International Conference On Intelligent Computing
Wuhan,China August 15-18, 2018

ICIC 2018 Special Session

2018 International Conference on Intelligent Computing
August 15-18 ,2018
Wuhan,China
(http://ic-ic.tongji.edu.cn/2018/index.htm)

The ICIC 2018 Program Committee is inviting proposals for special sessions to be held during the conference (http://ic-ic.tongji.edu.cn/2018/index.htm), taking place on August 15-18 2018, in Wuhan,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 2018. Each special session organizer will be session chair for their own special sessions at ICIC 2018 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 to Special Session Chair:

Ling Wang,
Tsinghua University, China
wangling@tsinghua.edu.cn


Tentative 18 Special Session Proposals List

orders
Title
Organizers
Nationality
Evolutionary Optimization for Scheduling
Bo Liu,Bin Qian and Ling Wang
China
Heuristic Optimization Algorithms for Real-world Applications
Wenyin Gong,Ling Wang and Rui Wang
China
Evolutionary Multi-Objective Optimization and Its Applications
Rui Wang,Ling Wang,Wenyin Gong and Lining Xing
China
Swarm Evolutionary algorithms for Scheduling and Combinatorial Optimization
Junqing Li,Hongyan Sang,Kaizhou Gao and Yu-Yan Han
China
Swarm Intelligence and Applications in Combinatorial Optimization
Gai-Ge Wang,Ling Wang,Yongquan Zhou and Lining Xing
China
Advances in Metaheuristic Optimization Algorithm
Yongquan Zhou,Mohamed Abdel-Basset and Aijia Ouyang
China
Advances in Image Processing and Pattern Recognition Techniques
Vandana Dixit Kaushik and Phalguni Gupta
INDIA
Protein and Gene Bioinformatics: Analysis, Algorithms and Applications
Michael Gromiha and Y-h. Taguchi
INDIA
AI in Biomedicine
Kyungsook Han
South Korea
High-throughput Biomedical Data Integration and Mining
Chun-Hou Zheng,Junfeng Xia and Jin-Xing Liu
China
Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology
Dong Wang,Lin Wang,Shi-yuan Han and De-shuai Lou
China
Exploring Complex Diseases with Next Generation Sequencing Data for Personalized Medicine
Jiawei Luo,Shulin Wang and Jianwen Fang
China
Machine Learning Algorithms and Applications
Yonggang Lu,Li Liu and Yi Yang
China
IoT and Smart Data
Abir Hussain and Dhiya Al-Jumeily
UK
Intelligent Systems and Applications for Bioengineering
Vitoantonio Bevilacqua
Italy
Evolutionary Optimization: Foundations and Its applications to Intelligent Data Analytics
Peng Yang and Ke Tang
China
Color Image Processing
Farid Garcia Lamont,Jair Cervantes Canales and Asdrúbal Lopez Chau
Mexico
Learning from Imbalanced Data
Jair Cervantes Canales,Farid Garcia Lamont and Asdrúbal Lopez Chau
Mexico

1. Special Session on Evolutionary Optimization for Scheduling (Bo Liu, et. al, China)

Organizers:
Bo Liu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Email:bliu@amss.ac.cn

Bin Qian
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Email: bin.qian@vip.163.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn

Scope:
This special session invites papers discussing recent advances in the development and application of Evolutionary Optimization for Scheduling. Scheduling where a given set of jobs must be processed subject to various resource constraints, is a subject of substantial and well-developed research issue in communities of operations research and evolutionary optimization. The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both academician and practitioners working on Evolutionary Optimization for Scheduling to discuss new and existing issues in this area. We encourage papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Novel evolutionary optimization approaches for scheduling
  • Many-objective optimization for scheduling
  • Hybrid approaches for scheduling
  • Interactive evolutionary scheduling optimization
  • Real-world scheduling applications based on evolutionary optimization

2. Special Session on Heuristic Optimization Algorithms for Real-world Applications (Wenyin Gong, et. al, China)

Organizers:
Wenyin Gong
School of Computer Science, China University of Geosciences, Wuhan, 430074, China
Email:wygong@cug.edu.cn

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn

Rui Wang
College of Systems Engineering, National University of Defense Technology, China
Email:ruiwangnudt@gmail.com

Scope:
Many real-world optimization problems are complex and very difficult to solve and they present a great challenge for the research communities. This is due to large and heavily constrained search spaces which make their modelling (let alone solving) a very complex task. Although they have received a significant amount of attention from various research communities, there is still a quest for an efficient and effective algorithm that is able to produce very good results within acceptable amount of time and can adapt itself to the solution landscape changes. Heuristic optimization algorithms, such as differential evolution, particle swarm optimization, ant colony optimization, and artificial bee colony, have proven to be effective solution methods for various optimization problems. The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both academician and practitioners on heuristic optimization algorithms to develop an efficient and effective algorithm that is able to support the decision maker and can produce very good results for the real-word problems in diverse fields. We encourage papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Inversion in Geophysics
  • Energy systems
  • Computational fluid dynamics
  • Reservoir optimization in oil-fields
  • Weather prediction
  • Evolutionary robotics
  • Online control
  • Structural optimization
  • Medical imaging
  • Space applications
  • Molecular dynamics
  • Financial markets
  • Engineering design
  • Rapid prototyping
  • Manufacturing sciences
  • Drug design and pharmaceuticals
  • Vehicle routing
  • Micro electro-mechanical systems

3. Special Session on Evolutionary Multi-Objective Optimization and Its Applications (Rui Wang, et. al, China)

Organizers:
Rui Wang
College of Systems Engineering, National University of Defense Technology, China
Email:ruiwangnudt@gmail.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email:wangling@mail.tsinghua.edu.cn

Wenyin Gong
School of Computer Science, China University of Geosciences, Wuhan, 430074, China
Email:wygong@cug.edu.cn

Lining Xing
College of Systems Engineering, National University of Defense Technology, China
Email:xingliningnudt@gmail.com

Scope:
This special session invites papers discussing recent advances in the development and application of biologically-inspired multi-objective optimization algorithms. Many problems from science and industry have several (and normally conflicting) objectives that have to be optimized at the same time. Such problems are called multi-objective optimization problems and have been subject of research in the past two decades. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto-optimal solutions) in a single run rather than requiring an iterative one-solution-at-a-time process as followed in traditional mathematical programming techniques.
The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both experts and new-comers working on Evolutionary Multi-objective Optimization (EMO) to discuss new and existing issues in this area.
We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Many-objective optimization
  • Theoretical aspects of EMO algorithms
  • Real-world applications of EMO algorithms
  • Test and benchmark problems for EMO algorithms
  • New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc.
  • Multi-objectivization and visualization techniques
  • Handling practicalities, such as uncertainty, noise, constraints, dynamically changing problems, bi-level problems, mixed-integer problems, computationally expensive problems, fixed budget of evaluations, etc.
  • Performance measures for EMO algorithms
  • Techniques to keep diversity in the population
  • Comparative studies of EMO algorithms
  • Memetic and metaheuristics based EMO algorithms
  • Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
  • Parallel EMO approaches
  • Adaptation, learning, and anticipation
  • Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning
  • Interactive Multi-objective Optimization

4. Special Session on Swarm Evolutionary algorithms for Scheduling and Combinatorial Optimization (Junqing Li, et. al, China)

Organizers:
Junqing Li
School of information science and engineering, Shandong Normal University, China
Email: lijunqing.cn@gmail.com

Hongyan Sang
School of Computer Science, Liaocheng University, China
Email: sanghongyan@lcu-cs.com

Kaizhou Gao
Nanyang Technological University
Email: kgao001@e.ntu.edu.sg

Yu-Yan Han
School of Computer Science, Liaocheng University, China
Email: hanyuyan@lcu-cs.com

Scope:
Swarm evolutionary algorithms and their applications in Scheduling and Combinatorial Optimization are active research areas in Artificial Intelligence, automation and Operations Research due to its applicability and interesting computational aspects. Evolutionary algorithms are suitable for scheduling and combinatorial optimization problems since they are highly flexible in terms of handling constraints, dynamic changes and multiple conflicting objectives.

This special session invites papers discussing recent advances in the development and application of biologically-inspired swarm optimization algorithms focus on solving scheduling and combinatorial optimization problems.

