ICIC 2021 Workshop


The purpose of workshops at ICIC 2021 is to provide a comprehensive forum on topics that will not be fully explored during the main conference and to encourage in-depth discussions of future technical and application issues in Intelligent Computing Community. The workshop will also provide a platform for researchers of Intelligent Computing Community to set an agenda and lay the foundation for future development in Intelligent Computing Community. The number of accepted papers at each Workshop should be not less than 8 ones.

All the authors for each Workshop must follow the guidelines in CALL FOR PAPERS to prepare your submitted papers.

Proposals for Workshop should be submitted in ELECTRONIC FORMAT by http://www.ic-icc.cn/icg/index.asp at Workshop Session.


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Title
Organizers
Nationality
The 1st International Workshop on Evolutionary Computing and Deep Learning for Health Informatics (ECDLHC2021)
Waqas Haider Bangyal, Liang Gao, Jamil Ahmad, Zia ul Qayyum
Pakistan,China
The 1st International Workshop on Mathematical Methods for Analyzing Biological Data
Tatsuya Akutsu, Hongmin Cai, Yushan Qiu
Japan,China
The 1st International Workshop on AI in Biomedicine
Kyungsook Han, Byungkyu Park, De-Shuang Huang
Korea,China
The 1st International Workshop on Theoretical Computational Intelligence and Applications in 2021
Lin Wang, Dong Wang, Bin Yang, Wenzheng Bao
China
The 2nd International Workshop on Recent advances in deep learning methods and techniques for medical image analysis
Yudong Zhang, Chenxi Huang
UK,China
The 1st International Workshop on Advanced Intelligent Modeling Technologies for Smart Cities
Pengjiang Qian, Yuanpeng Zhang
China

1. The 1st International Workshop on Evolutionary Computing and Deep Learning for Health Informatics (ECDLHC2021)

Organizers:
Waqas Haider Bangyal
University of Gujrat, Pakistan
Email:waqas_bangyal@hotmail.com

Liang Gao
Huazhong University of Science and Technology, China
Email: gaoliang@mail.hust.edu.cn

Jamil Ahmad
Hazara University, Pakistan
Email: jamil@ieee.org

Zia ul Qayyum
Allama Iqbal Open University, Pakistan
Email: ziaqayum67@icloud.com

Scope and Topics:
The 1st International Workshop on Evolutionary Computing and Deep Learning for Health Informatics (ECDLHC2021), aims to be a forum where researchers, practitioners and industry representatives have the opportunity to present and discuss ongoing work. Similarly, explains the systems and effective research contribution in health care using evolutionary computing approaches. Evolutionary Computing covers a number of nature-inspired computational methodologies, mainly Artificial Neural Networks (ANNs), Fuzzy Sets, Genetic Algorithms (GAs), Swarm Intelligence, and their hybridizations for addressing the real-world problems. Due to the several reasons of conventional modeling such as complexity, existent of uncertainties, and the stochastic nature of the processes, conventional modeling approach is useless. According to the success of evolutionary computing methods and techniques in health care applications, it can be expected that these methods can also be applied successfully to solve the medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease and presentation of images captured via X-Ray, Ultrasound, MRI, Nuclear Medicine and Visual Imaging technologies. In this context, computational intelligence paradigms are comprising of numerous branches (including Neural Networks, Swarm Intelligence, Expert Systems, Evolutionary Computing, Fuzzy Systems, and Artificial Immune systems) can play a vital role for handling the different aspects of health care. We invite such papers which target the creativity, design, and engineering-related challenges such as new algorithms, systems, methodology, datasets, evaluations, surveys, reproduced results, and negative results. 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:
  • Medical Imaging
  • Medical Signal Processing
  • Medical Text Analysis
  • Clinical Diagnosis and Therapy
  • Data Mining Medical Data and Records
  • Clinical Expert Systems
  • Modeling and Simulation of Medical Processes
  • Genomic-based Clinical Studies
  • Patient-centric Care
  • Health Care Informatics
  • Biomedical Imaging and Image Processing
  • Evolutionary Computing for Recommendation in Healthcare

2. The 1st International Workshop on Mathematical Methods for Analyzing Biological Data

Organizers:
Tatsuya Akutsu
Kyoto University, Japan
Email:takutsu@kuicr.kyoto-u.ac.jp

Hongmin Cai
South China University of Technology, China
Email: hmcai@scut.edu.cn

Yushan Qiu
Shenzhen University, China
Email: yushan.qiu@szu.edu.cn

Scope and Topics:
Analysis of biological data has become one of major application areas of intelligent computing. For example, various mathematical models, statistical methods, and machine learning methods have been developed and successfully applied for analysis of DNA/RNA/protein sequences and structures, gene expression profiles, and biological pathways. The First International Workshop on Mathematical Methods for Analyzing Biological Data (MMABD) aims to provide a forum for researchers, engineers, and practitioners to present and discuss their ongoing work on mathematical methods for analyzing biological data. This workshop covers wide ranges of mathematical aspects of Bioinformatics, Computational Biology, and Systems Biology. 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:
  • Mathematical models of biological networks
  • Inference of biological networks
  • Pattern discovery from biological sequence and/or structure data
  • Classification of tumor cells using gene expression data
  • Analysis of biomedical image data
  • Application of cutting-edge machine learning techniques to analysis of biological data.

