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Special Issue "Data Mining and Machine Learning in Multimedia Databases"
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 8811
Special Issue Editor
Interests: multimedia data management; content-based analysis of multimedia databases; efficient query processing in multimedia databases; distributed big data query processing; real-time analysis of massive multimedia streams
Special Issue Information
Nowadays, thanks to the worldwide availability of cheap information-sensing devices (such as sensors, cameras, RFID readers, and mobile phones) and the growth of storage capacity, data generation has greatly increased, reaching several exabytes per day. Most of such data are of multimedia (MM) types, given the diffusion of inexpensive tools for creating/capturing images, videos, audio, textual documents, and so on.
This MM data avalanche has completely overrun existing techniques for extracting knowledge and value from conventional data. Automatic analysis of MM data is yet an open research issue due to their very complex nature and the lack of appropriate methodologies for accurate and efficient characterization of their content and semantics. Possible contexts of application include, among the others, smart cities, smart mobility, internet-of-things, public health, aging, public/citizen safety/security, advanced education, smart advertising, and automated industry.
This Special Issue focuses on data mining (DM) and machine learning (ML) techniques in the context of MM databases. Our aim is to collect the most recent evidence of innovation in extracting knowledge and value from MM data. We would like to gather researchers from different disciplines and methodological backgrounds to discuss new ideas, original research, recent results, and future challenges in this exciting area. Potential topics include, but are not limited to, the following:
- Big data techniques for MM databases;
- Real-time analysis of massive MM data streams;
- Pipelines for MM data analysis;
- Bias in ML for MM data;
- MM data-driven decision making;
- Classification of MM data;
- Clustering of MM data;
- Prediction of MM data;
- Recommendation of MM data
Prof. Dr. Ilaria Bartolini
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Multimedia databases
- Data mining
- Machine learning
- Knowledge extraction
- Knowledge learning
- Semantics in machine learning
- Big data
- Artificial intelligence
- Deep learning