Applications of Artificial Intelligence Using Real Data
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (17 January 2022) | Viewed by 7989
Special Issue Editors
Interests: ubiquitous data mining; data mining in finance; deep learning; text mining
Special Issue Information
Dear Colleagues,
The topic “data is the new oil” has been discussed in editorials from Forbes to The Economist. The continuous and non-continuous information data sources are both advantageous and a challenge to the future economy and society. The artificial intelligence and machine learning algorithms used to process such data require acceptable accuracy rates for specific tasks. Yet, over time, the monitoring accuracy, processing overhead and transparency of such algorithms still warrant careful analysis for real applications. Data-driven methods can also be unintentionally contaminated by bias or undermined by concept drift within dynamical data streams. The goal of this Special Issue in the journal of Information is to map how artificial intelligence algorithms can be applied to real problems and address such issues. Specific focus will be given to applications in areas such as medicine, education, IoT, and finance. Original research work, significantly extended versions of conference papers, and review papers are welcome. Topics of interest include, but are not limited to, the following:
- AI in medicine and healthcare;
- Data mining in genomics;
- AI-powered therapy;
- Data mining using epidemiological data;
- Data mining in education;
- Mining public data sources;
- IoT and data stream mining;
- Cybersecurity and data mining;
- AI in stock markets;
- AI in industry and services;
- AI in finance, marketing, and production.
Dr. Nuno Cavalheiro Marques
Dr. Bruno Silva
Guest Editors
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Keywords
- artificial intelligence in medicine
- artificial intelligence in finance
- artificial intelligence and Internet of Things
- data streams
- machine learning data visualization
- interpretable machine learning
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