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人工智能

CEMiSG 2018

International Workshop on Computational Energy Management in Smart Grids

全文截稿: 2018-01-15

开会时间: 2018-07-08

会议难度: ★★

CCF分类: 无

会议地点: Rio de Janeiro, Brazil

网址：http://www.cemisg.org/cemisg2018

The 5th International Workshop on Computational Energy Management in Smart Grids (CEMiSG 2018) will be held on July 8-13, 2018,  in Rio de Janeiro, Brazil as inside the IEEE World Congress on Computational Intelligence 2018 (IEEE WCCI 2018).

The Workshop is oriented to explore the new frontiers and challenges within the Computational Intelligence research area for the optimal usage and management of energy resources in Smart Grid applicative scenarios. The Workshop will be a proficient discussion table within the IEEE WCCI 2018 conference, which attracts the most famous researchers in the Computational Intelligence field worldwide.

人工智能

IV 2018

IEEE Intelligent Vehicles Symposium

全文截稿: 2018-01-15

开会时间: 2018-06-26

会议难度: ★★

CCF分类: 无

会议地点: Suzhou, China

网址：http://www.iv2018.org/

The 2018 IEEE Intelligent Vehicles Symposium (IV'18) is a premier annual technical forum sponsored by the IEEE Intelligent Transportation Systems Society (ITSS). It brings together researchers and practitioners worldwide to share and discuss the latest advances in theory and technology related to intelligent vehicles. It welcomes articles dealing with any aspect of intelligent vehicles, as well as proposals for workshops and tutorial sessions. Demonstration and Exhibition related to intelligent vehicles are also welcome.

Together with IV'18, the Chinese 10th Intelligent Vehicles Future Challenge (IVFC 2018, June 30-July 1, 2018) will be held at the Chinese flagship Intelligent Vehicle Proving Center (iVPC), Changshu, Suzhou, China. which is a county-level city located in the lower reaches of the Yangtze River in Jiangsu Province. Its envied cultural history, beautiful landscape and abundant produce have won itself a great admiration in east China.

The topics of interest include and but are not limited to the following:
-Automated vehicles with/without pilot/driver
-Advanced driver assistance systems
-Vehicle dynamics and control
-Advanced sensing and recognition
-Connected vehicles
-Human factors and driver personalization
-Intelligent electrified vehicles
-Parallel driving: Cyber-physical-social systems based connected automated vehicles
-Navigation and localization systems
-Vehicular signal processing
-Artificial intelligence technologies in intelligent vehicles
-Human machine interaction
-Vehicle on-board diagnostics
-Vehicle hardware /software systems
-Inter-vehicular networks
-Testing and assessment of connected and automated vehicles
-Policies and regulations for intelligent vehicles

人工智能

AIED 2018

International Conference on Artificial Intelligence in Education

摘要截稿: 2018-01-31

全文截稿: 2018-02-07

开会时间: 2018-06-25

会议难度: ★★★

CCF分类: 无

会议地点: London, UK

网址：https://aied2018.utscic.edu.au/

Welcome to the official web site for the 19th International Conference on Artificial Intelligence in Education! For the 2018 conference on Artificial Intelligence in Education, we are excited to have a co-located event, the “Festival of Learning”, together with the International Conference of the Learning Sciences (ICLS) and ACM Learning at Scale (L@S). The Festival will take place in London (UK) between June 23 and 30. AIED 2018 will take up recent trends emerging from AI and Neural Science, asking how these advances can impact human learning at various scales and in various contexts. Moreover, we ask how the fields of Artificial Intelligence and Learning Sciences may speak to one another at the confluence, which is the field of Artificial Intelligence in Education. Thus, the theme of this year’s conference is “Bridging the Behavioral and the Computational: Deep Learning in Humans and Machines”.

The preparations for AIED’18 are currently ongoing and we will be releasing information in the coming months.

