Saturday 30 June 2018

CFP: International Conference on Advanced Computational Intelligence (ICACI2019) (Jan 1)

The Eleventh International Conference on Advanced Computational Intelligence (ICACI2019) will be held in Guilin, China from June 7-9, 2019. ICACI2019 aims to provide a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research and applications in computational intelligence. The conference will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics. In addition, best paper awards will be given during the conference. The proceedings of ICACI2019 will be submitted for inclusion into the IEEE Xplore Database which is indexed by EI Compendex and Scopus. Moreover, selected papers with extended version will be recommended to publish in special issues of related journals. The conference will favor papers representing advanced theories and innovative applications in computational intelligence.

Call for Papers and Special Sessions

Prospective authors are invited to contribute high-quality papers to ICACI2019. In addition, proposals for special sessions within the technical scopes of the conference are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information on the organizers.

Important Dates

Special session proposals deadline: Dec. 1, 2018
Paper submission deadline: Jan. 1, 2019
Notification of acceptance: Mar. 1, 2019
Camera-ready copy and author registration: Apr. 1, 2019

Further information: http://www.guet.edu.cn/icaci/

Friday 29 June 2018

CFP: IEEE CIM Special Issue on CI for Internet of Things in the Big Data Era (Dec 31)

https://sites.google.com/site/ieeesigbdci/journal/ieeecim-si-iot

AIMS AND SCOPE

Emerging Internet of Things (IoT) applications in various fields, including smart city, smart home, smart grid, e-health, smart transportation, computer vision applications, etc., critically require trustworthy networking solutions that are resilient against disturbances and disruptions, including high mobility, high density, disasters, infrastructure failures, cyberattacks, and other disruptions. The networking framework should be capable of providing more secure, reliable and efficient communications in various network environments, especially for the performance-sensitive and mission-critical applications such as remote surgery and autonomous driving.

Two main challenges exist in enforcing trustworthy IoT. The first challenge comes from the spatial diversity of the entities involved in communications, such as the high mobility of the devices, and the limitations of propagation media and other resources. The second challenge is due to the varying temporal features of the environment. Due to the spatial challenges, the connectivity between network nodes could be unreliable, and therefore the information maintained at each node could be inaccurate, which requires trustworthy solutions that are able to handle the dynamic, imprecise and uncertain information. This can be solved by using computational intelligence (CI) technologies such as fuzzy logic and evolutionary computation. On the other hand, Big Data-based approaches, including deep neural networks, could facilitate data-driven prediction and performance improvement by capturing time- dependent properties of network elements such as user traffic and behaviors. However, the IoT data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The integration of two new technologies, namely IoT and Big Data, gives birth to a novel ecosystem, conveniently called IoT Big Data, which calls for novel CI technologies to provide efficient and powerful tools that scale well with massive data volume analytics and processes, while addressing the challenges brought by the massive amount of data. In short, while CI technologies can achieve a flexible and self-evolving system design, Big Data can facilitate the use of deep neural networks through which learning the best strategy from complex data becomes possible.

This special issue will focus on the technical challenges and the synergistic effect of Big Data and CI for trustworthy IoT. It is envisioned that the combination of Big Data with a large collection of CI algorithms will reach the level of true artificial intelligence in IoT. We invite researchers to contribute their original research articles that will facilitate the development of IoT based on CI and big data technologies, including (but not limited to):
  • Artificial neural networks for IoT Big Data
  • CI for trustworthy IoT
  • CI for mobile edge computing
  • CI for wireless networking
  • CI for security in IoT systems
  • CI for sensor and actuator networks
  • CI for IoT applications
  • Convolutional neural networks for IoT
  • Crowdsourced learning for IoT
  • Data-driven IoT with CI
  • Deep neural networks for trustworthy IoT
  • Deep reinforcement learning for IoT
  • Development of CI for IoT environments
  • Domain adaptation for IoT Big Data
  • Evolutionary computing for IoT Big Data
  • Evolutionary models for IoT Big Data
  • Fuzzy logic for IoT Big Data
  • Learning theory for IoT Big Data
  • Machine learning for IoT Big Data
  • Probabilistic methods for IoT Big Data
  • Recurrent neural networks for IoT
  • Sequence-to-sequence learning for IoT Big Data

IMPORTANT DATES

Submission Deadline: December 31st, 2018
Notification of the First Review Results: March 15th, 2019
Submission of Revised Manuscripts: April 15th, 2019
Notification of Second Review Results: May 15th, 2019
Submission of Final Manuscript: June 15th, 2019
Special Issue Publication: November 2019 Issue

GUEST EDITORS

Dr. Celimuge Wu, The University of Electro-Communications, Japan, celimuge@uec.ac.jp
Dr. Guoliang Xue, Arizona State University, USA, xue@asu.edu
Dr. Jie Li, University of Tsukuba, Japan, lijie@cs.tsukuba.ac.jp

Dr. Kok-Lim Alvin Yau, Sunway University, Malaysia, koklimy@sunway.edu.my

Dr. Junaid Qadir, Information Technology University, Pakistan, junaid.qadir@itu.edu.pk

SUBMISSION INSTRUCTIONS

1. The IEEE Computational Intelligence Magazine requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12 point font, including figures and references. Submitted manuscript must be typewritten in English in single-column format. Before the manuscript is submitted, prospective authors should make sure that the PDF file is (1) printable, and (2) its first page contains the title, authors' names and the corresponding author's email address, abstract, and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission should be made via https://easychair.org/conferences/?conf=ieeecimsiiot2019.

