International Conference on Data Analytics and Computing
The Department of Mathematics, College of Science and Technology, Wenzhou- Kean University is proud to conduct and organize the International Conference on Data Analytics and Computing (ICDAC) on May 28-29, 2022.
Hybrid Event: Physical and online participation by delegates.
The theme of the International Conference on Data Analytics and Computing (ICDAC) is mainly focused on the following aims:
1. To promote and support the use of Data Science and computing for important potential societal and economic benefits.
2. Collaboration of industry workforce and academia knowledge and expertise.
3. To educate students to meet the growing demand for data scientists.
4. To operate the extended networks, including career opportunities, research funding, awareness of industry trends.
The objective of ICDAC-2022 is to provide a unique forum for discussion of the latest developments in data science and computing. It will offer networking opportunities, providing you a chance to meet and interact with leading researchers as well as the most influential minds in the field of data science and computing. ICDAC also encourage industrial sectors to propose challenging problems where academician can provide insight and new ideas.
ICDAC-2022 will bring together, from a global perspective, scientists, researchers, end-users, industry, policymakers from several countries and professional backgrounds to exchange ideas, advance knowledge and discuss key issues for data science and computing.
ICDAC aims to enforce the interaction between academia and industry, leading to innovation in both fields. During this event, significant advances in data science will be presented, bringing together prominent figures from business, science, and academia to promote the use of data science in industry. ICDAC also encourage industrial sectors to propose challenging problems where academician can provide insight and new ideas.
This conference aims to have young researchers’ papers and poster presentations. Accepted papers that are presented at the conference will be included in the conference proceedings and published in the Springer Book Series or Scopus indexed Journal (Approval Pending).
About Publications:
Lecture Notes on Data Engineering and Communications Technologies (Scopus & EI indexed)
Special Issue on Advances in Operations Research and Machine Learning Focused on Pandemic Dynamics, Operations Research Perspectives, Elsevier
Special Issue on Machine Learning Algorithms under Uncertainty: Real-world Systems, International Journal of Fuzzy Systems, Springer
Special Issue on Internet of Behaviour (IoB) for Industry 4.0, Internet Technology Letters, Wiley
Special Issue on Intelligent Approaches for Smart Water Reusability in Smart Cities, Urban Water Journal, Taylor & Francis
Special Issue on Environmental Toxicology and Bio-Remediation, Human and Ecological Risk Assessment, An International Journal, Taylor & Francis
Special Issue on Remote Sensing for Sustainable Forest Management, Journal of Applied Remote Sensing, SPIE Digital Library
International Journal of Applied Decision Sciences
International Journal of Data Science
Previous Conference: Academia-Industry Consortium for Data Science (AICDS) on December 19-20, 2020.
AICDS Proceedings: https://link.springer.com/book/10.1007/978-981-16-6887-6
We are pleased to announce the information regarding the best presenters at the ICDAC-2022
Paper ID | Presenter | Title |
11 | Hong-Seng Gan | Improving the Performance of Generative Adversarial Network via Normalization Techniques Analysis |
59 | Deepesh Sangwan | Automatic Generation Control Using Optimized TID Controller in Multi-Area System |
13 | Ankita Srivastava | A discrete Firefly-based task scheduling algorithm for cloud infrastructure. |
107 | Neha Srivastava | SoTE: A hybrid algorithm to improve seagull optimization algorithm using thermal exchange optimization |
158 | Vaibhav Garg | Efficient Nets-Based Scalable Regression Model to Predict Pulmonary Fibrosis |
218 | Lining Yu | ProtoSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation |