人工智能 | 国际会议/SCI期刊专刊信息5条

人工智能

CLD2 2019

The workshop on Classifier Learning from Difficult Data

全文截稿: 2018-12-15

开会时间: 2019-06-12

会议难度: ★★

CCF分类: 无

会议地点: Faro, Algarve, Portugal

网址：http://cldd.kssk.pwr.edu.pl/

Nowadays many practical decision task require to build models on the basis of data which included serious difficulties, as imbalanced class distributions, high number of classes, high-dimensional feature, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods which can combat the mentioned above difficulties should be the focus of intense research. The main aim of this workshop is to discuss the problems of data difficulties, to identify new issues, and to shape future directions for research.

Topics of interest
Learning from imbalanced data
learning from data streams, including concept drift management
learning with limited ground truth access
learning from high dimensional data
learning with a high number of classes
learning from massive data, including instance and prototype selection
learning on the basis of limited data sets, including one-shot learning
learning from incomplete data
case studies and real-world applications

人工智能

BigDaCI 2019

International Conference on Big Data Analytics, Data Mining and Computational Intelligence

全文截稿: 2019-01-28

开会时间: 2019-07-16

会议难度: ★★

CCF分类: 无

会议地点: Porto, Portugal

网址：http://bigdaci.org/

The conference is expected to provide an opportunity for the researchers to meet and discuss the latest solutions, scientific results and methods in solving intriguing problems in the fields of Big Data Analytics, Intelligent Agents and Computational Intelligence.

人工智能

NeuroIS 2019

NeuroIS Retreat

全文截稿: 2019-03-10

开会时间: 2019-06-04

会议难度: ★★

CCF分类: 无

会议地点: Vienna, Austria

网址：http://www.neurois.org/neurois-retreat-2019/

NeuroIS studies comprise conceptual and empirical works, as well a theoretical and design science research. It includes research based on
all types of neuroscience and physiological methods. Contributions may address the following topics, among others:

employment of neurophysiological tools to study IS phenomena, e.g., technology adoption, mental workload, website design, flow, virtual worlds, emotions and human-computer interaction, commerce, social networks, information behavior, trust, IT security, usability, avatars, music and user interfaces, multitasking, memory, attention, IS design science, risk, knowledge processes, business process modeling, ERP systems
application of psychophysiological approaches to study technostress, information overload, and IT addiction
identification of the neural correlates of IS constructs based on neuroscience methods
software prototypes of NeuroIS applications, which use bio-signals (e.g., EEG, skin conductance, pupil dilation) as system input
discussion of methodological and ethical issues and evaluation of the status of the NeuroIS field

人工智能

ICRSA 2019

International Conference on Robot Systems and Applications

全文截稿: 2019-03-25

开会时间: 2019-08-04

会议难度: ★

CCF分类: 无

会议地点: Moscow, Russia

网址：http://www.icrsa.org

Topics of interest for submission include, but are not limited to:
Underwater/Aerial Robots
Agriculture Robots
Space Robotics
Biomimetic robotics
Intelligent Transport Systems
Networked robots
Mobiligence
Rescue Robots
SWARM Intelligent Robots
Domestic Personal Robots
Visual Servoing/Robot vision
Medical/rehabilitation robotics
Perception/Learning
Mechanism and Robot Design
Human-Robot Interface
Distributed Robot Coordination
Multi-Agent Systems
Micro-robot
Humanoids
Service/Life Support Robots
Intelligent Security and Surveillance Systems
IAS for Manufacturing
Professional Service Robots
Haptics/Teleoperation
Motion Planning
Navigation/Localization
Robot Simulations

人工智能

Artificial Intelligence in Medicine

Precision Digital Medicine and Health

全文截稿: 2019-03-31

影响因子: 2.879

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 3区

  • 小类 : 工程：生物医学 - 3区

  • 小类 : 医学：信息 - 3区

网址: http://www.journals.elsevier.com/artificial-intelligence-in-medicine/

Advances in artificial intelligence tools and methods provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine [1]. Also, the recent revolution in digital devices (e.g. mobile apps, fitness trackers, sensors, IoT assets) and their software applications enables clinicians and health care workers, consumers and patients to make better informed decisions [2]. Moreover, new initiatives such as precision health and medicine emphasize the importance of focusing on individuals’ risk factors for disease prevention, early diagnosis, and intervention [3].

This special issue of theArtificial Intelligence in Medicinejournal seeks original contributions presenting significant results on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics, focused on applications in precision health and digital medicine. The topics of interest include, but are not limited to, the following areas:

- Knowledge Representation and Extraction

- Integrated Health Information Systems

- Patient Education

- Patient-Focused Workflows

- Shared Decision Making

- Geographical Mapping and Visual Analytics for Health Data

- Social epidemiology

- Social Media Analytics

- Epidemic Intelligence

- Predictive Modeling and Decision Support

- Semantic Web and Web Services

- Biomedical Ontologies, Terminologies, and Standards

- Bayesian Networks and Reasoning under Uncertainty

- Temporal and Spatial Representation and Reasoning

- Case-based Reasoning in Healthcare

- Crowdsourcing and Collective Intelligence

- Risk Assessment, Trust, Ethics, Privacy, and Security

- Sentiment Analysis and Opinion Mining

- Computational Behavioral/Cognitive Modeling

- Health Intervention Design, Modeling and Evaluation

- Online Health Education and E-learning

- Mobile Health

- Internet of Things (IoT) in Health and Medicine

- Applications in Epidemiology and Surveillance (e.g. Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening)

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