2. Send also an email to guest editor C. Wu (celimuge@uec.ac.jp) with subject “IEEE CIM special issue submission” to notify about your submission.

3. Early submissions are welcome. We will start the review process as soon as we receive your contribution.

Call for Participation: IEEE Conference on Computational Intelligence and Games (CIG 2018), Maastricht, The Netherlands (Aug 14-17)

Computer games offer not only a killer application for computational intelligence (CI), machine learning and search, but also provide a compelling domain in which problem solving and decision making meet artifact creation, to provide highly immersive, complex and rich interaction experiences. Additionally, CI methods promise to have a huge impact on game technology and development, assisting designers and developers, and enabling new types of games.

The Computational Intelligence and Games (CIG) conference series brings together leading researchers and practitioners from academia and industry, to discuss recent advances and explore future directions in this field. The annual IEEE Conference on Computational Intelligence and Games (IEEE CIG) is one of the premier international conferences in this exciting and expanding field.
The topics of interest include, but are not limited to:
  • Machine-learning in games
  • Adversarial search
  • Player/opponent modeling
  • AI in education
  • Emotion recognition in game-play
  • CI/AI-based game design
  • Affective modeling
  • Player experience
  • Procedural content generation
  • Game generation
  • Intelligent interactive narrative
  • Character development and narrative
  • CI/AI for virtual cinematography
  • Multi-agent and multi-strategy learning
  • Applications of game theory
  • General game playing
  • Serious games

Thursday 28 June 2018

CFP: IEEE TETCI Special Issue on Privacy and Security in Computational Intelligence (Nov 30)

I. AIM AND SCOPE

  The advance in the state-of-the-art computing paradigms and infrastructure such as cloud computing, Internet of Things (IoT) and their fusion fog computing, has enabled a variety of large-scale applications where big data are collected, transmitted, stored, processed and mined. Unlocking the value of the data plays the key role in the data lifecycle. Computational intelligence (CI) technologies are an effective and important way to extract the intelligence and knowledge from datasets for data-driven decision-makings. Given that CI methods are usually both data- and computation-intensive, leveraging the large-scale computing paradigms and infrastructure empowers CI methods to handle data at a very large scale for deeper or personalized intelligence and insights. A typical example is the recent boom of deep learning research which is significantly enhanced by the development of massive computational power.
  However, the characteristics of the state-of-the-art computing paradigms and infrastructural platforms, such as ubiquitous access and multi-tenancy, pose unprecedented privacy and security threats on the computing infrastructure for CI and the application of CI in real problems, rendering users more vulnerable to privacy leakage and security attacks. It is necessary to keep privacy and security concerns in mind when implementing hardware (e.g., Intel’s neural networks processor instructions) and platforms for CI, designing CI algorithms, and deploying CI applications. Hence, it is the high time to investigate the privacy and security issues related to CI in the era of big data and cloud/fog computing.
  This special issue aims to present the most recent advances in the privacy and security research related to CI, particularly in (1) secure and privacy hardware and platforms to support CI technologies, (2) innovative secure and privacy CI algorithms for data mining and knowledge discovery, as well as (3) novel CI methods that strengthen privacy and security technologies.

II. TOPICS

  Potential topics of interest for this special issue include, but are not limited to:
  • Secure and large-scale systems and platforms supporting computational intelligence paradigms
  • Privacy-preserving and anonymization technologies for computational intelligence
  • Secure and private computational intelligence algorithm design and analysis
  • Computational intelligence paradigm implementation across private/public computing systems/platforms
  • Information security and privacy theories from computational intelligence perspectives
  • Computational intelligence techniques for cyberspace intrusion detection systems
  • Computational intelligence for digital forensics
  • Computational intelligence for risk management
  • Computational intelligence for data-driven cyberspace security and information privacy
  • Real-world applications of computational intelligence for privacy and security

III. SUBMISSIONS

  Manuscripts should be prepared according to the“Information for Authors” section of the journal(http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of Privacy and Security in Computational Intelligence” and clearly marking “Privacy and Security in Computational Intelligence Special Issue Paper” as commentsto the Editor-in-Chief. Submitted papers will be reviewed by at least three different reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATE


  • Paper submission deadline: November 30, 2018
  • Notice of the 1st round review results: March 01, 2019
  • Revision due: May 31, 2019
  • Final notice of acceptance/reject: August 30, 2019

V. GUEST EDITORS


Wednesday 27 June 2018

CFP: IEEE TEVC Special Issue on Parallel Evolution for Large Scale Optimization (Nov 1)

