The 2nd International Conference on Intelligent Systems and Data Science (ISDS 2024)
Nha Trang University, 09-10 November, 2024
Call for papers
Follow success of the first ISDS 2023 organized at Can Tho University, the second ISDS 2024 will be organized at Nha Trang University. Objectives of this international conference is to attract domestic and foreign researchers to participate and present outstanding and recent research in the field of ICT. This is an opportunity for scientists to meet, exchange, and cooperate. The ISDS 2024 is also a place for students to report and learn new results in the field of ICT. This ISDS conference looks at state-of-the-art and original research issues (in the topics of intelligent systems and data science).
Topics of the conference relate to (but not limited to):
- Track 1: Intelligent Systems & Recommender Systems
- Track 2: Data Science & Machine Learing
- Track 3: Image Processing & Pattern Recognition
- Track 4: Natural Language Processing
Important dates
- Deadline for submission: 31-07-2024
- Acceptance notification: 26-08-2024
- Deadline for final papers: 16-09-2024
- Conference dates: 09-11-2024 - 10-11-2024
Submission guidelines
All papers must be original and not simultaneously submitted to another journal or conference. Authors are invited to electronically submit full papers in English. The submitted papers must be in PDF in the LNCS/CCIS one-column page format. The length of submitted papers should be from 12-15 pages (for long papers) and 6-8 pages (for short papers). All papers have to be written in the English language.
Authors are invited to submit their papers at the EasyChair web site using the following URL: https://easychair.org/conferences/?conf=isds2024
Publications
All accepted papers will be published in one of the following methods (based on the review results):
- Proceedings: Papers with acceptance rate less than 40% will be published by Springer Verlag in Communications in Computer and Information Science (CCIS - indexed in Scopus).
Registration fee for each paper/author in Proceedings:
+ For Vietnamese authors: 4.000.000 VND
+ For Foreigner authors: 300 USD
- Special issue in journal: Papers with acceptance rate from 40% to 60% will be published by CTU Journal of Innovation and Sustainable Development (CTUJoISD). These papers will be converted to the CTUJoISD template by the authors.
Registration fee for each paper/author in the CTUJoISD:
+ For Vietnamese authors: 1.500.000 VND
+ For Foreigner authors: 150 USD
Moreover, the selected papers, after further revision and extension (at least 30%), will be considered for publication in special issues of the Springer Nature Computer Science (SNCS) journal. SNCS is a broad-based, peer reviewed journal that publishes original research in all the disciplines of computer science including various interdisciplinary aspects. SNCS is indexed and abstracted in Scopus, ACM Digital Library, DBLP, Google Scholar, etc.
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Keynotes
Keynote 1: Data-driven approaches of image analysis for industrial and medical applications
by Prof. Masayuki Fukuzawa, Kyoto Institute of Technology, Japan.
Data-driven approach is a research strategy that explores promising predictor through detailed data-analysis without prior hypotheses, in contrast to classical hypothesis-driven strategy. Although it requires wide variety of data and large amounts of computational resources, it has the advantage of providing novel insights without the constraints of prior hypotheses. It is particularly favorable for complex and exploratory analysis of domain-specific images such as estimation of quality deterioration factors of industrial products (mechanical parts, semiconductor devices, etc.) as well as prognostic prediction in medical image diagnosis (CT, MRI, US, etc.). Recently, it has also been applied to large amounts of open data due to the drastic cost decrease in computational resources with the advances of information and communication technology. This talk aims to share our recent and ongoing studies related to such approaches. The concepts and experimental achievements are presented to demonstrate the potential of this approaches and their future prospects.
Keynote 2: Decoding Biological Language in Membrane Proteins: Combining Protein Language Model Embeddings with Multi-Window Scanning Deep Learning for Functional Identification
by Prof. Yu-Yen Ou, Department of Computer Science and Engineering, Yuan Ze University, Taiwan.
This presentation will explore the integration of protein language pre-training models with multi-window scanning deep learning techniques to decode and analyze the biological language embedded within membrane protein sequences. We will first utilize protein language models such as ProtTrans or ESM-2 to transform protein sequences into high-dimensional vector embeddings, capturing the intricate biological language within these sequences. Following this, we will employ multi-window convolutional neural networks (MCNN) to extract features across various scales, enabling the identification of membrane protein functions based on these language features. This innovative approach, combining language models with multi-scale analysis, not only enhances our understanding of membrane and transporter proteins but also offers new perspectives and potential applications in the field of bioinformatics.
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