Machine Learning and Data Mining: Innovations in Big Data Analytics, 2nd Edition
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 256
Special Issue Editors
Interests: machine learning; data mining; applied artificial intelligence; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on "Machine Learning and Data Mining: Innovations in Big Data Analytics" aims to explore the latest advancements and applications of machine learning and data mining techniques in the context of big data. As the volume, variety, and velocity of data continue to grow exponentially, there is a pressing need for innovative methods to extract meaningful insights and knowledge from large datasets. This Special Issue will bring together researchers and practitioners to present cutting-edge approaches, share experiences, and discuss future trends in this rapidly evolving field.
Contributions to this Special Issue should address theoretical, methodological, and practical aspects of machine learning and data mining as they relate to big data analytics. We welcome high-quality research papers, comprehensive reviews, and insightful case studies that highlight new challenges, propose novel solutions, and demonstrate successful applications in various domains such as healthcare, finance, social media, and more.
Topics of interest include the following:
- Advanced machine learning algorithms for big data;
- Scalable data mining techniques;
- Deep learning and its applications in big data analytics;
- Real-time data processing and analytics;
- Predictive modeling and forecasting with big data;
- Anomaly detection and pattern recognition in large datasets;
- Big data visualization and interpretation;
- Applications of machine learning and data mining in healthcare, finance, social media, etc.;
- Ethical and privacy considerations in big data analytics;
- Tools and frameworks for big data processing.
This Special Issue aims to be a comprehensive resource for those looking to stay at the forefront of machine learning and data mining with respect to big data applications. By bringing together diverse perspectives and pioneering research, we hope to foster a deeper understanding of the challenges and opportunities in this exciting field.
Dr. Shadi Banitaan
Dr. Mina Maleki
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. Information 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 analytics
- machine learning
- data mining
- deep learning
- scalable algorithms
- predictive modeling
- anomaly detection
- real-time processing
- data visualization
- social media analytics
- data privacy
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