I. AIM AND SCOPE

  Human societies have entered a new era of intelligent tech- nology, where machines, information, and humans are tightly coupled in the large scale cyber-physical-social spaces (CPSS). As a result, a lot of large-scale problems, such as optimization and learning, are emerging with the aim to explore and exploit of the physical world, mental world and virtual world. With the dramatic advances in big data analytics, communications, computing and data storage, it is expected that Evolutionary Computation (EC), as a powerful approach to complex prob- lems, would play an even more important role in CPSS. This could be achieved through advances in several aspects, such as developing more powerful EC techniques for large-scale optimization problems, bridging EC and emergent techniques in CPSS (e.g., the theory and methods of parallel systems) to offer new mechanisms for managing and controlling complex systems that involve complexity issues of both engineering and social dimensions, and building large-scale evolution systems that are capable of describing, predicting and prescribing the evolution of real-world complex systems. This special issue aims at promoting the development of EC in the above aspects.

II. THEMES

  Researchers are encouraged to submit their latest inves- tigations on EC, either fundamental advances or practical cases, for large-scale problems as well as systems to the special issue. In addition to advancements of EC for large- scale optimization, learning and other challenging problems that arise in complex systems, research on building large-scale evolutionary systems for simulation, management and control of cyber-physical-social systems are most welcome as well.
Topics of interest include (but are not limited to):
  • Evolutionary Computation for Large-Scale Optimization Problems;
  • Evolutionary Computation for Large-Scale Learning Problems;
  • Evolutionary Computation for Complex Systems;
  • Evolutionary Computation for Optimal Management and Control in CPSS;
  • Theoretical Analysis on Evolutionary Computation for Large-Scale Problems and Systems;
  • Adaptation and Learning Mechanisms for large-scale evolutionary systems;
  • Parallel Evolutionary Computation Techniques;
  • New Implementation Technologies of Evolutionary Computation for Emerging Large-Scale problems;
  • New Trends for Evolutionary Computation in Large Scale Optimization.

III. SUBMISSION

  Manuscripts should be prepared according to the “In- formation for Authors” section of the journal found at http://cis.ieee.org/ieee-transactions-on-evolutionary-computation/.
  Please submit your manuscript in electronic form through: http://mc.manuscriptcentral.com/tevc-ieee/, by selecting “PEforLSO Special Issue Papers” as theManuscript Type. Also, please indicate “PEforLSO Special Issue Paper” in the comments to the Editor-in-Chief.
  Submitted papers will be reviewed by at least three different experts. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES

Submission open: May 15, 2018
Submission deadline: November 1, 2018
Tentative publication date: 2019
For further information, please contact one of the following 
Guest Editors.

V. GUEST EDITORS


  • Fei-Yue Wang, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, China, and Qingdao Academy of Intelligent Industries, China feiyue.wang@ia.ac.cn
  • Qinglai Wei, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, China qinglai.wei@ia.ac.cn
  • Ke Tang, Department of Computer Science and Engineering, Southern University of Science and Technology, China tangk3@sustc.edu.cn
  • Carlos A. Coello Coello, Department of Computer Science, CINVESTAV-IPN, Mexico ccoello@cs.cinvestav.mx

Tuesday 26 June 2018

CFP: IEEE TETCI Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT (Oct 15)

I. AIM AND SCOPE

  Wireless networking technology is one of the main components that could empower a wide range of Internet-of- Things (IoT) applications including smart city, smart home, smart grid, e-health, smart transportation, etc. While providing an easily extensible solution for information exchange, wireless networks also have brought some crucial challenges due to the unstable characteristics of wireless communications.
  The first challenge, namely the spatial challenge, comes from the massive number of spatially-spread connected static or mobile devices affected by the limitations and disruptions of the operating environment, including propagation media, disasters, infrastructure failures, and so on. The second challenge, namely the temporal challenge, is due to the time evolution of the temporal features, such as the varying traffic rates, different quality-of-service requirements, and the state changes of the operating environment. Both spatial and temporal challenges can possibly be solved by using Computational intelligence (CI) technologies such as fuzzy logic, artificial neural networks, evolutionary computation, learning theory, probabilistic methods, and so on. On the other hand, big data-based approaches, including deep neural networks and Long Short- Term Memory networks, could facilitate data-driven prediction and performance improvement by capturing time-dependent properties of network elements such as user traffics and behaviors. Meanwhile, new CI technologies should be discussed in order to handle the large volume of IoT big data from various types of devices with different generation speeds and characteristics.
  The design and the operation of a wireless network can benefit from data collected from widely deployed sensors, network devices, social networks, and other sources to address the spatial and temporal challenges. We refer collectively to these data sources as “IoT big data” for convenience. These data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The ready availability of IoT big data and the immense dividends on offer motivate a strong interest both in academia and in industry towards solving some of the vexing challenges that stand in the way of leveraging IoT big data to advance the state of the art in wireless network operations and applications.
  CI technologies are expected to provide efficient and powerful tools that scale well with data volume for IoT big data analytics and process, while addressing the challenges brought by the massive amount of data. While CI technologies can achieve a flexible and self-evolving system design, big data can facilitate the use of deep neural networks which is possible to learn the best strategy from complex data. It is envisioned that the combination of IoT big data with a large collection of CI algorithms will reach the level of true agility in wireless IoT.

II. TOPICS

  • CI-based solutions for spatial & temporal challenges in wireless IoT, including propagation challenges, MAC & routing problems, mobile edge computing issues, disasters, and infrastructure failures.
  • Data-driven prediction and performance improvement for wireless IoT including deep neural networks, Long Short-Term Memory networks, etc.
  • Joint neural networks and learning approaches, such as deep reinforcement learning, for addressing challenges in wireless IoT.
  • CI technologies for handling a large volume of wireless IoT big data.
  • Learning new flexible and self-evolving strategies for resource allocation, network management and planning by analyzing wireless IoT big data with CI.

III. IMPORTANT DATES


  • Manuscriptsubmission:October15,2018.
  • Notification of authors: January 15, 2019.
  • Revised manuscripts due: March 15, 2019.
  • Final editorial decision: May15,2019.

IV. SUBMISSION GUIDELINES

  Manuscripts should be prepared according to the “Information for Authors” section of the journal and submissions should be done through the journal manuscript submission system https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Big Data and Computational Intelligence for Agile Wireless IoT” and clearly marking “Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT” as comments to the Editor-in-Chief.

V. GUEST EDITORS



CFP: International Conference on Intelligent Control and Information Processing (ICICIP 2018) (Jul 15)

Sponsor: Chongqing Three Gorges University
Co-sponsors: Huazhong University of Science and Technology
South China University of Technology
Southwest University
Technical Co-sponsor: IEEE Computational Intelligence Society

The Ninth International Conference on Intelligent Control and Information Processing (ICICIP 2018) will be held in Wanzhou during November 9-11, 2018, following the successes of previous events. Wanzhou located in the northern part of the district and on the upper reaches of the Three Gorges on the Yangtze River, is 228 km away from downtown Chongqing. “Wanzhou” literally means “myriad-prefecture”, where a myriad rivers converge and a myriad traders gather. ICICIP 2018 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.

Call for Papers and Special Sessions

Prospective authors are invited to contribute high-quality papers to ICICIP 2018. In addition, proposals for special sessions within the technical scopes of the symposium are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Researchers interested in organizing special sessions are invited to submit formal proposals to ICICIP 2018. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information on the organizers.

Topic Areas

Topics of contributing papers include, but are not limited to, the following areas:

Intelligent Control and Automation

Autonomous systems, decision support systems, learning and adaptive control systems, intelligent control theory and applications, intelligent fault detection and identification, hybrid intelligent systems, network intelligence and network control, fuzzy logic control, network intelligence and network control, industrial networks and automation, neural networks based control systems, process control, mechatronic systems and optimization algorithms, environmental monitoring and control, intelligent manufacturing systems, microprocessor-based control, motor control and power systems, microprocessor-based control, intelligent vehicle control, intelligent robots, aerospace applications and other applications.

Intelligent Information Processing

Adaptive filtering & signal processing, audio/speech processing and coding, higher order spectral analysis, nonlinear & blind signal processing, neural signal processing, component analysis array signal processing, array signal processing, parallel and distributed processing, time series analysis, multimedia signal processing, design and implementation of signal processing systems, DSP Implementations and embedded systems, image and multidimensional signal processing, image processing & understanding, computer vision & pattern recognition, bioimaging and signal processing, multimedia communications, computer vision & virtual reality, next generation mobile communications, communication signal processing, modulation and channel coding, network coding, sensor networks, cryptography and information security and other applications.

Paper Submission

Authors are invited to submit full-length papers (8 pages maximum) by the submission deadline through the online submission system. Potential organizers are also invited to enlist five or more papers with cohesive topics to form special sessions. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality, and clarity. The authors of accepted papers will have an opportunity to revise their papers and take consideration of the referees' comments and suggestions. Papers presented at ICICIP 2018 will be published in the EI-indexed proceedings to be included in IEEE Xplore Database.

Important Dates

Special session proposals deadline - March 15, 2018
Paper submission deadline - July 15, 2018
Notification of acceptance - August 15, 2018
Camera-ready copy and author registration - September 15, 2018

Call for Participation: Competitions at IEEE CIG 2018 (Jul 15)

Computer games offer not only a killer application for computational intelligence (CI), machine learning and search, but also provide a compelling domain in which problem solving and decision making meet artifact creation, to provide highly immersive, complex and rich interaction experiences.

Additionally, CI methods promise to have a huge impact on game technology and development, assisting designers and developers, and enabling new types of games.

The Computational Intelligence and Games (CIG) conference series brings together leading researchers and practitioners from academia and industry, to discuss recent advances and explore future directions in this field. The annual IEEE Conference on Computational Intelligence and Games (IEEE CIG) is one of the premier international conferences in this exciting and expanding field.

The topics of interest include, but are not limited to:

Machine-learning in games
Adversarial search
Player/opponent modeling
AI in education
Emotion recognition in game-play
CI/AI-based game design
Affective modeling
Player experience
Procedural content generation
Game generation
Intelligent interactive narrative
Character development and narrative
CI/AI for virtual cinematography
Multi-agent and multi-strategy learning
Applications of game theory
General game playing
Serious games

Further information: https://project.dke.maastrichtuniversity.nl/cig2018/

Sunday 24 June 2018

CFP: IEEE TETCI Special Issue on Computational Intelligence for Cellular/Wireless Communications and Sensing (Oct 1)


I. AIM AND SCOPE


  As billions of phones, appliances, drones, traffic lights, security systems, environmental sensors, radars, and other radio-connected sensing and communication devices sum into a rapidly growing Internet of Things (IoT), many challenges such as spectrum allocation and efficiency, energy efficiency, security, have emerged as urgent topics to be solved. For example, 5G wireless communications will be deployed in the 28GHz, 37GHz, 39GHz frequency band, which may co-exist with radars and other sensing devices. Quite often, researchers often handle these challenges using traditional approaches such as game theory, convex optimization, etc. Computational intelligences techniques such as fuzzy systems, evolutionary computing, neural networks and learning systems are capable of handling resources allocation, decision making, where uncertainties abound, so it is very natural to apply computational intelligence to the above challenges in cellular/wireless communications and sensing.
  There are four important differences that make the emerging topics in Computational Intelligence for Cellular/Wireless Communications and Sensing (CICCS) unique.
  1. Compared to traditional communication and sensing problems, the RF data rate is much higher in the emerging area of communication and sensing which means real-time decision such as resource allocation or signal detection should be made much faster based on computational intelligence.
  2. The operating frequencies are much higher and users are heterogeneous.
  3. RF waveforms are typically captured and represented as complex numbers, underscoring the importance of both amplitude and phase of the signal. Although there has been interest recently in complex-valued neural networks, the technology for learning naturally in the complex plane is not fully developed and relies on treating complex variables as two real numbers.
  4. The integration of communication and sensing is highly desirable because the communication and sensing modules are often co-located such as in smart phones, and they may be operated in the same frequency band.

II. TOPICS

  Topics of interest for this special issue include, but are not limited to:

  • New computational intelligence models for communications and sensing
  • Computational intelligence for 5G Communications Wireless
  • Computational intelligence for IoT
  • Computational intelligence for sensor networks
  • Computational intelligence for remote sensing
  • Computational intelligence for spectrum efficiency
  • Computational intelligence for energy efficiency
  • Computational intelligence for radars
  • Computation intelligence for radar and communications co-existence
  • Computational intelligence for integration of communications and sensing


III. SUBMISSIONS


  Manuscripts should be prepared according to the “Information for Authors” section of the journal(http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of Computational Intelligence for Cellular/Wireless Communications and Sensing (SI:CICCS)” and clearly marking “Computational Intelligence for Cellular/Wireless Communications and Sensing (SI: CICCS) Special Issue Paper” as comments tothe Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of amanuscript implies that it is the authors’ originalunpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES

Paper submission deadline: October 1, 2018
Final notice of acceptance/reject: February 1, 2019

V. GUEST EDITORS

Qilian Liang, University of Texas at Arlington, USA;
liang@uta.edu
Gary Yen, Oklahoma State University, USA;
gyen@okstate.edu
Tariq S. Durrani, University of Strathclyde, UK;
durrani@strath.ac.uk
Wei Wang, Tianjin Normal University, China;
weiwang@tjnu.edu.cn
Xin Wang, Qualcomm Inc, USA;
xinwng@qca.qualcomm.com

Saturday 23 June 2018

IEEE Transaction on Fuzzy System, Volume 26, Issue 3, June 2018

1. Lagrange Stability for T–S Fuzzy Memristive Neural Networks with Time-Varying Delays on Time Scales
Author(s): Q. Xiao and Z. Zeng
Page(s): 1091-1103
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7927477&isnumber=8370013

2. A T–S Fuzzy Model Identification Approach Based on a Modified Inter Type-2 FRCM Algorithm
Author(s): W. Zou, C. Li and N. Zhang
Page(s): 1104-1113
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7930413&isnumber=8370013

3. Sensor Fault Estimation of Switched Fuzzy Systems With Unknown Input
Author(s): H. Zhang, J. Han, Y. Wang and X. Liu
Page(s): 1114-1124
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7929306&isnumber=8370013

4. Fuzzy Remote Tracking Control for Randomly Varying Local Nonlinear Models Under Fading and Missing Measurements
Author(s): J. Song, Y. Niu, J. Lam and H. K. Lam
Page(s): 1125-1137
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7931574&isnumber=8370013

5. Distributed Adaptive Fuzzy Control for Output Consensus of Heterogeneous Stochastic Nonlinear Multiagent Systems
Author(s): S. Li, M. J. Er and J. Zhang
Page(s): 1138-1152
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7937827&isnumber=8370013

6. Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems
Author(s): Y. Li and S. Tong
Page(s): 1153-1163
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7937916&isnumber=8370013

7. Dissipativity-Based Fuzzy Integral Sliding Mode Control of Continuous-Time T-S Fuzzy Systems
Author(s): Y. Wang, H. Shen, H. R. Karimi and D. Duan
Page(s): 1164-1176
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7937910&isnumber=8370013

8. A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum-Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue
Author(s): W. Ding, C. T. Lin, M. Prasad, Z. Cao and J. Wang
Page(s): 1177-1191
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7953563&isnumber=8370013

9. Fuzzy Model Predictive Control of Discrete-Time Systems with Time-Varying Delay and Disturbances
Author(s): L. Teng, Y. Wang, W. Cai and H. Li
Page(s): 1192-1206
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7953664&isnumber=8370013

10. Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems
Author(s): F. Wang, B. Chen, X. Liu and C. Lin
Page(s): 1207-1216
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7953644&isnumber=8370013

11. Combination of Classifiers With Optimal Weight Based on Evidential Reasoning
Author(s): Z. G. Liu, Q. Pan, J. Dezert and A. Martin
Page(s): 1217-1230
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7956193&isnumber=8370013

12. Evaluating and Comparing Soft Partitions: An Approach Based on Dempster–Shafer Theory
Author(s): T. Denœux, S. Li and S. Sriboonchitta
Page(s): 1231-1244
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7954636&isnumber=8370013

13. Adaptive Tracking Control for a Class of Switched Nonlinear Systems Under Asynchronous Switching
Author(s): D. Zhai, A. Y. Lu, J. Dong and Q. Zhang
Page(s): 1245-1256
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7959189&isnumber=8370013

14. Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets
Author(s): Y. Yang, D. Chen, H. Wang and X. Wang
Page(s): 1257-1273
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7959194&isnumber=8370013

15. Galois Connections Between a Fuzzy Preordered Structure and a General Fuzzy Structure
Author(s): I. P. Cabrera, P. Cordero, F. García-Pardo, M. Ojeda-Aciego and B. De Baets
Page(s): 1274-1287
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7954674&isnumber=8370013

16. IC-FNN: A Novel Fuzzy Neural Network With Interpretable, Intuitive, and Correlated-Contours Fuzzy Rules for Function Approximation
Author(s): M. M. Ebadzadeh and A. Salimi-Badr
Page(s): 1288-1302
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7954716&isnumber=8370013

17. On Using the Shapley Value to Approximate the Choquet Integral in Cases of Uncertain Arguments
Author(s): R. R. Yager
Page(s): 1303-1310
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7955006&isnumber=8370013

18. Adaptive Fuzzy Sliding Mode Control for Network-Based Nonlinear Systems With Actuator Failures
Author(s): L. Chen, M. Liu, X. Huang, S. Fu and J. Qiu
Page(s): 1311-1323
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7955022&isnumber=8370013

19. Correntropy-Based Evolving Fuzzy Neural System
Author(s): R. J. Bao, H. J. Rong, P. P. Angelov, B. Chen and P. K. Wong
Page(s): 1324-1338
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7956185&isnumber=8370013

20. Multiobjective Reliability Redundancy Allocation Problem With Interval Type-2 Fuzzy Uncertainty
Author(s): P. K. Muhuri, Z. Ashraf and Q. M. D. Lohani
Page(s): 1339-1355
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7964733&isnumber=8370013

21. Distributed Adaptive Fuzzy Control For Nonlinear Multiagent Systems Under Directed Graphs
Author(s): C. Deng and G. H. Yang
Page(s): 1356-1366
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7967847&isnumber=8370013

22. Probability Calculation and Element Optimization of Probabilistic Hesitant Fuzzy Preference Relations Based on Expected Consistency
Author(s): W. Zhou and Z. Xu
Page(s): 1367-1378
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7967849&isnumber=8370013

23. Solving High-Order Uncertain Differential Equations via Runge–Kutta Method
Author(s): X. Ji and J. Zhou
Page(s): 1379-1386
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7967828&isnumber=8370013

24. On Non-commutative Residuated Lattices With Internal States
Author(s): B. Zhao and P. He
Page(s): 1387-1400
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7971990&isnumber=8370013

25. Robust ${L_1}$ Observer-Based Non-PDC Controller Design for Persistent Bounded Disturbed TS Fuzzy Systems
Author(s): N. Vafamand, M. H. Asemani and A. Khayatian
Page(s): 1401-1413
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7970135&isnumber=8370013

26. Decentralized Fault Detection for Affine T–S Fuzzy Large-Scale Systems With Quantized Measurements
Author(s): H. Wang and G. H. Yang
Page(s): 1414-1426
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7970193&isnumber=8370013

27. Convergence in Distribution for Uncertain Random Variables
Author(s): R. Gao and D. A. Ralescu
Page(s): 1427-1434
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7970195&isnumber=8370013

28. Line Integrals of Intuitionistic Fuzzy Calculus and Their Properties
Author(s): Z. Ai and Z. Xu
Page(s): 1435-1446
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7971962&isnumber=8370013

29. Unknown Input-Based Observer Synthesis for a Polynomial T–S Fuzzy Model System With Uncertainties
Author(s): V. P. Vu, W. J. Wang, H. C. Chen and J. M. Zurada
Page(s): 1447-1458
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7971977&isnumber=8370013

