The ICIC 2025 Program Committee is inviting
proposals for special sessions to be held during the conference(http://www.ic-icc.cn/2025/index.htm), taking place on July 26-29, 2024, in Ningbo,
China.
Each special session proposal should be well
motivated and should consist of 8 to 12 papers. Each paper must have the title,
authors with e-mails/web sites, and as detailed an abstract as possible. The special
session organizer(s) contact information should also be included. All special session
organizers must obtain firm commitments from their special session presenters and
authors to submit papers in a timely fashion (if the special session is accepted)
and, particularly, present them at the ICIC 2024. Each special session organizer
will be session chair for their own special sessions at ICIC 2024 accordingly. All
planned papers for special sessions will undergo the same review process as the
ones in regular sessions. All accepted papers for special sessions will also be
published by Springer's Lecture Notes in Computer Sciences (LNCS)/ Lecture Notes
in Artificial Intelligence (LNAI)/ Lecture Notes in Bioinformatics (LNBI).
All the authors for each special session must
follow the guidelines in CALL FOR PAPERS to prepare your submitted papers.
Proposals for special sessions should be submitted
in ELECTRONIC FORMAT by http://www.ic-icc.cn/icg/index.php at Special Session.
|
orders |
Title |
Organizers |
Nationality |
|
The 6th International Session on
Theoretical Computational Intelligence and Applications in 2025 |
Wenzheng Bao |
China |
|
|
Advancements in Multimodal Intelligent
Computing |
Yong Su |
China |
|
|
Computational Intelligence Models for
Smart Cities |
Pengjiang Qian Khin-Wee Lai |
China |
|
|
A Contrastive Learning Framework for
Alzheimer's Disease Classification CLFAD |
Peiyuan Li Jiangnan University |
China |
|
|
Test-Time Training: Methods, Theory, and
Applications |
Lingjie Li |
China |
1. The 6th International
Session on Theoretical Computational Intelligence and Applications in 2025
Organizer:
Wenzheng Bao
Xuzhou University of Technology
Email: baowz55555@126.com
Scope and Topics:
Since the birth of artificial intelligence, the theory and technology are
increasingly mature. The application field is also expanding. According to some
laws and mechanisms in the process of natural evolution, and researchers deal
with problems though imitation. That is where theoretical computational
intelligence comes in. Theoretical computational intelligence is the successor
of artificial intelligence. In addition, it turns into one of the most active
researches in the field of intelligent information science. Theoretical
computational intelligence has been successfully used to solve the critical
problems in pattern recognition, data mining, image processing and so on.
Nowadays, there is some representative algorithms in the field such as fuzzy
systems, neural networks, evolutionary computation, group intelligence and
immune system, etc. Recently, theoretical computational intelligence is at
rapid development, in the case of both methodological development and practical
applications. Computational intelligence plays pivotal roles in finding the
stable convergence of the optimal solution or approximate optimal solution
through multiple iterative calculation. Especially in practical applications,
it has been widely implemented by researchers. Computational intelligence is an
essential combination of learning, adaptation and evolution used to intelligent
and innovative applications. Similar to other scientific domains, there is no
doubt that computational intelligence has a great research space both in theory
and in applications. This workshop consists of invited talks and contributed
talks, and welcomes submission of both papers and short abstracts, where all
submissions will be subject to peer review. The topics of interest include but
are not limited to the following: Applications of theoretical Computational
Intelligence in bioinformatics Applications of theoretical Computational
Intelligence in traffics Applications of theoretical Computational Intelligence
in pharmaceutics Applications of theoretical Computational Intelligence in
pharmacology Applications of theoretical Computational Intelligence in
Computational chemistry Applications of theoretical Computational Intelligence
in Microbiomics Applications of theoretical Computational Intelligence in image
processing Applications of theoretical Computational Intelligence in natural
language processing Applications of theoretical Computational Intelligence in
financial Other related topics.
2. Advancements in
Multimodal Intelligent Computing
Organizers:
Yong Su
Tianjin Normal University
Email: suyong@tju.edu.cn
Meng Xing
Ningbo Institute of Digital Twin
Email: xingmeng@tju.edu.cn
Yijun Yang
The Chinese University of Hong Kong
Email: yjyang@cse.cuhk.edu.hk
Long Tian
Southwest Jiaotong University
Email: long.tian@swjtu.edu.cn
Wei Shang
City University of Hong Kong
Email: csweishang@gmail.com
Scope and Topics:
With the rapid development of information technology, multimodal data
processing has become a core research direction in the field of artificial
intelligence. Multimodal data originates from various sensory channels or
devices, including images, text, speech, and sensor data. Each modality
provides a different perspective or understanding of the same phenomenon or
object, contributing to a richer and more comprehensive representation.
Efficiently integrating and processing these heterogeneous datasources to
enhance the perceptual, reasoning, and decision making capabilities of
intelligent systems has become a key challenge across multiple research
domains. This special session aims to comprehensively explore the latest
advancements, technical challenges, and practical applications of multimodal
data processing. Themes of interest： The special session welcomes research
contributions related to the following topics: 1. Cross-modal feature
extraction, alignment, and representation learning 2. Design and optimization
of multimodal data fusion models 3. Applications for healthcare 4. Applications
for intelligent systems 5. Social media and information retrieval 6. Smart
cities and environmental monitoring 7. Evaluation and dataset construction 8.
Autonomous systems and robotics 9.Human-computer interaction and user
experience 10. Security, privacy, and ethical considerations in multimodal
system.