The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together both experts and new-comers working on Swarm evolutionary algorithms for scheduling and combinatorial optimization to discuss new and existing issues in this area

This special session focuses on both theoretical and practical aspects of Swarm evolutionary algorithms and their applications in Scheduling and Combinatorial Optimization. Examples of swarm evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimization, particle swarm optimization, differential evolution, artificial bee colony, harmony search, chemical reaction optimization, teaching-and-learning-based optimization, pigeon-inspired optimization, water cycle algorithm, Jaya, evolutionary based hyper-heuristics, memetic algorithms and so on.

  • Production scheduling
  • Vehicle routing
  • Transport scheduling
  • Grid/cloud scheduling
  • Project scheduling
  • Space allocation
  • task assignment
  • traffic light scheduling
  • Multi-objective scheduling
  • Multiple interdependent decisions
  • Automated heuristic design
  • Building optimization
  • Energy saving optimization
  • Big data based scheduling optimization
  • Scheduling optimization in Cloud computing environment
  • Supply chain optimization
  • Parallel optimization algorithms
  • Knowledge-based evolutionary algorithms
  • Theoretical aspects of swarm evolutionary algorithms
  • New real-world and innovative applications

5. Special Session on Swarm Intelligence and Applications in Combinatorial Optimization (Gai-Ge Wang, et. al, China)

Organizers:
Gai-Ge Wang
Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China
Email: gaigewang@gmail.com

Ling Wang
Department of Automation, Tsinghua University, Beijing, China
Email: wangling@mail.tsinghua.edu.cn

Yongquan Zhou
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
Email: yongquanzhou@126.com

Lining Xing
College of Systems Engineering, National University of Defense Technology, China
Email: xinglining@gmail.com

Scope:
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. In SI, an individual has a simple structure and its function is single. However, such systems composed by many individuals show the phenomenon of emergence and can address several difficult real world problems that are impossible to be solved by only an individual. During the recent decades, SI methods have been successfully applied to cope with complex and time-consuming problems that are hard to be solved by traditional mathematical methods. Therefore, SI is indeed a topic of interest amongst researchers in various fields of science and engineering. Some popular SI paradigms, including ant colony optimization, and particle swarm optimization, have been successfully applied to handle various practical engineering problems.

Combinatorial optimization is a subset of mathematical optimization that is related to operational research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Many real world problems can be modeled and solved as combinatorial optimization problems. This is an active research area, where new formulations, algorithms, practical applications, and theoretical results are often proposed and published. Current challenges in the field involve modeling of hard problems, development of exact methods, design and experimental evaluation of approximate and hybrid methods, among others.

We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to combinatorial problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following

  • Swarm Intelligence Algorithms
  • Improvements of traditional SI methods (e.g., ant colony optimization and particle swarm optimization)
  • Recent development of SI methods (e.g., monarch butterfly optimization, earthworm optimization algorithm, elephant herding optimization, moth search algorithm, bird swarm algorithm, chicken swarm optimization, fireworks algorithm, and brain storm optimization)
  • Theoretical study on SI algorithms using various techniques (e.g., Markov chain, dynamic system, complex system/networks, and Martingale)
  • Applications in Combinatorial Optimization
  • Scheduling (e.g., vehicle rescheduling, nurse scheduling problem, flow shop scheduling, and fuzzy scheduling)
  • Traveling salesman problem (e.g., symmetric traveling salesman problem, asymmetric traveling salesman problem, fuzzy traveling salesman problem, and other real world problems that can be converted to traveling salesman problem)
  • Knapsack problem (e.g., 0/1 knapsack problem, multi-objective knapsack problem, multi-dimensional knapsack problem, multiple knapsack problem, and quadratic knapsack problem)
  • Others (e.g., constraint satisfaction problem, set cover problem, task assignment problem, and portfolio optimization)

6. Special Session on Advances in Metaheuristic Optimization Algorithm (Yongquan Zhou, et. al, China)

Organizers:
Yongquan Zhou, Professor, Ph.D
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006,China
Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China
Email:yongquanzhou@126.com

Mohamed Abdel-Basset, Professor, Ph.D
Faculty of computers and informatics, Zagazig university, head of department of operations research, Egypt
Email: analyst_mohamed@yahoo.com