30. Distributed Filtering for Discrete-Time T–S Fuzzy Systems With Incomplete Measurements
Author(s): D. Zhang, S. K. Nguang, D. Srinivasan and L. Yu
Page(s): 1459-1471
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7972907&isnumber=8370013

31. Multi-ANFIS Model Based Synchronous Tracking Control of High-Speed Electric Multiple Unit
Author(s): H. Yang, Y. Fu and D. Wang
Page(s): 1472-1484
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7973097&isnumber=8370013

32. A New Self-Regulated Neuro-Fuzzy Framework for Classification of EEG Signals in Motor Imagery BCI
Author(s): A. Jafarifarmand, M. A. Badamchizadeh, S. Khanmohammadi, M. A. Nazari and B. M. Tazehkand
Page(s): 1485-1497
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7982748&isnumber=8370013

33. $Hinfty$ LMI-Based Observer Design for Nonlinear Systems via Takagi–Sugeno Models With Unmeasured Premise Variables
Author(s): T. M. Guerra, R. Márquez, A. Kruszewski and M. Bernal
Page(s): 1498-1509
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7982734&isnumber=8370013

34. Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections
Author(s): P. Rathore, J. C. Bezdek, S. M. Erfani, S. Rajasegarar and M. Palaniswami
Page(s): 1510-1524
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7984880&isnumber=8370013

35. Lattice-Valued Interval Operators and Its Induced Lattice-Valued Convex Structures
Author(s): B. Pang and Z. Y. Xiu
Page(s): 1525-1534
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7990263&isnumber=8370013

36. Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules
Author(s): Y. Zhang, H. Ishibuchi and S. Wang
Page(s): 1535-1549
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7984865&isnumber=8370013

37. Stability Analysis and Control of Two-Dimensional Fuzzy Systems With Directional Time-Varying Delays
Author(s): L. V. Hien and H. Trinh
Page(s): 1550-1564
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7987699&isnumber=8370013

38. A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients
Author(s): O. Obajemu, M. Mahfouf and J. W. F. Catto
Page(s): 1565-1577
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8000603&isnumber=8370013

39. Resolution Principle in Uncertain Random Environment
Author(s): X. Yang, J. Gao and Y. Ni
Page(s): 1578-1588
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8000682&isnumber=8370013

40. Observer-Based Fuzzy Adaptive Event-Triggered Control Codesign for a Class of Uncertain Nonlinear Systems
Author(s): Y. X. Li and G. H. Yang
Page(s): 1589-1599
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8002623&isnumber=8370013

41. Static Output Feedback Stabilization of Positive Polynomial Fuzzy Systems
Author(s): A. Meng, H. K. Lam, Y. Yu, X. Li and F. Liu
Page(s): 1600-1612
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8003337&isnumber=8370013

42. Global Asymptotic Model-Free Trajectory-Independent Tracking Control of an Uncertain Marine Vehicle: An Adaptive Universe-Based Fuzzy Control Approach
Author(s): N. Wang, S. F. Su, J. Yin, Z. Zheng and M. J. Er
Page(s): 1613-1625
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8003434&isnumber=8370013

43. Information Measures in the Intuitionistic Fuzzy Framework and Their Relationships
Author(s): S. Das, D. Guha and R. Mesiar
Page(s): 1626-1637
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8007288&isnumber=8370013

44. A Random Fuzzy Accelerated Degradation Model and Statistical Analysis
Author(s): X. Y. Li, J. P. Wu, H. G. Ma, X. Li and R. Kang
Page(s): 1638-1650
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8007272&isnumber=8370013

45. Measures of Probabilistic Interval-Valued Intuitionistic Hesitant Fuzzy Sets and the Application in Reducing Excessive Medical Examinations
Author(s): Y. Zhai, Z. Xu and H. Liao
Page(s): 1651-1670
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8010471&isnumber=8370013

46. A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data
Author(s): S. Zeng, X. Wang, H. Cui, C. Zheng and D. Feng
Page(s): 1671-1687
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8015177&isnumber=8370013

47. Asynchronous Piecewise Output-Feedback Control for Large-Scale Fuzzy Systems via Distributed Event-Triggering Schemes
Author(s): Z. Zhong, Y. Zhu and H. K. Lam
Page(s): 1688-1703
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8016371&isnumber=8370013

48. Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence
Author(s): N. Capuano, F. Chiclana, H. Fujita, E. Herrera-Viedma and V. Loia
Page(s): 1704-1718
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8016383&isnumber=8370013

49. Fuzzy Bayesian Learning
Author(s): I. Pan and D. Bester
Page(s): 1719-1731
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8017463&isnumber=8370013

50. Observer and Adaptive Fuzzy Control Design for Nonlinear Strict-Feedback Systems With Unknown Virtual Control Coefficients
Author(s): B. Chen, X. Liu and C. Lin
Page(s): 1732-1743
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8030110&isnumber=8370013

