Machine Learning in E-services
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 2432
Special Issue Editors
Interests: e-learning; e-health; machine learning; semantic web
Interests: current research is focussing in the application of machine learning techniques to detect Mild Cognitive Impairment and Alzheimer's Disease in an early stage. Moreover, in the development of tech-solutions (e.g., serious games, computerized tools, smart conversational agents, etc.), to both cognitive assessment and cognitive interventions, in order to prevent dementia and other age-related diseases, from an ecological and non-intrusive perspective.
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Special Issue Information
Machine learning is a discipline within computer science that went through massive growth in the past several years. Through a series of mathematical algorithms, together with the increasing availability of data and computational power, a machine learning system can make accurate predictions based on existing data. In addition to the development of new algorithms or the fine-tuning of existing ones, many researchers identified a huge potential in the application of machine learning methods to solve relevant problems. One of the most promising application fields is ‘e-services’, that is, the provision of traditional services through an online Internet-based channel.
This Special Issue will address the most recent advances in the introduction of machine learning techniques to improve and personalize e-services. Thus, contributions are expected to present original research on machine learning with real-world applications in e-services.
Topics for this Special Issue include, but are not limited to the following:
- New machine learning algorithms for e-services.
- Benchmarking of machine learning alternatives in e-services.
- Adaptation and fine-tuning of machine learning in specific e-services domains.
- Machine learning in e-learning.
- Machine learning in e-health.
- Machine learning in e-government.
- Machine learning in e-commerce.
- Machine learning in e-Business.
- Machine learning in e-security.
- Machine learning in social networks.
- Machine learning in marketing.
- Machine learning for the development and deployment of e-services on cloud computing scenarios.
Prof. Dr. Luís E. Anido-Rifon
Dr. Sonia Valladares Rodriguez
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. Electronics 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 2400 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.
