CCF推荐+SCI二区, IVC和EAAI期刊特刊征稿, 全部明年3月出录用结果!

期刊名：Image and Vision Computing

简称：IVC

中科院JCR分区:

• 大类 : 计算机科学 - 3区

• 小类 : 计算机：人工智能 - 3区

• 小类 : 计算机：软件工程 - 3区

• 小类 : 计算机：理论方法 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 光学 - 3区

CCF等级：人工智能目录下C类

官网：

http://www.journals.elsevier.com/image-and-vision-computing/

Call for paper链接：

https://www.sciencedirect.com/journal/image-and-vision-computing/about/call-for-papers#special-issue-on-synthetic-data-in-generalizable-video-analytics

Image and Vision Computing目前正面向Special issue on Synthetic Data in Generalizable Video Analytics特刊进行征稿，感兴趣的朋友可以关注一下。

（特刊也是正刊，只是某个主题的专辑，不影响SCI检索！）

关键日期：该特刊截稿日期为2022年11月30日，录用通知日期为2023年3月31日。

影响因子: 3.103

投稿范围：

Survey/review of recent advances on synthetic data in video analytics
Evaluating the quality of synthetic data
Self-supervised learning using synthetic data
Generative adversarial learning for robust visual data synthesis
Few-shot learning models that benefit from synthetic data pretraining.
Synthetic-to-real visual domain adaptation
Creating synthetic data for privacy-sensitive object identification and association
Creating synthetic data for testing Autonomous systems
Improving the out-of-distribution generalization of models with synthetic data
Synthesizing realistic video data

The strong performance of Deep Neural Networks should be mainly attributed to the availability of abundant labeled high-quality training data. However, it is prohibitively expensive and time-costly to have the abundant data annotated properly under various real-world environments. Besides, the ever-growing privacy concerns about the disclosure of the identities of and sensitive information about the respondents providing the data are making the data collectors limit the access to data. Such data scarcity and privacy concerns render the application of visual AI models in real-world scenarios more challenging. Recent literature is seeing a new trend that promises to be a saving grace: Synthetic Data. Synthetic data are artificial computer-generated data that can stand-in for real-world data. Creating synthetic data is a much more cost-effective and efficient way for training and testing visual AI models, while mitigating privacy and confidentiality concerns and reducing algorithmic bias. Recently, we’re seeing several emerging research directions about the utility of synthetic data in video analytics. Despite promising progress, the study on the utility of synthetic data in generalizable video analytics is still in the very early stage of research. The goal of the special issue is to solicit high-quality, high-impact and original papers on recent advances about the utility of synthetic data in generalizable video analytics.

期刊名：Engineering Applications of Artificial Intelligence

简称：EAAI

中科院JCR分区:

• 大类 : 计算机科学 - 2区

• 小类 : 自动化与控制系统 - 2区

• 小类 : 计算机：人工智能 - 2区

• 小类 : 工程：电子与电气 - 2区

• 小类 : 工程：综合 - 1区

CCF等级：人工智能目录下C类

官网：

http://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/

Call for paper链接：

https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence/about/call-for-papers

Engineering Applications of Artificial Intelligence目前正面向Computational intelligence-based approaches to fault-tolerant and self-healing control and maintenance of dynamic systems特刊进行征稿，感兴趣的朋友可以关注一下。

关键日期：该特刊截稿日期为2022年12月15日，录用通知日期为3月15日。

影响因子: 4.201

投稿范围：

Mechanisms for the impact of data imbalance and other factors on diagnostic and predictive models
Redundancy elimination technology for mechanical health status data
Data augmentation technology, such as generative adversarial networks, variational autoencoder models
Data quality assessment indicator and method
Transfer learning technology for fault diagnostic and prognostics
Feature design and optimization methods for diagnostic and predictive models
Semi-supervised learning technology for fault diagnostic and prognostics
Metric learning method for data imbalance scenarios
Digital twin-based machinery fault diagnostic and prognostics

Machinery diagnostics and prognostics are involved in several engineering areas, such as manufacturing, energy, transportation, and aerospace. In these areas, the failure of key components such as bearings, gears, and motors will have a serious negative impact on the operation of the entire equipment. Timely detection of faults in machinery can ensure operational efficiency and quality, and prevent catastrophic accidents.

With the development of sensor technology and communication technology, machinery diagnostics and prognostics have entered the era of big data. The combination of artificial intelligence technology and big data has significantly improved the accuracy of machinery diagnostics and prognostics. Artificial intelligence technology makes it possible to mine information about the health status of equipment directly from the raw data, but the distribution of the data is a key factor in determining the effectiveness of data mining. For example, data imbalance, a real and unavoidable data distribution in the field of machinery diagnostics and prognostics, can seriously impact the training process and the convergence correctness of diagnostic and prognostic models. Therefore, it is important to eliminate the negative effects of data imbalance when constructing machinery diagnostics and prognostics models in order to improve the accuracy of diagnostics and prognostics in practical applications.

Artificial Intelligence has received increasing attention for machinery diagnostics and prognostics in recent years. However, there are still some outstanding issues that need to be improved. This special issue aims at stimulating discussions through state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges.

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