51. Controllable-Domain-Based Fuzzy Rule Extraction for Copper Removal Process Control
Author(s): B. Zhang, C. Yang, H. Zhu, P. Shi and W. Gui
Page(s): 1744-1756
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8039200&isnumber=8370013

52. Renewal Reward Process With Uncertain Interarrival Times and Random Rewards
Author(s): K. Yao and J. Zhou
Page(s): 1757-1762
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7954980&isnumber=8370013

53. Uncertainty Measures of Extended Hesitant Fuzzy Linguistic Term Sets
Author(s): C. Wei, R. M. Rodríguez and L. Martínez
Page(s): 1763-1768
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7970150&isnumber=8370013

54. Correction to “Detection of Resource Overload in Conditions of Project Ambiguity” [Aug 17 868-877]
Author(s): M. Pelikán, H. Štiková and I. Vrana
Page(s): 1769-1769
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8370173&isnumber=8370013

Friday 22 June 2018

Call for Participation: IEEE World Congress on Computational Intelligence (IEEE WCCI 2018), Rio de Janeiro, Brazil (Jul 8-13)

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences: The 2018 International Joint Conference on Neural Networks (IJCNN 2018), the 2018 IEEE International Conference on Fuzzy Systems (FUZZ- IEEE 2018), and the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

IEEE WCCI 2018 will be held at the Windsor Barra Convention Centre, Rio de Janeiro, Brazil. Rio de Janeiro is a wonderful and cosmopolitan city, ideal for international meetings. Rio boasts fantastic weather, savory cuisine, hospitable people, and modern infrastructure. Rio is the first city to receive the Certificate of World Heritage for its Cultural Landscape, recently conferred by UNESCO.

IJCNN is the flagship conference of the IEEE Computational Intelligence Society and the International Neural Network Society. It covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.

FUZZ-IEEE is the foremost conference in the field of fuzzy systems. It covers all topics in fuzzy systems, from theory to applications.

IEEE CEC is a major event in the field of evolutionary computation, and covers all topics in evolutionary computation from theory to applications.

The highlights of the Congress include:


Apart from the technical program, participants are also cordially invited to attend various social events that will include welcome reception and conference banquet. In addition, participants are also encouraged to explore the beautiful city of Rio de Janeiro which has an endless supply of attractions and things to see and do (http://www.ecomp.poli.br/~wcci2018/rio-de-janeiro-2/).

Wednesday 20 June 2018

Call for Participation: International Summer Camp on AI, Hefei, China (Jul 1-14)

    Artificial Intelligence (AI) has become an important engine for next ‘Cambrian of Civilization’, burst of knowledge and technology. Do you want to know the state of the arts of AI? Do you want to experience and touch by yourself the most advanced techniques of AI? Do you want to know the trend of AI in both academic and industry? Do you want to know all the above happened in China? Come to join us! This Summer Camp will bring you with excellent experience in both technology and culture. You may get some knowledge about why AI is so special in China, and why AIs in China are so special.
Further Information: http://scai.ustc.edu.cn/

CFP: Australasian Joint Conference on Artificial Intelligence (AI 2018) (Jul 1)

The Australasian Joint Conference on Artificial Intelligence is an annual conference that has dedicated to fostering research communication and collaboration among Australasian AI community since inception. The 31th Australasian Joint Conference on Artificial Intelligence will be hosted by Victoria University of Wellington, New Zealand in December 2018. The Program Committee invites prospective authors to submit original and previously unpublished research and application papers in all spectrums of Artificial Intelligence. The conference topics cover but are not limited to the following areas:
Prospective authors are invited to submit their original unpublished work to AI 2018. The topics of interest to this conference include but are not limited to the following:
  • Agent-based and multiagent systems
  • AI applications and innovations
  • Cognitive modeling and computer human interaction
  • Big data capture, representation, and analytics
  • Commonsense reasoning
  • Computer vision and image processing
  • Constraint satisfaction, search and optimization
  • Data mining and knowledge discovery
  • Evolutionary computation and learning
  • Fuzzy systems and neural networks
  • Game playing and interactive entertainment
  • Intelligent education and tutoring systems
  • Knowledge acquisition and ontologies
  • Knowledge representation and reasoning
  • Machine learning and applications
  • Multidisciplinary AI
  • Natural language processing
  • Planning and scheduling, combinatorial optimization
  • Uncertainty in AI
  • Visualisation in AI
  • Robotics
  • Game theory
  • Text mining and Web mining
  • Web/Social media mining
We encourage cross-boundary works contributing to theory and practice of AI. Novel application domains including cybersecurity, healthcare, IoT, social media and big data real-world applications are highly welcome. Submitted papers should not exceed 12 pages and should not be under review or submitted for publication elsewhere during the review period. All papers will be peer-reviewed by at least three independent referees.
All papers accepted and presented at AI 2018 will be included in the conference proceedings published by the Springer Lecture Notes in Computer Science/Artificial Intelligence (LNCS/LNAI, pending to approval), which are typically indexed by Engineering Index (Compendex), ISI Proceedings/ISTP, and DBLP.

Submission page is here.