Aijia Ouyang, Professor, Ph.D
College of Computer and Information, Zunyi Normal University, Zunyi, China
Email:ouyangaijia@163.com

Scope:
Metaheuristic Optimization Algorithm is the collective behavior of decentralized, self-organized systems, natural or artificial. Metaheuristic Optimization Algorithm can be used in a number of applications. This special session will highlight the latest development in this rapidly growing research area of new swarm intelligence algorithm, such as, Glowworm Swarm Optimization (GSO), Bat Algorithm (BA), Cuckoo Algorithm (CA), Grey Wolf Optimization Algorithm (GWOA),, Moth Swarm Algorithm (MSA), Dragonfly Algorithm(DA) et. al and its applications. Authors are invited to submit heir original work in the areas including (but not limited to) the following:

  • New metaheuristic optimization algorithms convergence analysis and parameter choice method
  • Two-stage metaheuristic optimization algorithms with applications
  • Hybrid metaheuristic optimization algorithms with applications
  • Hyper metaheuristic optimization algorithms with applications
  • Various improved version metaheuristic optimization algorithms with applications

7. Special Session on Advances in Image Processing and Pattern Recognition Techniques(Vandana Dixit Kaushik, et. al, INDIA)

Organizers:
Dr.Vandana Dixit Kaushik
Department of Computer Science & Engineering,
Harcourt Butler Technological University,
Kanpur 208016, INDIA
Email:vandanadixitk@yahoo.com

Dr.Phalguni Gupta
National Institute of Technical Teachers’ Training & Research, Kolkata
Kolkata 700 106, INDIA
Email: phalgunigupta@nitttrkol.ac.in

Scope:
This session will consider algorithms in the field of pattern recognition, image recognition, image analysis, understanding, and processing. There exist several problems in this domain of work. For example, biometrics who is a typical pattern recognition problem, is used as a form of identification and access control. It extracts physiological and behavioral characteristics of an individual and used as a descriptor and identifier of an individual. The collection of biometric data raises privacy concerns about the ultimate use of this information. ?With the increase in the size and complexity of biometric data the problem becomes much more acute. It still suffers from various challenges which need careful attention before the mass scale deployment. The public acceptance of biometrics is greatly dependent upon its ease of use, social status, performance, and feasibility of spoofing. Security of biometric data in transit and during storage is a challenging problem.

Further, the use of data hiding techniques, such as steganography and digital watermarking, to secure biometrics data is still in its infancy but promising. Digital forensics is a branch of forensic science related to recovery and investigation of all digital devices capable of storing data. It can be used to fix accountability and support hypothesis before law. It can be a step further towards establishing trust among people.

The topics of interest include but are not limited to--

  • Image enhancement, restoration, denoising, deblurring and segmentation.
  • Image feature management, representation, extraction, invariants and moments.
  • Image representation and Waveform analysis.
  • Biometrics
  • Pattern Recognition
  • Indexing and retrieval of large data
  • Multimodal fusion approaches
  • Anti-spoofing issues in real time images
  • Medical Image Processing

8. Special Sessionon on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications(Michael Gromiha, et. al, INDIA)

Organizers:
Michael Gromiha
Head, Protein Bioinformatics Lab, Department of Biotechnology,
Indian Institute of Technology Madras, Chennai 600 036, India
Email:gromiha@iitm.ac.in

Y-h. Taguchi
Professor, Department of Physics, Chuo University, Tokyo 112-8551,Japan
Email: tag@granular.com

Scope:
The advanced developments in Biotechnology provide a wealth of data on genomes, proteomes, metabolomes and transcriptomes. This has been evidenced with the growth of data in gene expression profiles, amino acid sequences, protein three-dimensional structures and protein-protein interaction networks. The availability of data pave way to several analyses in biological and medical research, such as high-throughput protein structure prediction, genome-wide protein-protein interaction prediction, binding sites and interface structures in protein complexes, identification of post-transcription modification sites, single nucleotide polymorphism (SNP) prediction, gene expression profile data analysis and so on. The comprehensive analysis, development of efficient algorithms, software and tools for data integration and visualization are necessary in these cutting-edge research fields.

This special session provides a forum for researchers to present and discuss their latest research results to timely identify and address related problems and challenges. We invite the submission of high-quality, original and unpublished papers in this area. Computational methods for protein and gene bioinformatics includes but are not limited to:

  • Protein structure analysis, folding and stability
  • Secondary and tertiary structure prediction of globular and membrane proteins
  • Analysis and prediction of protein-protein, protein-nucleic acid and protein-ligand interactions including contact sites, hotspots and interface
  • Modeling and Analysis on protein interaction network
  • Gene regulatory network modeling
  • Disease related single nucleotide polymorphism identification
  • Disease related cell signaling pathway identification
  • Gene expression profile data analysis
  • Next Generation Sequence (NGS) analysis
  • Deep learning
All accepted papers will be published in Lecture Notes in Bioinformatics (LNBI)/ Lecture Notes in Computer Sciences (LNCS)/ Lecture Notes in Artificial Intelligence (LNAI) and few high quality papers will be invited for publication in a special issue of IEEE Transactions in Computational Biology and Bioinformatics/Neurocomputing/.

9. AI in Biomedicine(Kyungsook Han, et. al, South Korea)

Organizers:
Kyungsook Han
Department of Computer Engineer, Inha University, Incheon, South Korea
Email:khan@inha.ac.kr

Scope:
Data in biomedicine is increasing at an exponential rate, and dealing with big data for anaysis 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 special session 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.

The topic of the session includes the following areas (but not limited to):

  • Big Data Analysis
  • AI methods in Biomedicine
  • Machine Learning, Knowledge Discovery and Data Mining in Biomedicine
  • Case-based Reasoning in Biomedicine
  • Biomedical Ontologies
  • Biomedical Knowledge Acquisition and Knowledge Management
  • Biomedical Imaging and Signal Processing

10. Special Session on High-throughput Biomedical Data Integration and Mining (Chun-Hou Zheng, et. al, China)

Organizers:
Chun-Hou Zheng, Professor, Ph.D
School of Computer Science and Technology, Anhui University, China
Email:zhengch99@126.com

Junfeng Xia, Professor, Ph.D
School of Computer Science and Technology, Anhui University, China
Email: jfxia@ahu.edu.cn

Jin-Xing Liu,Professor,Ph.D
School of Information Science and Engineering, Qufu Normal University, China
Email:sdcavell@126.com

Scope:
With the advent of high throughput technologies, especially next generation sequencing, an increasing number of data are generated in biomedical research. These big biomedical data encourage researchers to develop novel data integration and mining methodologies. Tools and techniques for analyzing big biomedical data enable a transformation of basic biomedical research to clinical applications. This session provides a forum for researchers to present and discuss the latest research results, to summarize recent advances, to evaluate existing algorithms and methods, and to timely identify and address emerging problems and challenges with regard to biomedical data integration and mining in the era of big data. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:

  • Disease gene network/pathway analysis
  • MiRNA-Disease association prediction
  • Next generation sequencing data analysis, applications, and tools
  • Drug discovery, design, and repurposing
  • Computational modeling and data integration
  • AI and machine learning methods in bioinformatics
  • Applications of systems biology approaches to biomedical studies
  • Integrative analysis of omics data
  • Medical informatics and translational bioinformatics
  • Big data science including storage, analysis, modeling and visualization

11. Special Session on Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology(Dong Wang, et. al, China)

Organizers:
Prof. Dong Wang
Department of Information science and Engineering, University of Jinan, Jinan, PR China
Email:ise_wangd@ujn.edu.cn

Prof. Lin Wang
Department of Information science and Engineering, University of Jinan, Jinan, PR China
Email: ise_wanglin@ujn.edu.cn

Prof. Shi-yuan Han
Department of Information science and Engineering, University of Jinan, Jinan, PR China
Email:ise_hansy@ujn.edu.cn

Dr. De-shuai Lou
Chongqing University of Eduncation, Chongqing, PR China
Email:prosci@126.com

Scope:
Bioinformatics and Computational Biology deal with a wide range of issues and applications which, in recent years, have been successfully solved by Computational Intelligence approaches. However, with the rapid biological technology development, more huge amounts of data concerning biological organisms are gathered and collected. Meanwhile, the problems in Bioinformatics, Computational Biology and Systems Biology become more complicated. Recent applications of Hybrid Computational Intelligence in this area suggest that they are well-suited to this area of research. This special session will highlight applications of Hybrid Computational Intelligence to a broad range of topics. Particular interest will be directed towards novel applications of Hybrid Computational Intelligence approaches to problems in such field. The scope of this special session includes the theory and application of evolutionary computation, neural computation, neural/neurons network, fuzzy systems, artificial immune systems, and other CI methods or hybridizations during solving biological problems.

