AI-Driven Data Analytics and Mining
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 27
Special Issue Editors
Interests: data analysis; databases; big data; data mining; data science; cybernetics; big data analytics; machine learning; deep learning; sentiment analysis; text mining
Interests: big data analytics; data science; data mining; artificial intelligence; machine learning; deep learning; reinforcement learning; NLP; sentiment analysis; cybersecurity
Special Issue Information
Dear Colleagues,
The rapid growth of data volumes and complexity has made AI-driven analytics and mining indispensable for extracting actionable knowledge. By integrating cybernetic feedback principles with advanced machine learning and sentiment analysis techniques, researchers can develop adaptive, self-regulating systems that learn from dynamic environments and human inputs. This convergence addresses critical challenges in processing high-velocity, heterogeneous data streams while ensuring robust decision support and system autonomy.
This Special Issue of Electronics welcomes contributions that advance AI-driven data analytics and mining, with a focus on cybernetics, big data analytics, and sentiment analysis. We seek original research, reviews, and case studies highlighting novel algorithms, system architectures, and end-to-end pipelines—from data acquisition and integration through to explainable modeling and deployment. Submissions should align with the journal’s mission to foster innovative, open access dissemination of impactful AI solutions.
Application scenarios of interest include, but are not limited to, the following:
- Cybernetic control architectures for adaptive mining.
- Scalable big data preprocessing and feature-learning frameworks.
- Sentiment-aware text and social media analytics.
- IoT-enabled data mining applications.
- Cybersecurity and privacy maintenance.
- Industrial automation and cybernetic control in Industry 4.0.
- Big data analytics in finance and accounting.
- Sentiment analysis and social media intelligence.
- Life sciences and healthcare monitoring.
- Internet of Things (IoT) deployments.
- Management and marketing optimization.
- Environmental monitoring and sustainability.
I/We look forward to hearing from you.
Prof. Dr. Marian-Pompiliu Cristescu
Dr. Claudiu Brandas
Dr. Dumitru Alexandru Mara
Guest Editors
Manuscript Submission Information
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Keywords
- AI-driven data analytics
- data mining
- cybernetics
- big data analytics
- stream mining
- concept-drift adaptation
- explainable AI
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