Machine Learning Applications and Big Data Challenges
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 31 August 2024 | Viewed by 1578
Special Issue Editors
Interests: data science; applied machine learning; networks science; computational social science; natural language processing
Interests: GIS; geospatial big data; health geography; health disparities
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; AI explanation; computational logic; cognitive AI; philosophy of AI; automated scientific discovery; computational philosophy; biodiversity informatics; AI for sustainability and conservation biology
Special Issue Information
Dear Colleagues,
Machine learning (ML) has become a critical component in real-world application domains like industry, transportation, healthcare, manufacturing, and beyond. As organizations move towards digital environments, there will be a surge in data availability, which can introduce novel opportunities and challenges for any machine learning task. Big data, characterized by massive volumes, high velocity, and diverse varieties of data formats, can increase the power and performance of machine learning algorithms designed to solve downstream tasks. Although it introduces new problems with respect to scalability, efficiency, and complexity, the synergy between machine learning and big data can offer unprecedented capabilities to reveal complex patterns and trends. Understanding the applications of machine learning in the context of big data and mitigating any associated challenges still have the potential to advance the modeling of data-driven systems.
The scope of this Special Issue is to collect recent advancements in machine learning applications that are targeted towards tackling the challenges of big data. This Special Issue will also highly value interdisciplinary research to bring new challenges, research questions, approaches, and datasets.
This Special Issue invites new research contributions to machine learning tasks specifically tailored for big data challenges. The scope includes, but is not limited to, the following topics:
- Information retrieval;
- Computer vision;
- Natural language processing;
- Social network analysis;
- Knowledge discovery;
- Trustworthy and secure ML;
- Multi-modal ML systems;
- ML for big graphs;
- Lightweight and efficient models;
- Spatiotemporal and geospatial ML;
- Distributed and parallel ML;
- Applied research such as healthcare, industry, and manufacturing.
Dr. Arunkumar Bagavathi
Dr. Tao Hu
Dr. Atriya Sen
Guest Editors
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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.
Keywords
- big data
- data science
- machine learning
- artificial intelligence
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