人工智能 | 国际会议/SCI期刊专刊信息6条

人工智能

ARDUOUS 2019

International Workshop on Annotation of useR Data for UbiquitOUs Systems

全文截稿: 2018-11-10

开会时间: 2019-03-11

会议难度: ★★

CCF分类: 无

会议地点: Kyoto, Japan

网址：https://text2hbm.org/arduous/

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or though the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. With pervasive systems relying increasingly on large datasets for designing and testing models of users’ activities, the process of data labelling is becoming a major concern for the community. This also reflects the increasing need of (semi-)automated annotation tools and knowledge transfer methodologies, which can reduce the manual annotation effort and to improve the annotation performance in large datasets.

To address the problem, this year’s workshop focuses on tools and methods for annotation of data for diverse tasks and settings and such for (semi-)automated annotation of large user datasets and annotation reusability across datasets.

人工智能

ISMSI 2019

International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

全文截稿: 2018-11-10

开会时间: 2019-03-23

会议难度: ★★

CCF分类: 无

会议地点: Male, Maldives

网址：http://www.ismsi.org/

Welcome to the official website of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2019). The Conference is organized by India International Congress on Computational Intelligence (IICCI) (www.iicci.in) and will be held in Male, Maldives during March 23-24, 2019.

The main objective of ISMSI 2019 is to present the latest research and results of scientists related to Intelligent Systems, Metaheuristics & Swarm Intelligence topics. This conference provides opportunities for the delegates to exchange new ideas face-to-face, to establish business or research relations as well as to find global partners for future collaborations. We hope that the conference results will lead to significant contributions to the knowledge in these up- to- date scientific fields.

人工智能

FLAIRS-CBR 2019

International FLAIRS Conference  In cooperation with the American Association for Artificial Intelligence

全文截稿: 2018-11-19

开会时间: 2019-05-20

会议难度: ★★

CCF分类: 无

会议地点: Sarasota, FL, USA

网址：https://www.adventiumlabs.com/flairs32-cbr

This track is intended to gather AI researchers and practitioners with an interest in CBR and related fields to present and discuss developments in CBR theory and application as well as possibilities for collaboration, integration, and sharing of ideas, experience, and best practices between the CBR community and other AI research communities.

Similar to the ICCBR conference, we will actively seek submissions that create connections to other research communities whose interests overlap with those of the CBR community, such as the Computational Creativity, Goal Reasoning, Cognitive Systems, and Game AI communities.

Papers and contributions are encouraged for any work relating to Case-Based Reasoning. Topics of interest may include (but are in no way limited to):
-Foundations of CBR
-Methods for CBR (e.g., representation, indexing, retrieval, adaptation)
-Evaluation Methods for CBR Systems and Integrations
-Practical Applications of CBR
-Textual CBR
-CBR and Creativity
-CBR and Design
-Distributed CBR
-Case Base Maintenance
-CBR in the Health Sciences
-CBR Integrations
-Case-Based Planning
-CBR & Games
-CBR and Recommender Systems
-CBR Tools and Methodologies
-Case-based Agents
-Reasoning about Time in CBR
-Process-Oriented CBR
-CBR and Knowledge Discovery
-Computational Analogy

人工智能

IJCNN 2019

International Joint Conference on Neural Networks

全文截稿: 2018-12-15

开会时间: 2019-07-14

会议难度: ★★★

CCF分类: C类

会议地点: Budapest, Hungary

网址：https://www.ijcnn.org/

The 2019 International Joint Conference on Neural Networks (IJCNN) will be held at the InterContinental Budapest Hotel in Budapest, Hungary on July 14-19, 2019. The conference is organized by the International Neural Network Society (INNS) in cooperation with the IEEE Computational Intelligence Society, and is the premier international meeting for researchers and other professionals in neural networks and related areas. It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distrbuted intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest.

人工智能

Cognitive Systems Research

Special Issue on Neutrosophic Sets and Systems in Data Science

全文截稿: 2019-01-01

影响因子: 1.425

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：人工智能 - 4区

  • 小类 : 神经科学 - 4区

网址: http://www.journals.elsevier.com/cognitive-systems-research/

Data science employs techniques and theories drawn from many fields for the knowledge extraction from large volumes of data. The increasing number of developments in data analytics, data driven discovery and big data present a challenging need to deal with various uncertain issues in the data science process. This need has become an essential problem with regard to the success of data science projects.

Neutrosophic sets and logic are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. A number of new netrosophic theories have been proposed and have been applied in multi-criteria decision making, computational intelligence, image processing, medical diagnosis, fault diagnosis, optimization design, and so on.

Neutrosophic logic, set, probability, statistics, etc., are, respectively, generalizations of fuzzy and intuitionistic fuzzy logic and set, classical and imprecise probability, and classical statistics and so on.

Neutrosophic sets can be used to effectively describe and incorporate uncertain data, and Neutrosophic systems can directly aid reasoning and inference in a learning machine. In addition, neutrosophic sets and systems techniques can be used in conjunction with machine learning methodologies to model human behaviors.

The objective of this special issue is to bring together the most recent advances in the design and application of Neutrosophic approaches to real problems in data science.

We welcome authors to present new techniques, methodologies, mixed method approaches and research directions unsolved issues. Topics of interest include, but are not limited to:

- Neutrosophic tools in Big data analytics

- Novel Neutrosophic techniques in Deep learning

- Neutrosophic tools in Social media mining

- Neutrosophic techniques in Data visualization

- Neutrosophic techniques in Mining complex patterns

- Transfer learning

- Neutrosophic techniques in Classification, regression, clustering, pattern mining and real-time learning

- Neutrosophic theory in bioinformatics, health and medical analytics

- Neutrosophic theory in machine learning

- Neutrosophic theory in Intelligent system modelling

- Neutrosophic set-based applications and case studies

人工智能

Applied Soft Computing

Special Issue on Benchmarking of Computational Intelligence Algorithms

全文截稿: 2019-04-14

影响因子: 3.907

CCF分类: 无

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

  • 小类 : 计算机：跨学科应用 - 2区

网址: http://www.journals.elsevier.com/applied-soft-computing/

Computational Intelligence (CI) is a huge and expanding field which is rapidly gaining importance, attracting more and more interests from both academia and industry. It includes a wide and ever-growing variety of optimization and machine learning algorithms, which, in turn, are applied to an even wider and faster growing range of different problem domains. For all of these domains and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Such knowledge can only be obtained empirically, by collecting data from experiments, by analyzing this data statistically, and by mining new information from it. Benchmarking is the engine driving research in the fields of optimization and machine learning for decades, while its potential has not been fully explored. Benchmarking the algorithms of Computational Intelligence is an application of Computational Intelligence itself! This virtual special issue of the EI/SCIE-indexed Applied Soft Computing journal published by Elsevier solicits novel contributions from this domain according to the topics listed below.

Topics of Interest

- mining of higher-level information from experimental results

- modelling of algorithm behaviors and performance

- visualizations of algorithm behaviors and performance

- statistics for performance comparison (robust statistics, PCA, ANOVA, statistical tests, ROC, …)

- evaluation of real-world goals such as algorithm robustness, reliability, and implementation issues

- theoretical results for algorithm performance comparison

- comparison of theoretical and empirical results

- new benchmark problems

- automatic algorithm configuration and selection

- the comparison of algorithms in “non-traditional” scenarios

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