Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

2. Special Session on Advanced networking:

Ankur Dumka, Assistant Professor
University of Petroleum and Energy Studies, Dehradun, India
Email: adumka@ddn.upes.ac.in

Scope:

This session deals with the advanced concepts of networking like Software Defined networks (SDN) and network function virtualization (NFV) and its uses and application and how it changes the networking by making a software based platform and providing the open source. Concept of Multiprotocol Label Switching (MPLS) and Virtual Private Network is also discussed in this session and the research areas in this field. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

3. Special session on Big data analytics:

Zhou Ji, Ph.D., Columbia University, USA
Email: zji@c2b2.columbia.edu

Henry Han, Professor, Fordham University, USA
Email: xhan9@fordham.edu

Scope:

Massive data sets of unprecedented complexity have been generated by large scale modern computing sources from different fields, e.g. high frequency trading in finance, next generation sequencing in bioinformatics, and extremely active web-based social media, in recent years. The opportunities and the challenges provided by big data call for new methodologies in processing, analysis, and other applications. Innovative ideas in this field are both valued greatly in research, and have heavy impact on real world problems. The goal of this special session is to solicit novel big data analytics approaches, and efforts to apply new methods in various big data applications. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

4. Special Session on Big Data and Predictive Business Analytics:

Ying Liu, Ph.D., MBA
Division of Computer Science, Mathematics and Science
College of Professional Studies
St. John’s University
Queens, NY 11439 USA
Email: liuy1@stjohns.edu

Scope:

Today, the amount of data we collect has been exploding. As a result, big data has become a new buzzword in information technology. Storing, managing and analyzing big data is challenging and will become a key basis of competition and a major differentiator between high performing and low performing organizations. Big data analytics, e.g. predictive analytics, apply techniques from statistics, data mining, text mining, machine learning, and mathematical modeling to predict future outcomes based on historical data. Using the patterns and information discovered from historical data allows businesses to identify potential future opportunities and risks. This session provides a forum for researchers to present and discuss 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 big data and predictive business analytics. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

5. Special Session on Pattern Recognition and Machine Learning:

Xuemei Yang, Professor
School of Mathematics and Information Science
Xianyang Normal University, China
Email: yangxuemei691226@163.com

Scope:

With the rapid development of computer science, pattern recognition, which has its origins in engineering, plays more and more important role in many modern artificial intelligence field, and more and more new approaches of machine learning have been proposed to solve pattern recognition problems. 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 pattern recognition and machine learning. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

6. Special Session on Advances in Particle Swarm Optimization:

Fei Han, Professor, Ph.D
School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
Email: hanfei@mail.ujs.edu.cn

Scope:

Particle swarm optimization (PSO), one of the pillars of Swarm Intelligence, is a population-based stochastic optimization technique. Compared with other optimization methods, PSO has no complicated evolutionary operators and adjusts less parameter in the course of training. These merits make it easy to implement, apply, extend and hybridise. Many attempts have been made to improve the performance of the original PSO in past several years. This special session will highlight the latest development in this rapidly growing research area of new PSO and its applications. Authors are invited to submit their original work in the areas including (but not limited to) the following:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

7. Special Session on Artificial Intelligence for Ambient Assisted Living:

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

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

Dr. Paul Fergus, Liverpool John Moores University, UK
Email: p.fergus@ljmu.ac.uk

Prof. Paulo Lisboa, Liverpool John Moores University, UK
Email: p.j.lisboa@ljmu.ac.uk

Prof. B. Chandra, Indian Institute of Technology, India
Email: b.cHANDRA@maths.iitd.ac.in

Scope:

In Europe and in most of the world, the aging population represents a significant part of the society that should not be ignored due to the significant experience that this population has as well as the precious richness of information and wisdom. This aging population requires special care services that could present challenges to the government’s finances. However, with the presence of technology and ambient assisted living will provide methods for taking care of this important population and create commercial opportunities. Ambient assisted living will create smart spaces and intelligent systems which can help the independence and executive function, social communication as well as the security of people with special needs. This special session on the use of artificial intelligence techniques and the concept of Ambient Assisted Living (AAL) to support aging and special needs people daily living by allowing independency using simple systems such as reminder systems as well as sophisticated smart environments. A smart home can monitor and assist a person with through the use of integrating sensors embedded in the environment that can extract information pertaining to the person’s health and well-being. The aim of this special session is how to deal with this information using artificial intelligence techniques. Various topics will include but are not limited to:

Various artificial intelligence techniques can be used for the analysis and pattern recognition of the data which include, but are not limited to:

8. Special Session on Wearable Technology and Artificial Intelligence for Mining Personal Data:

Chelsea Dobbins, BSc (Hons), PhD, MIEEE
School of Computing & Mathematical Sciences
Liverpool John Moores University
Byrom Street, Liverpool, L3 3AF, UK
Email: C.M.Dobbins@ljmu.ac.uk