3. The 1st International Workshop on AI in Biomedicine

Organizers:
Kyungsook Han
Inha University, South Korea
Email:khan@inha.ac.kr

Byungkyu Park
Inha University, South Korea
Email: bpark760914@gmail.com

De-Shuang Huang
Tongji University, China
Email: dshuang@tongji.edu.cn

Scope and Topics:
Data in biomedicine is increasing at an exponential rate, and dealing with big data for analysis or prediction is quite challenging. Artificial intelligence (AI) methods have been used for years to solve several problems in bioscience research, often with multidisciplinary collaboration. This workshop encourages anyone who is interested in AI and biomedicine to submit their original work regarding the development of theory, methods, systems, and application of AI approaches. 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:
  • Bio Big Data Analysis
  • AI methods in Biomedicine
  • Machine Learning
  • Knowledge Discovery and Data Mining in Biomedicine
  • Cancer Genomics and Informatics
  • Precision Medicine with AI
  • Biomedical Ontologies
  • Biomedical Knowledge Acquisition and Knowledge Management

4. The 1stInternational Workshop on Theoretical Computational Intelligence and Applications in 2021

Organizers:
Lin Wang
University of Jinan, China
Email:ise_wanglin@ujn.edu.cn

Dong Wang
University of Jinan, China
Email: ise_wangd@ujn.edu.cn

Bin Yang
Zaozhuang University, China
Email: batsi@126.com

Wenzheng Bao
Xuzhou University of Technology, China
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 image processing
  • Applications of theoretical Computational Intelligence in natural language processing
  • Applications of theoretical Computational Intelligence in financial Other related topics
  • Applications of theoretical Computational Intelligence in financial Other related topics

5. The 2nd International Workshop on Recent advances in deep learning methods and techniques for medical image analysis

Organizers:
Yudong Zhang
University of Leicester, China
Email:yudongzhang@ieee.org

Chenxi Huang
Xiamen University, China
Email: supermonkeyxi@xmu.edu.cn

Scope and Topics:
With advancement in biomedical imaging, the amount of data generated are increasing in biomedical engineering. For example, data can be generated by multimodality image techniques, e.g. ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. This poses a great challenge on how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling in clinical applications and in understanding the underlying biological process. Deep learning is a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available. The focus of this workshop is to carry out the research article which could be more focused on to the latest medical image analysis techniques based on Deep learning. In recent years Deep Learning method and its variants has been widely used by researchers. This Issue intends to bring new DL algorithm with some Innovative Ideas and find out the core problems in medical image analysis. Recommended topics include (but are not limited to) the following:

  • Application of deep learning in biomedical engineering
  • Transfer learning and multi-task learning
  • Joint semantic segmentation, object detection and scene recognition on biomedical images
  • Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
  • New model of new structure of convolutional neural network
  • Visualization and explainable deep neural network

6. The 1st International Workshop on Advanced Intelligent Modeling Technologies for Smart Cities

Organizers:
Pengjiang Qian
Jiangnan University, China
Email:qianpjiang@jiangnan.edu.cn

Yuanpeng Zhang
Hong Kong Polytechnic University, HongKong
Email: y.p.zhang@polyu.edu.hk

Scope and Topics:
Smart cities constitute a new generation of information technology, considering Internet of Things (IoT), cloud computing, big data, and artificial intelligence (AI), which are fully used in all fields of life in the city. Based on comprehensive and thorough perception, wide-band ubiquitous interconnection, and intelligent integration of advanced forms of urban informationization, the deep integration of informationization, industrialisation, and urbanisation can be achieved. This can then alleviate the "big city disease" by improving the quality of urbanisation, realising fine and dynamic management, improving the effectiveness of urban management, and improving the quality of life of citizens. However, in the process of promoting smart cities, there are many challenges and opportunities to be solved urgently. In particular, advanced intelligent technologies, such as transfer learning, deep learning and multi-view learning, have found numerous successful applications in diverse fields, including infrastructure deployment and control, garbage sorting and recycling, traffic congestion prediction and dredging, smart medical and health monitoring, emergency disaster and first aid, smart city communication, and smart power transmission. 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. Potential topics include but are not limited to the following:

  • Data management, data storage, data transmission, data integration for smart cities
  • Real-time capability and optimization under smart cities
  • Clouds computing for smart cities
  • Industrial IoT platforms under smart cities
  • Learning methods for user interaction prediction in smart cities
  • Deep model interpretability for smart cities
  • Virtualization for smart cities

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