Authors are encouraged to submit their original and unpublished work in the areas including, but not limited to:

  • Biomedical Model Parameterization
  • Development of Synthetic Biological Devices
  • Drug Structure and Function Analysis, Drug Design
  • Emergent Properties in Complex Biological Systems
  • Gene Expression Array Analysis
  • High-Throughput Data Analysis
  • High Efficiency Algorithms for Solving Biological Problems
  • Mining of Biomedical Data
  • Modelling, Simulation and Optimization of Biological Systems
  • Molecular Dynamics and Molecular Docking
  • Approaches for Optimizing Neural Networks Based on Evolutionary Computation Algorithms
  • Neural Modeling Fields
  • Evolutionary Computation in Neural Networks
  • Theoretical Analysis of Neural Networks
  • Bioinformatics and Complex Networks
  • Applied Hybrid Computational Intelligence on Life Sciences
  • Applied Hybrid Computational Intelligence on Engineering
  • Applications, design, and theory of neural networks and related real systems for robotics
  • Knowledge incorporation in Evolutionary Computation and/or Swarm Intelligence
  • Deep learning algorithms that efficiently handle large-scale data
  • Modelling and analysis of real spiking neurons and neuron networks
  • Others

12. Special Session on Exploring Complex Diseases with Next Generation Sequencing Data for Personalized Medicine (Jiawei Luo, et. al, China)

Organizers:
Jiawei Luo, Professor, Ph.D
College of Computer Science and Electronics Engineering, Hunan University, China
Email:luojiawei@hnu.edu.cn

Shulin Wang, Professor, Ph.D
College of Computer Science and Electronics Engineering, Hunan University, China
Email:smartforesting@163.com

JianwenFang,Ph.D
Division of Cancer Treatment and Diagnosis, National Cancer Institute,USA
Email:jianwen.fang@nih.gov

Scope:
The accumulation of next generation sequencing (NGS) data brings great challenges for analyzing and mining these data to explore complex diseases (such as cancer, diabetes and schizophrenia, etc.). Novel data mining methods, therefore, are emergently explored to solve the integration of various NGS data to discover the potential laws hidden in these NGS data, which are of great benefit to personalized medicine as well as drug discovery and development. We invite the submission of high-quality, original and unpublished papers in this area and the extended and revised versions of the selected papers will be considered to be published in relevant Journals. Topics for this session include, but are not limited to:

  • Identifying cancer-related driver genes and cancer progression
  • Integrative analysis of various next generation sequencing data
  • Cancer-related driver mutation/gene/pathway recognition and analysis
  • Identifying miRNA-mRNA regulatory modules based on next generation sequencing data
  • Gene expression and regulation pattern discovery and classification
  • Network Biology/Medicine and Pathway/Regulation Analysis for cancer
  • Drug repositioning and computer-aided drug design by integrating sequencing information
  • Disease biomarker discovery and pharmacogenomics
  • Personalized medicine and treatment optimization
  • High-performance bio-computing and big data analytics in biology and medicine

13. Special Session on Machine Learning Algorithms and Applications (Yonggang Lu, et. al, China)

Organizers:
Yonggang Lu
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Email:ylu@lzu.edu.cn

Li Liu
School of Software Engineering, Chongqing University, Chongqing 400044, China
Email:dcsliuli@cqu.edu.cn

Yi Yang
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Email:yy@lzu.edu.cn

Scope:
With the development of new algorithms, machine learning has been successfully applied on many applications using various data, including numerical data, text data, transaction data, structured data (graph), sensor data, biometric data, etc. The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together researchers in both theory and application fields of the machine learning research. We encourage papers that are unpublished original work for this special session at ICIC 2018. Topics of interest include, but are not limited to:

  • Deep learning
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Learning Theory
  • Natural Language Processing
  • Learning for Big Data
  • Human Activity Recognition
  • Bioinformatics
  • Computer Vision
  • Intelligent Information Systems
  • Social Networks

14. Special Session on IoT and Smart Data (Abir Hussain, et. al, UK)

Organizers:
Dr. Abir Hussain
Computer Science, Liverpool John Moores University, Liverpool, UK
Email:a.hussain@ljmu.ac.uk

Prof. Dhiya Al-Jumeily
Computer Science, Liverpool John Moores University, Liverpool, UK
Email:d.aljumeily@ljmu.ac.uk

Prof. Hissam Tawfik
Computer Science, Leeds Beckett University, Leeds, UK
Email:H.Tawfik@leedsbeckett.ac.uk

Scope:
Internet of Things (IoT) are providing academic and industry the power to monitor, control and understand vast amount of data such as financial, weather, social life, security, health, emergencies, engineering and so on. Despite the power of gathering these big data, there will be also challenges to provide an effective and scalable support for the computation, data storage, analysis and use of the data that will be created by the utilisation of the IoT and machine. The improvements in the area of IoT will require challenging and new methods to transform the gathered Big Data into Smart Data using machine learning and data science approaches.

In this context, smart data aims to remove the noise and compact the data into the valuable, effectively and meaningful. A large number of computational intelligent technologies such as artificial neural networks, evolutionary computation and fuzzy logic have been developed to extracted the smart data from the IoT platform and transform the big data into a meaningful information.

The aim of this special session is the use of Smart Data for Internet of Things. We welcome papers from authors providing new methodologies and research directions that have not been published previously.

Topics of this special session will include, but are not limited to:

  • Applications of machine learning in Smart Data for IoT
  • Deep learning algorithms, design and systems for Big Data using IoT
  • Security and privacy issues in Big Data and Smart Data for IoT
  • Intelligent decision-making systems for Big Data and Smart Data for IoT
  • Predicting and classification of big data for IoT platform

15. Special Session on Intelligent Systems and Applications for Bioengineering (Vitoantonio Bevilacqua, et. al, Italy)

Organizers:
Vitoantonio Bevilacqua, Associate Professor, Ph.D. Eng.
Head of Industrial Informatics Lab
Department of Electrical and Information Engineering - Polytechnic University of Bari, Italy
Email:vitoantonio.bevilacqua@poliba.it

Scope:
This special session invites papers discussing recent advances in the development and application of intelligent systems for Bioengineering. The bioengineering sector includes the use of methods and algorithms deriving from the traditional engineering domains (for example, mechanics, electronics and computer science) in the medical field, with the aim of improving health outcomes, such as diagnostic accuracy and sensitivity, personalized therapeutic efficacy, good prognosis, and assisted rehabilitation.

The main aim of this special session organized at the 2018 International Conference on Intelligent Computing (ICIC 2018) is to bring together the researchers from the different domains working on the different aspects of the bioengineering, discussing the recent advances and the existing issues in this area.

We strongly encourage the submission of papers describing innovative algorithms, frameworks and platforms highlighting their potentialities in real-world healthcare applications.

You are invited to submit papers that are unpublished original work for this special session at ICIC 2018. The topics are, but not limited to, the following:

  • Biomedical Image Analysis
  • Signal Processing and Analysis
  • Machine Learning Algorithms
  • Deep Learning
  • Virtual and Augmented Reality
  • Serious Game
  • Speech Analysis and Understanding
  • Bioinformatics
  • Medical Informatics
  • Optimization and Personalization at hospitals

16. Special Session on Evolutionary Optimization: Foundations and Its applications to Intelligent Data Analytics (Peng Yang, et. al, China)

Organizers:
Peng Yang
Department of Computer Science and Engineering, Department of Computer Science and Engineering, Southern University of Science and Technology
Email:yangp@sustc.edu.cn

Ke Tang
Department of Computer Science and Engineering, Southern University of Science and Technology
Email:tangk3@sustc.edu.cn

