Chairs
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Rozaida Ghazali, Universiti Tun Hussein Onn Malaysia, Malaysia, Email: rozaida@uthm.edu.my
Mohd Helmy Abd Wahab, Universiti Tun Hussein Onn Malaysia, Malaysia
Sasalak Tongkaw, HRH Princess Chulabhorn College of Medical Science, Thailand
International Program Committee
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Dipak Kumar Jana, Haldia Institute of Technology, India
Masoud Mohammadian, University of Canberra, Australia
Francesco Masulli, Universita de Genova, Italy
Abzetdin Adamov, University of Qafqas, Azerbaijan
Mario Jose Divan, Universidad Nacional de La Pampa, Argentina
Valentina Emilia Balas, Aurel Vlaicu University, Romania
Mohamad Farhan Mohamad Mohsin, Universiti Utara Malaysia

Scope:
Soft-Computing refers to a collection of emerging problem-solving techniques spanning many fields that fall under various categories in Computational Intelligence, which study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Soft computing is essentially used for information processing by employing methods, which are capable to deal with imprecision and uncertainty especially needed in ill-defined problem areas. It is a fusion of methodologies that were designed to model and enable solutions to real world problems, which are not modeled, or too difficult to model, mathematically. It has three main branches: Fuzzy Logic, Evolutionary Computation, and Neural Networks. Each of these technique provides complementary reasoning and searching methods to solve complex, real-world problems. Practically, there is no area of human activity is left untouched by these Soft Computing techniques. All current Soft Computing technologies will most likely be vastly improved upon in the future. Everything from handwriting and speech recognition to stock market prediction will become more sophisticated as researchers develop better algorithms. It is widely recognized that Soft Computing have been a challenging future research direction. It can be expected that Soft Computing will find broader application in almost every angle of research domain. Today, equipped with more powerful computing devices, we want to solve much more complex (highly nonlinear and high-dimensional) problems. It is indeed of great interest for the scientific community to understand how and to what extent novel Soft Computing techniques can be efficiently used in varieties of applications. The aim of this session is therefore to draw a picture of the recent advances and challenges in evolving Soft Computing and particularly, aims at soliciting contributions dealing with real-world applications. Topics for this session include, but are not limited to:

• Ambient intelligence
• Artificial immune systems
• Evolutionary computing
• Fuzzy set theory
• Genetic algorithms
• Grey set theory
• Hybrid intelligent systems
• Nature inspired computing
• Neural networks
• Support vector machines
• Swarm intelligence

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

Scope:
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. Swarm Intelligence-based techniques 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), Krill Herd Algorithm (KHA), Moth Swarm Algorithm (MSA), Dragonfly Algorithm et. al and its applications. Authors are invited to submit heir original work in the areas including (but not limited to) the following:

• New swarm intelligent algorithms convergence analysis and parameter choice method
• Multi-stage swarm intelligence algorithms with applications
• Hybrid swarm intelligence algorithms with applications
• Hyper-swarm intelligence algorithms with applications
• Various improved version swarm intelligence algorithms with applications

Dr. Zhong-Qiu Zhao & Dr. Donghui Hu
Hefei University of Technology, China
Email: zhongqiuzhao@gmail.com hudh@hfut.edu.cn

Scope:
Image processing and its applications involve many computational intelligence and security problems, especially in social network applications. Users tend to share all kinds of images in the Online Social Networks (OSNs), aiming to satisfy their own psychological needs of “self-disclosure”, enhance the friendship and trust with other users, etc. On the other hand, the users also confront the threat of disclosing their personal information when sharing the images in the open OSNs. There exist two kinds of contradictions problems in social network digital images sharing. One is the contradiction between the utility and privacy when sharing digital images among social network, the other is the contradiction between the complexity as well as the magnanimity of the digital images and it’s sharing environments and the real-time as well as reliable requirements for the privacy decision. It's an important branch of machine learning and artificial intelligence, covering sparse coding, deep learning and other state of the art methods. This special session will provide researchers and engineers in the field of image processing and its applications a chance to exchange their new ideas, which may contribute much to boosting related theoretical researches and industrial applications. Topics for this session include, but are not limited to:

• Image representation and feature extraction
• Information security in image processing and applications
• Image classification and annotation
• Deep learning with image privacy and security
• Sparse coding with image privacy and security
• Trust, privacy and security of digital image in social network
• Other related topics in image processing and applications

Prof. Jun Feng
School of Information and Technology,Northwest University, Xi’an Shannxi,710127, China.
Email: fengjun@nwu.edu.cn

Prof. Dinggang Shen
Department of Radiology and BRIC,University of North Carolina at Chapel Hill, United States
Email: dgshen@med.unc.edu

Scope:
Biomedical image analysis is playing an important role in various clinical applications. The further improvement of respective computational algorithms highly depend on the progress of related areas such as mathematics, biomedical engineering, information science, cognitive science, and computer science. The purpose of this special session is to bring together researchers and practitioners who are interested in biological statistical modeling, quantitative measurement, visualization and objective decision for improving automatic analysis of biomedical images. The resulted discoveries would have potentials to improve or change the current clinical diagnosis workflow. Authors are encouraged to submit full papers that describe the original research work in the following areas:

• Biomedical Image Segmentation
• Biomedical Image Analysis
• Biomedical Image Reconstruction and Visualization
• Computer-aided Diagnosis
• Discovery of imaging biomarkers
• Human Computer Interaction for Biomedical Image Processing
• Biomedical Interpretation
• Image-Guided Intervention
• Biomedical Image Perception
• Biomedical Image Registration
• Intelligent Biomedical Systems
• Machine Learning in Imaging
• Multimodality Image Analysis
• Quantitative Image Analysis
• Organ Shape Analysis
• Super-resolution in Biomedical Imaging
• Statistical Methods in Biomedical Imaging
• Texture Analysis in Biomedical Imaging
• Time Series Analyses in Biomedical Imaging

Dr. Vitoantonio Bevilacqua
Human Computer Interaction Tenured Professor
Electrical and Informatics Department of Polytechnic of Bari �C Italy
Email: vitoantonio.bevilacqua@poliba.it

Scope:
Regardless of the constantly changing scenario of novel systems, devices, and interfaces, Human-machine interaction plays a crucial role in determining how individuals adopt and use technology, both hardware and software. In addition to performance, factors such as, ergonomics, usability, and aesthetics are increasingly important in the era of just in time, on demand interaction: nowadays, devices are able to detect human actions, augment senses and cognition, and provide individuals with assistance in specific tasks or everyday work. Human-Machine systems create a seamless technology environment in which individuals learn, interact, and thus, change the way they realize their activities. The Special Session on Human-Machine Interaction is an opportunity for scientists to share their work and to provide and gain insights on how to design innovative and intelligent tools which will enhance and shape our lives. Topics for this session include, but are not limited to:

• Machine learning techniques in HMI
• Cognitive aspects of HMI, usability, and design principles
• Usability, Accessibility & User experience
• Robotics and Human-machine coupling
• Wearable computing and interaction
• Multimodal interaction and interfaces
• Context-aware, ubiquitous, pervasive, and mobile interaction
• Mathematical and computational aspects of HMI
• Mobile, Ambient, Virtual, Augmented or Tangible HMI
• Recognition of and interaction with human activity and emotion
• HMI and Biometrics in healthcare
• HMI as enabling technology
• HMI in Human-robot cooperation
• Biosignal-based HMI
• Novel devices, sensors, and interfaces

Yunxia Liu, Professor, Ph.D
College of information science and technology
Zhengzhou normal university, Zhengzhou, China
Email: liuyunxia0110@hust.edu.cn

Scope:
Information security has become a crucial need for almost all information transaction applications due to the large diversity of the hackers and attacks, Traditional techniques such as cryptography, watermarking, and data hiding are basic notions and play an important role in developing information security algorithms and solutions. In spite of the large development in the information security techniques, there are still several challenges that need to be addressed in terms of time, accuracy and reliability. The special session targets the information security research area with respect to trends, advanced techniques and applications, which attracts researchers and practitioners from academia and industry, and provides a discussion environment in order to share their experiences in information security.Authors are encouraged to submit both theoretical and applied papers on their research in information security. Topics of interest include, but are not limited to:

• Machine learning techniques in HMI
• Data hiding
• applied cryptography
• data protection
• formal methods in security
• information dissemination control
• network security
• privacy
• secure group communications
• security in social networks
• embedded security