Paul Fergus, BSc, MSc, PhD, MBCS
School of Computing & Mathematical Sciences
Liverpool John Moores University
Byrom Street, Liverpool, L3 3AF, UK
Email: P.Fergus@ljmu.ac.uk

Scope:

Within today’s society, it is a common practice to capture, store and share almost every moment of our lives. The inception of wearable devices, such as cameras, body sensors and mobile phones, allows us to continually capture rich information about our surroundings and ourselves. As such, these devices offer an innovative, and less obtrusive, method for capturing content ubiquitously. Various pieces of information can be recorded in this manner, such as photos, videos and physiological readings. Bringing together such heterogeneous data enables a digital profile of our lives to be reconstructed from our collected digital objects. However, mining these personal records to derive meaning, context, learning and understanding is a challenge. Nevertheless, the application of artificial intelligence in this area provides us with a unique opportunity to develop novel solutions, which include much richer data processing capabilities, so that we can obtain meaning from our data. As such, there are still many challenges that remain that encompass a variety of areas and form part of the topics under this special session. The topics of interest for this special session include, but are not limited to:

Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.

9. Special Session on Complex Networks and their Applications:

Dr. Yunxia Liu (Associate Professor)
College of information science and technology
Zhengzhou normal university, Zhengzhou, China
Email: liuyunxia0110@hust.edu.cn

Scope:

Real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network. Complex networks describe a wide range of systems in nature and society, much quoted examples including biological systems, engineering systems, economic systems, the Internet, and so on. Complex Networks and their Applications?aims at bringing together researchers and practitioners from different science communities working on areas related to complex networks, which can cover everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications in ecological, informational, engineering, technological and other systems. Authors are encouraged to submit both theoretical and applied papers on their research in complex networks. Topics for this session include, but are not limited to:

10. Special Session on Swarm Intelligence with Discrete Dynamics: Algorithms and Applications:

Dr. Ben Niu, (Associate Professor)
College of Management, Shenzhen University, Shenzhen, China
Email: drniuben@gmail.com

Dr. Felix T.S. Chan (Professor)
Department of Industrial System and Engineering
The Hong Kong Polytechnic University, Hong Kong
Email: f.chan@polyu.edu.hk

Dr. Chao Liu (Professor)
School of Economic and Management, Bejing University of Technology, Bejing, China
Email: liuchao@bjut.edu.cn

Dr. Yanmin Liu (Professor)
School of Mathematics and Computer Science, Zunyi Normal College, Zunyi, China
Email: yanmin7813@gmail.com

Dr. X.H. Chu
College of Management, Shenzhen University, Shenzhen, China
Email: xianghua.chu@gmail.com

Scope:

Swarm intelligence is a discrete dynamic system that deals with natural and artificial systems which consist of many dynamic individuals with decentralized control and self-organization. As a multidisciplinary study inspired by nature, this field typically focuses on collective behaviors deriving from a population of simple agents interacting with one another and with environment. These behaviors include bees colonies, birds flocking, bacteria foraging, and so on. Over the last few decades, there has been a remarkable growth in this field that encompasses the interests and efforts of researchers ranging from social science and ethology to computer science and engineering. Swarm intelligence has been successfully applied to various real-world problems such as discrete optimization, dynamic decision and computational system.

Topics of interest include but not limited to:

Besides, applications of the above algorithms include but not limited to:

Prospective authors are invited to contribute high-quality papers (not shorter than 10 pages) to ICIC 2015 through Online Electronic Submission System. Some high-quality papers will be selected for special issue in Discrete Dynamics in Nature and Society (SCI index, IF=0.882) or international journals recommended by ICIC committee. Special issue of Discrete Dynamics in Nature and Society is located at: http://www.hindawi.com/journals/ddns/si/430921/cfp/

11. Special Session on Learning from Imbalanced Data:

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 include but not limited to:

12. Special Session on Human-Machine Interaction (HMI) in Rehabilitation Field:

Vitoantonio Bevilacqua, PhD, Tenured Professor
Human Machine Interaction
Dipartimento di Ingegneria Elettrica e dell 'Informazione - Politecnico di Bari, Italy
Email: vitoantonio.bevilacqua@poliba.it

Scope:

Nowadays, the assessment and management of both Cognitive and Motor-Function Rehabilitation require the early convergence of clinical and technical perspectives. Biomedical Data, such as Electro Encephalo Graphic (EEG), surface Electro Myo Graphic (sEMG) and Electro Cardio Graphic (ECG) signals, are used both to perform Rehabilitation Strategies as well as to evaluate the outcomes. Moreover, the availability of low cost devices able to provide Virtual/Augmented scenarious to the patients, motivated the development of Rehabilitation Protocols featuring immersive environments. This Special Session aims to solicit new approaches of Biomedical Data analysis and HMI application for Cognitive and Motor-Function Rehabilitation purposes. Topics for this session include but are not limited to: