人工智能 | SCI期刊专刊/国际会议信息5条

人工智能

BMVC 2019

British Machine Vision Conference

全文截稿: 2019-04-29

开会时间: 2019-09-09

会议难度: ★★★

CCF分类: C类

会议地点: Cardiff, Wales, UK

网址：https://bmvc2019.org

The British Machine Vision Conference (BMVC) is the British Machine Vision Association (BMVA) annual conference on machine vision, image processing, and pattern recognition. It is one of the major international conferences on computer vision and related areas held in the UK. With increasing popularity and quality, it has established itself as a prestigious event on the vision calendar.

人工智能

ECAI 2019

International Conference on Electronics, Computers and Artificial Intelligence

全文截稿: 2019-05-19

开会时间: 2019-06-27

会议难度: ★★

CCF分类: 无

会议地点: Pitesti, Arges, Romania

网址：http://www.ecai.ro

The conference is aimed to serve as an international forum for effective exchange of scientific knowledge and experience among researchers active in various theoretical and applied areas of electronic equipments, communication, automatic control, applied informatics, information technology and computer science.

The program will include plenary and regular sessions, special sessions, workshops, discussions’ groups and social events.

The conference’s official language is English and areas of interest are:

Electronic circuits and equipments;
Communications;
Microwaves - techniques and technologies & EMC;
Bio-medical applications & biomaterials;
Software, data bases, and computer applications;
Expert systems & Artificial Intelligence;
Robotics, mechatronics and control;
Electrical engineering applications;
Energy & Environmental issues;
Educational multimedia applications;
Other topics are welcome

人工智能

DataCom 2019

IEEE International Conference on Big Data Intelligence and Computing

全文截稿: 2019-05-30

开会时间: 2019-11-18

会议难度: ★★

CCF分类: 无

会议地点: Kaohsiung, Taiwan, China

网址：https://www.cs.ccu.edu.tw/~conference/datacom2019/index.php

Topics of interest include, but are not limited to:

The 5Vs of the data landscape: volume, variety, velocity, veracity, value
Big data science and foundations, analytics, visualization and semantics
Software and tools for big data management
Security, privacy and legal issues specific to big data
Big data economy, QoS and business models
Scientific discovery and business intelligence
Software, hardware and algorithm co-design, high-performance computing
Large-scale recommendation systems and graph analysis
Infrastructures and systems for big data analytics and managements
Middleware and tools for big data analytics and managements
Algorithmic, experimental, prototyping and implementation
Data quality issues: such as validation, metrics, optimizations and consistency
Data-driven innovation, computational modelling and data integration
Data intensive computing theorems and technologies
Big data for advanced manufacturing and productivity
Modelling, simulation and performance evaluation
Green data centers / environmental-friendly perspectives
Computing, scheduling and resource management for sustainability
Complex applications in areas where massive data is generated

人工智能

International Journal of Approximate Reasoning

Special Issue on Formal concept analysis, Rough sets, and Three-way decisions

全文截稿: 2019-07-01

影响因子: 1.766

CCF分类: B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

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

Formal concept analysis, rough sets, and three-way decisions are prominent theories and methods for data representation and analysis. They have been applied to data mining, machine learning, artificial intelligence as well as many other areas.

This special issue of theInternational Journal of Approximate Reasoningwill provide a forum for scholars studying formal concept analysis, rough sets, and three-way decisions to contribute to theses areas and share their achievements. The editors of this special issue invite authors to submit theoretical and empirical papers on these topics.

人工智能

Neural Networks

Special Issue on Advanced Deep Learning Methods for Biomedical Image Analysis

全文截稿: 2019-08-01

影响因子: 7.197

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

  • 小类 : 计算机：人工智能 - 2区

  • 小类 : 神经科学 - 2区

网址: http://www.journals.elsevier.com/neural-networks/

Biomedical processing involves the analysis of heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction and brain activity to provide useful information upon which clinicians can make decisions. It furthers emphasis on practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.

Large amount of biomedical information and health data (particularly images) was gathered these years. However, how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the collected data is important for clinical applications and in understanding the underlying biological process.

Deep neural network is a subset of machine learning, using a model inspired by the structure of the brain. It has been rapidly developed recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing neural-network layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.

The importance of our special issue is to bring the latest theoretical and technical advancements of deep learning to biomedical image and health data analysis. Meanwhile, the investigations on the applications of deep learning to biomedical image and health data analysis may bring the reflect of improving the models of deep neural network.

The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of Deep learning and Computational Machine Learning, to solve problems in biomedical engineering. The ultimate goal is to promote research and development of deep learning for multimodal biomedical images and other health data, by publishing high-quality research articles and reviews/tutorials in this rapidly growing interdisciplinary field.

Main Topics include:

Theoretical understanding of deep learning in biomedical engineering

Transfer learning, disentangling task transfer learning, and multi-task learning

Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images

Adversarial training on biomedical images and other health data

Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming

Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.)

Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)

Optimization by deep neural networks, design of new loss functions (e.g., rank-based loss function)

New model or improved model of convolutional neural network, (e.g., ResNet, DenseNet, Google Inception, etc.)

Visualization and explainable deep neural network.

下载Call4Papers App，获取更多详细内容！