Dr. Michael Gromiha
Head, Protein Bioinformatics Lab, Department of Biotechnology
Indian Institute of Technology Madras, Chennai 600 036, India
Email: gromiha@iitm.ac.in
URL:https://biotech.iitm.ac.in/faculty/michael-gromiha-m/

Y-h. Taguchi, Professor, Ph.D.
Department of Physics, Chuo University, Tokyo 112-8551,Japan
Email: tag@granular.com
URL: http://orcid.org/0000-0003-0867-8986

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

Dr. Prashan Premaratne
School of Electrical, Computer and Telecommunications Engineering
University of Wollongong,Australia
Email: prashan@uow.edu.au

Scope:
Ever since the success of US DARPA Grand Challenges, the world is fixated on achieving autonomous navigation using multiple sensors. Out of all the sensors available to the scientists, vision remains the simplest and the most versatile. This session will focus on bringing the diverse research in navigation methodology in Drones, Robotic Ground Vehicles, driverless cars and Humanoid Robots together. Some of the focus areas are, but not limited to:

• Navigation techniques of driverless cars such as Tesla and Uber
• Small scale drone navigation
• Navigation of robotic battle tanks
• Navigation of robotic aquatic vehicles including submarines and ships
• Any other unclassified vision based navigation methods

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. Jin Zhou
Department of Information science and Engineering, University of Jinan, Jinan, PR China
Email: ise_zhouj@ujn.edu.cn

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

Scope:
Neural network has over 50 years of development and has been widely used as an efficient tool for many real-world applications. In recent years, novel studies on Neural Networks have attracted worldwide attention again, and entered upon a next stage of development. The typical example is the Neural Networks in Deep Learning. It has been successfully applied in real-world applications including signal processing, robot control, classification, etc. Recently, it has also been employed to construct deep architectures for deep learning to model high-level abstractions in data, and achieved considerable success in applications such as natural language processing, music signal recognition, computer vision and automatic speech recognition, etc. In this special session we mainly discuss on the practical research for advanced theory and application of Neural Networks to various fields. Authors are encouraged to submit their original and unpublished work in the areas including, but not limited to:

• Approaches for optimizing neural networks based on evolutionary computation algorithms
• Modelling of neural networks paradigm based on real-world application problems
• Neural modeling fields
• Evolutionary Computation in Neural Networks
• Theoretical analysis of neural networks
• Real-world applications of evolutionary neural networks
• Bioinformatics and complex networks
• Applied neural networks on Life Sciences
• Applied neural networks on Engineering
• Novel or Improved frameworks of Neural Networks
• Advances in Neural Networks
• Approaches for optimizing neural networks based on evolutionary computation algorithms
• Theoretical analysis of evolutionary neural networks
• Theory, design, and applications 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
• Applications of deep learning in data representation and analysis, including recognition, understanding, detection, segmentation, retrieval, restoration, super-resolution, and compression
• Modelling and analysis of real spiking neurons and neuron networks
• Others

Prof. Bin Liu
Harbin Institute of Technology Shenzhen Graduate School, China
Email: bliu@hit.edu.cn

Scope:
To expedite analyses of increasing number of biological sequences, many machine-learning algorithms have been introduced into computational biology. By using these techniques, protein structures and functions can be identified based on their primary sequences, and genomics functions can also be analyzed via the sequence data, such as promoter identification, enhancer identification, and disease relationship prediction, etc. The machine learning methods and feature extraction algorithms are playing key roles in computational biology. In order to promote the development of this important area, we invite authors to contribute original research manuscripts to this special session, focusing on the machine learning algorithms and their applications to computational proteomics, genomics, and biological sequence analysis. Potential topics include, but are not limited to:

• DNA, RNA, and protein feature extraction algorithms, and their applications to computational biology
• Protein structure and function prediction based on machine learning methods
• DNA binding protein and RNA binding protein identification
• Epigenomics and disease relationship prediction
• Piwi-interacting RNA, microRNA, and long noncoding RNA prediction
• Protein-protein interaction and their binding site prediction
• Enhancer, promoter prediction and their function analysis
• Recombination hot/cold spot identification
• Motif and regulatory element identification from high-throughput data
• Advanced machine learning methods and their applications to bioinformatics