人工智能

International Journal of Approximate Reasoning

Special issue on Advances on Belief Functions and Their Applications

全文截稿: 2018-03-31

影响因子: 2.845

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：人工智能 - 2区

网址: http://www.journals.elsevier.com/international-journal-of-approximate-reasoning/

The theory of belief functions, also known as evidence theory or Dempster-Shafer theory (DST), is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories. DST was first introduced by Arthur P. Dempster in 1960s, and was later developed by Glenn Shafer in 1970s. During the last fifty years, numerous approaches have been developed to improve the existing theory of belief functions and to extend its applications in various areas. A series of International conferences and schools on Belief Functions have been successfully held since 2010, and more and more sessions about belief functions are included in other related conferences. For example, there were seven sessions on belief functions at the 20th International Conference on Information Fusion in 2017 (FUSION 2017). There is a large and quickly expanding research community interested in the topics related with belief functions.

The objective of this special issue is to collect and report the recent advances on the theory and applications of belief functions. High quality contributions addressing related theoretical and/or practical aspects are expected.

The submissions can be revised and significantly extended versions of recent conference papers (with, e.g., additional results, detailed proofs, applications, etc.) related with belief functions, and this call for papers is also open to everyone interested in the topic of this special issue.

Topics include but are not limited to:

1.Methodology:

- Decision making

- Combination rules

- Conditioning

- Continuous belief functions

- Independence and graphical models

- Statistical inference

- Geometry and distance metrics

- Mathematical foundations

- Computational frameworks

- Links with other uncertainty theories

2.Application:

- Data and information fusion

- Pattern recognition

- Machine learning and clustering

- Tracking and data association

- Data mining

- Signal and image processing

- Computer vision

- Medical diagnosis

- Business decision

- Risk analysis

- Engineering and environment

- Climate change

人工智能

Neurocomputing

Special Issue on Deep Learning Neural Networks: Methods, Systems, and Applications

全文截稿: 2018-03-31

影响因子: 3.317

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：人工智能 - 3区

网址: http://www.journals.elsevier.com/neurocomputing/

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.

The aim of this special issue of Neurocomputing is to showcase state-of-the-art work in the field of deep learning neural networks including their methods, systems, and applications. Original papers related are welcome. The list of possible topics includes, but is not limited to:

l New deep learning algorithms

l New neural network architectures for deep learning

l Hierarchical deep learning

l Multi-dimensional deep learning

l Deep learning of spatio-temporal data

l On-line deep learning neural networks

l Neuromorphic deep learning architectures

l Better combinations of existing algorithms and techniques for deep learning

l Combining policy learning, value learning, and model-based search

l Data-driven deep learning and control

l Optimization by deep neural networks

l Optimization and optimal decision in games by deep learning

l Mathematical analysis of deep learning (regarding convergence, optimality, stability, robustness, adaptability and so on)

l Applications of deep learning algorithms, architectures, and systems to robotics, control, data analysis, prediction and forecast, modeling and simulation, precision health, and other.

人工智能

IEEE SSCI 2018

IEEE Symposium Series on Computational Intelligence

全文截稿: 2018-06-15

开会时间: 2018-11-18

会议难度: ★★

CCF分类: 无

会议地点: Bengaluru, India

网址：http://ieee-ssci2018.org/

The 2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2018) is a flagship annual international conference sponsored by the IEEE Computational Intelligence Society promoting all aspects of computational intelligence.

The IEEE SSCI 2018 co-locates several symposia under one roof, each dedicated to a specific topic in the computational intelligence domain, thereby encouraging cross-fertilization of ideas and providing a unique platform for top researchers, professionals, and students from all around the world to discuss and present their findings. The IEEE SSCI meeting features a large number of keynote addresses, tutorials, panel discussions and special sessions all of which are open to all participants. Each meeting will consider awarding best student paper and best overall paper awards. The conference proceedings of the IEEE SSCI have always been included in the IEEE Xplore and indexed by all other important databases. The IEEE SSCI 2018 will be held in Bangalore, India, a garden city, also known as a Silicon Valley of India.

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