This workshop focuses on deep learning applied to large-scale unstructured data, such as that found in social media, healthcare, IoT devices, and online reviews. The international event will bring together academic and industrial researchers to exchange cutting edge research enabling deep learning to perform in a reliable and user-independent manner handling uncertainty and unforeseen events. 

 

 

 

 

 

 

 

The 3rd International Workshop on Deep Learning in Large-scale Unstructured Data Analytics( DeepLUDA’22)

 

Paper submission guidelines:

 

All papers must be submitted through EasyChair system, via the following link. The submission site is now open. 

https://easychair.org/conferences/?conf=deepluda2022The conference proceedings, including all accepted papers, will be published in the Springer Lecture Notes in Computer Science (LNCS) series. Authors should avoid the use of non-English fonts to avoid problems with printing and viewing the submissions. All accepted papers MUST follow strictly the instructions for LNCS Authors. Springer LNCS site offers style files and information: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.

 

Submissions must be original (not previously published and not under review in other forums). This applies to papers on all tracks of the conference. Authors are advised to interpret these limitations strictly and to contact the PC chairs in case of doubt. Each accepted paper must be accompanied by at least one full registration, and an author is expected to present the paper at the conference, otherwise, the paper will be removed from the proceedings and the LNCS digital library.

 

The review process is single-blinded. There is no need for authors to mask their names and affiliations in the manuscript. The maximal length of the paper is 15 pages.

 

Important dates:

Submission deadline: 31 June, 2022

Acceptance: 21July, 2022

Registration: TBA

Camera-ready: TBA

 

For registration and related questions, please follow the APWeb-WAIM 2022 registration instructions.

 

More details: click here