Machine Learning in Big Data Modeling
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 2725
Special Issue Editor
Interests: psychometrics; psycho-educational assessments; educational data mining; big data modeling; large-scale testing; learning analytics; digital assessments; computerized adaptive testing; statistical programming
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The concept of big data refers to structured, semi-structured, and unstructured data that come with greater variety, increasing volumes, and more velocity. With the exponential growth in data storage and computing power, it is now possible to process quantitative and textual data from various sources, such as mobile devices, social media, and the Internet of Things. Although, big data analytics offers great potential for leveraging big data in knowledge discovery and automation. However, traditional tools for managing and using smaller volumes of data may not be suitable for big data. To manage, organize, and model big data, researchers and practitioners need powerful hardware and distributed computing paradigms, as well as strong predictive models.
The focus of this Special Issue is big data analytics, including—but not limited to—data capture and storage, big data technologies, data visualization techniques for big data, architectures for parallel processing of big data, data mining tools and techniques, machine-learning algorithms for big data, and cloud computing platforms designed for processing big data. We encourage submissions that present findings of empirical research, systematic reviews, or theoretical work utilizing big data in social and natural sciences (e.g., education, psychology, business, health, computing science, etc.).
Dr. Okan Bulut
Guest Editor
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. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- machine learning
- deep learning
- reinforcement learning
- statistical learning
- data mining
- data science
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