3. Computational Intelligence Models for Smart Cities
Organizers:
Pengjiang Qian
iangnan University, China
Email: qianpjiang@jiangnan.edu.cn
Wenbing Zhao
Cleveland State University, USA
Email: wenbing@ieee.org
Khin-Wee Lai
University of Malaya, Malaysia
Email: lai.khinwee@um.edu.my
Scope and Topics:
Smart city comprehensive adopts the new generation of Internet, big data,
Internet of Things, artificial intelligence, cloud computing and other
information technologies to realize the intelligence of urban construction,
planning, management, and service, forming an innovative and sustainable
intelligent city. It integrates a variety of new generation information
technologies to complete the automatic perception, collection, integration,
analysis and sharing of urban information resources, and realize intelligent
medical care, emergency response, environmental protection, education,
transportation, etc., thus bringing convenience, high-efficiency, intelligence
to people's life and response to their personalized needs. The construction of
smart city involves many aspects, from urban road traffic to urban spatial
layout and management, which require high technical support, as well as a
complete information-based decision-making mechanism to provide a reliable
guarantee for urban development. In recent years, advanced computational
intelligence models such as deep learning, active learning, transfer learning
and information fusion have brought opportunities for smart city. Computational
intelligence models have been successfully applied in many areas of smart city
construction, such as urban traffic flow prediction, health monitoring and
early warning, mobile intelligent question answering system, intelligent
environmental resource deployment, etc. Although the existing computational
intelligence models based on single-view data have achieved certain results,
their practical application performance still cannot meet the needs of smart city
construction. Compared with single-view data, multi-view data can provide more
abundant and comprehensive information for the computational intelligence
models, thereby further improving the performance of the model. Therefore, it
is necessary to study the deep multi-view learning-driven computational
intelligence model to overcome the defects existing in the construction of
smart cities. In this special issue, we hope to build a platform for
researchers and engineers to explore this field and contribute their experience
and wisdom to the development of computational intelligence models for smart
cities. Topics of intended papers contain, but not limited to, Advanced
computational intelligence models for smart city, such as deep learning, sparse
learning, transfer learning, active learning, multi-task learning, Smart city
information management platform involving artificial intelligence, Smart city
information decision-making system based on multi-view data, Prediction models
combined with multi-view features, such as intelligent traffic flow prediction,
intelligent medical disease prediction, intelligent weather prediction, signal
light warning, Visualized human-computer interaction platform for smart city,
Smart city monitoring system driven by deep multi-view learning, Deployment and
management for smart cities with unsupervised methods, such as self-training
models, clustering algorithms, principal component analysis, Data automated
management and analysis for smart city, Screening and fusion of multi-modal
heterogeneous clinical data for smart medical care with supervision methods,
such as random forest, decision tree, naive bayes, Design of smart city
resource allocation system based on big data analysis
4. A Contrastive Learning Framework for Alzheimer's
Disease Classification CLFAD
Organizers:
Peiyuan Li
Jiangnan University
Email: 6223115015@stu.jiangnan.edu.cn
Zhuxin Peng
Jiangnan University
Email: 6233110032@stu.jiangnan.edu.cn
Qianyi Zhan
Jiangnan University
Email: zhanqianyi@jiangnan.edu.cn
Zhenping Xie
Jiangnan University
Email: xiezp@jiangnan.edu.cn
Yuan Liu
Jiangnan University
Email: lyuan1800@jiangnan.edu.cn
Scope and Topics:
Contrastive learning,Alzheimer’s disease, Image Classification, Data
augmentation
5. Test-Time Training: Methods, Theory, and Applications
Organizers:
Xueliang Li
National Engineering Laboratory for Big Data System Computing Technology Shenzhen
University
Email: lixueliang01@gmial.com
Lingjie Li
College of Big Data and Internet, Shenzhen Technology University
Email: lilingjie@sztu.edu.cn
Junkai Ji
National Engineering Laboratory for Big Data System Computing Technology Shenzhen
University
Email: jijunkai@szu.edu.cn
Scope and Topics:
Test-Time Training TTT has emerged as a paradigm to enhance model adaptability
and robustness by allowing continuous learning directly from test data. Unlike
traditional static training, TTT enables models to dynamically update their
parameters or representations during inference, addressing challenges such as
concept drift, domain shifts, and data scarcity. This approach leverages
self-supervised learning, meta-learning, or online optimization to refine
predictions on-the-fly, making it highly relevant for real-world applications
like autonomous systems, healthcare monitoring, and dynamic environments.
Recent advances in TTT have shown promising results in improving
generalization, reducing catastrophic forgetting, and enabling lifelong learning.
However, key challenges remain, including theoretical guarantees for stability,
efficient optimization algorithms for real-time adaptation, and scalable
implementations across heterogeneous platforms. This session aims to foster
discussions on cutting-edge TTT methodologies, their theoretical foundations,
and interdisciplinary applications. More details can be found via http: http://www.ic-icc.cn/2025/index.php.
Topics include but are not restricted to : Theory of Test-Time Training in
Dynamic Environments Optimization Algorithms for Test-Time Training Self-Supervised
Learning for Test-Time Training Transfer Learning for Test-Time Training Meta-Learning
Frameworks for Test-Time Training Benchmarking and Evaluation Metrics for
Test-Time Training Lifelong Learning Systems for Test-Time Training Applications
of Test-Time Training in Healthcare Monitoring Applications of Test-Time
Training in Computer Vision Applications of Test-Time Training in Drug Design Applications
of Test-Time Training in Financial Risk Detection