWO pages. The paper will be peer-reviewed.
Important Dates
Submission Deadline: 14 January 2018
Notification of Acceptance: 21 January 2018
Camera Ready Submission: 27 January 2018
Registration: 21 January — 2 February 2018
Symposium Date: 2 February 2018
Keynote Speakers
Organizing Committee
Advisory
Chair
Technical Program Committee
Proceeding of JSCI 4
Final Program
| Time | Details | Remark |
| 13:30–14.45 | An Machine Learning Approach to Identify Natural Rubber Latex Quality, Weng-Kin Lai | Keynote Speaker |
| 15:00–15:15 | Sentiment Analysis of the Burmese Language using the Distributive Representation of n-gram-based Word, Myat Lay Phyu, and Kiyota Hashimoto | [PDF] |
| 15:15–15:30 | Development of Hybrid Deep Learning in Sentence Classification, Thiptanawat Phongwattana, Praisan Padungweang, and Jonathan H. Chan |
[PDF] |
| 15:30–15:45 | Detection of Personal Vehicles Stopping on the Road in a No Parking Area Using Support Vector Machine, Eakbodin Gedkhaw, Manussawee Piyaneeranart, and Mahasak Ketcham | [PDF] |
| 15:45–16:00 | A Comparision of Iteration-Free Bi-Dimensional Mode Decomposition and Empirical Monocomponent Image Decomposition, Donyarut Kakanopas, and Kuntpong Woraratpanya | [PDF] |
| 16:00–16:15 | SNP selection for Porcine breed classification by a hybrid information gain and genetic algorithm, Wanthanee Rathasamuth, Kitsuchart Pasupa, and Sissades Tongsima | [PDF] |
| 16:15–16:30 | Subnetwork Identification based on Dissimilarity Profiles of Gene Co-Expressions, Thanyathorn Thanapattheerakul, Narumol Doungpan, and Jonathan H. Chan | [PDF] |
| 16:30–16:45 | Development of an Automated Biological Tool for Visualizing Dissimilarity within Gene Co-Expression Networks in Hierarchical Clustering, Prissadang Suta, Panissara Thanapol, Jonathan H. Chan, and Thiptanawat Phongwattan | [PDF] |
| 16:45-17:00 | An Adaptive Learning System Based on Proportional VARK to Enhance Learning Achievement Concept, Beesuda Daoruang, Suthida Chaichomchuen, and Anirach Mingkhwan | [PDF] |