Since the concept of “Big Data” was first introduced in Nature in 2008, it has been widely applied in fields, such as business, healthcare, national defense, education, transportation, and security. With the maturity of artificial intelligence technology, big data analysis techniques tailored to various fields have made significant progress, but still face many challenges in terms of data quality, algorithms, and computing power.

Journal of Electronic Science and Technology (JEST) plans to publish the Special Section on Progress of Analysis Techniques for Domain-Specific Big Data (ATDSBD). We invite all researchers to contribute their original works and to submit the articles as well as review articles that present state-of-the-art research results and industrial applications. Particularly, we are interested in the articles describing the new techniques for improving the current existing difficulties in the domain-specific big data. Potential topics include, but are not limited to:

♦ Machine learning algorithms for domain-specific big data analysis

♦ Computer vision algorithms for domain-specific big data analysis

♦ Natural language processing techniques for domain-specific big data analysis

♦ Security issues in domain-specific big data analysis

♦ Challenges faced in domain-specific big data analysis

This is a long-term CFP. After paper submission, the review processing for the Initial Review Comments Notification is about one month. After that, the revision time for authors is usually one month or less. And the accepted paper is published in the ABP style on ScienceDirect. That is, the accepted paper will be published online with DOI, vol. no., issue no., and article no., when it is accepted in the Editorial Manager system at once.

Benefits and Responsibilities of Authors:

♦ Rapid peer-review process: About 30 days for the initial review cycle.

♦ The accepted papers will be published for free and Open Access for readers.

♦ Show your paper by Email Alert to more than ten thousand scholars in related areas.

♦ Promote your paper by using TrendMD on ScienceDirect, Springer, IEL, Nature, Science, PNAS, etc.

♦ All authors must sign the Copyright Agreement with JEST.

JEST (ISSN 1674-862X) is a peer-reviewed periodical, open access on ScienceDirect, indexed by Ei Compendex, INSPEC, and Scopus, and also included in CA, CSCD, Science paper Online, etc.

All the manuscripts will undergo standard peer review processes.

For manuscript preparation and submission, please follow the Guide for Authors at https://www.keaipublishing.com/en/journals/journal-of-electronic-science-and-technology/guide-for-authors/.

All the manuscripts (WORD or PDF) should be submitted through the online submission system via https://www.editorialmanager.com/jnlest/default2.aspx.

When submitting, please indicate that this paper is intended for Special Section on Progress of Analysis Techniques for Domain-Specific Big Data (SI:ATDSBD).

For inquiries for this special section, please contact journal@uestc.edu.cn.

Ling Tian is a professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. She has published 69 high-level papers, and has been authorized 46 national invention patents. Her research interests include big data, artificial intelligence, and video coding. She is the national leading talent. She has led the construction of the national scientific platform.　 Prof. Tian has won the Second Prize of Wu Wen Jun AI Science & Technology Award (2020), Second Prize of National Technical Invention Award (2017), First Prize for Technical Invention in Sichuan Province (2017), First Prize for Science and Technology Progress in Sichuan Province (2016), The First Sichuan Provincial Outstanding Youth Science and Technology Innovation Award, and The Outstanding Paper Award of Big Data Mining and Analytics (2019 and 2020). She has served as the Local Organization Chair of ACM Multimedia 2021, Vice Chair of 2020 IEEE International Conference on AI & Mobile Services in Hawaii, USA, and Vice Program Chair of the 15th IEEE International Conference on Green Computing and Communications in Atlanta, USA (2019).

Jian-Hua Tao is a professor with the Institute of Automation, Tsinghua University, Beijing, China. He has published more than 300 papers in many top conferences and international journals, like IEEE TPAMI, IEEE TIP, IEEE TASLP, IEEE TAFFC, and IEEE TSMCB. He has also published a book in Springer, and his publications have been cited by more than 6000 times in Google Scholar, and his H-index is 37. He is a recognized scholar in the field of speech and language processing, multimodal human-computer interaction, and affective computing.　 Prof. Tao has won the Award of Distinguished Young Scholars, National Natural Science Foundation of China (NSFC) (2014), and Award of National Special Support Program for High-Level Person (2018). He has been the Leader or Chief-Scientist of more than 20 national key scientific projects, including the Key Project of NSFC, National Key R&D Program of China, and Key Project of International Cooperation. He has also organized many top international conferences, such as ACII 2005, NCMMSC 2015, NCMMSC 2017, ACII Asia 2018, INTERSPEECH 2020, and IEEE MLSP.

Bin Zhou is a researcher with the School of Computer Science, National University of Defense Technology, Changsha, China. He has engaged in research areas related to data mining, distributed computing, information retrieval, etc., and his current research interests include Internet-oriented text content analysis and mining technology, and large-scale data processing technology. He has published over 150 papers in domestic and international publications and conferences, possesses 8 software copyrights, and more than 10 software patents.　 Dr. Zhou is the Chair of more than 10 national and military projects. And he has won the Second Prize of National Technical Invention Award for 4 times; the provincial First Prize and the provincial Second Prize of Technology Process for 3 and 2 times, respectively. Over the years, he has guided or assisted over 80 students to obtain their Ph.D. or M.S. degrees. He is a member of Big Data Professional Committee, China Computer Federation (CCF).

The authors declare no conflicts of interest.