Scope:
Sophisticated optimization problems lay in many data analytics tasks. Although these problems could be smartly relaxed such that mathematical programming methods would be applicable, such relaxations often shift the problem and loses some important properties (e.g., convex loss functions may sensitive to data noise). Evolutionary optimization provides a set of direct search tools that make it possible to solve non-convex optimization problems for data analytics. This special session intends to bring together researchers to report their latest progress and exchange experience in using evolutionary optimization methods to solve either fundamental problems related to data analytics or data analytics tasks arise directly from real-world applications. The topics cover a broad range of data analytics tasks including (but not limited to):

  • Classification;
  • Learning to rank;
  • Prediction;
  • Dimensionality Reduction;
  • Feature Engineering;
  • Network Analysis
  • Real-world applications.

17. Special Session on Color Image Processing (Farid Garcia Lamont, et. al, Mexico)

Organizers:
Farid Garcia Lamont
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Texcoco)
Email:fgarcial@uaemex.mx

Jair Cervantes Canales
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Texcoco)
Email:jcervantesc@uaemex.mx>

Asdrúbal Lopez Chau
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Zumpango)
Email:asdrubalchau@gmail.com

Scope:
Color image processing is a topic, relatively, studied not so exhaustively, because most of the techniques developed for image processing are designed for gray scale images. Usually, color images are processed using extended versions of techniques developed for gray scale images. Not always such techniques success because these methods do not consider the chromaticity feature of colors. The colors of digital images are represented using the RGB space because most of the image acquisition hardware employs this color space to represent colors. But the RGB space is not adequate for color processing due to the color differences are not linear. A common procedure to process RGB images is to apply the image processing techniques to each color channel; but given the high correlation between the color channels, the chromaticity of colors may be modified. A plausible solution is to employ other color spaces suitable for color processing. But not always the processing techniques are applied adequately because the intensity and chromaticity of colors are processed jointly; leading that the colors are altered and/or the quality of the image may decrease. Hence, the techniques for color image processing must be developed considering the nature of color. The aim of this special session is to provide a forum for international researchers and practitioners to present original works addressing the new challenges, research issues and applications in color image processing.

Topics of interest

  • Color image segmentation
  • Color characterization
  • Histogram equalization of color images
  • Pseudo color processing
  • Color models
  • Applications

18. Special Session on Learning from Imbalanced Data (Jair Cervantes Canales, et. al, Mexico)

Organizers:
Jair Cervantes Canales
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Texcoco)
Email:jcervantesc@uaemex.mx

Farid Garcia Lamont
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Texcoco)
Email:fgarcial@uaemex.mx

Asdrúbal Lopez Chau
Department of Computer Sciences
Autonomous University of Mexico State (UAEMEX-Zumpango)
Email:asdrubalchau@gmail.com

Scope:
Machine learning techniques have shown tremendous progress in recent years, which has allowed it become commonly used in the real world. Many techniques have been introduced to discover different representations of knowledge from data in numerous fields. It is in this context that the importance of certain problems that some researchers were beginning to glimpse is of paramount importance. One of such problem is the imbalanced data, where one class contains much smaller number of examples than the remaining classes. The imbalanced distribution of classes constitutes a difficulty for standard learning algorithms and calls for specialized approaches. This problem is extensive in many real-world applications: fraud detection, risk management, face recognition, text classification, and many others. The aim of this special session is to provide a forum for international researchers and practitioners to present and share their original works addressing the new challenges, research issues and novel solutions in imbalanced data.

Topics of interest

  • Sampling techniques for imbalanced data
  • High dimensional and class-imbalanced data.
  • Ensembles for imbalanced data.
  • Pre-processing, structuring and organizing complex data
  • Imbalanced classes in noisy environments.
  • Skewed data and difficult classes.
  • Imbalanced data for regression.
  • Imbalanced data and semi-supervised learning.
  • Imbalanced in multi-class problems.
  • Performance evaluation of classifiers in imbalanced domains.
  • Handling class imbalance by modifying inductive bias and post-processing of learned models
  • Theoretical aspects of constructing combined imbalanced learning systems
  • Imbalanced learning in changing environments
  • Incremental online learning algorithms
  • Cost-sensitive learning.
  • Real applications.