Advances in Intelligent Data Analysis and Its Applications, 2nd Edition
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 25391
Special Issue Editors
Interests: data mining; granular computing; intelligent decision making
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; cognitive computation; granular computing
Special Issues, Collections and Topics in MDPI journals
Interests: Markov jump systems; stochastic systems; Event-triggered schemes; filtering design; controller design; cyber-attacks; time-delay; robust control
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid expansion of cloud computing, the Internet of Things (IoT), and the industrial Internet has given rise to a plethora of intricate data analysis tasks within the framework of societal and economic development. In grappling with these multifaceted challenges, the central role of computational intelligence becomes evident, encompassing the utilization of expansive models and the employment of cognitive analysis techniques.
Within the context of addressing data analysis dilemmas, a fundamental quandary surfaces: the effective management, modeling, and processing of the extensive and heterogeneous datasets acquired through the adoption of these emergent technologies. Consequently, there exists an imperative to delve into efficacious models and methodologies that leverage the potential of computational intelligence for the facilitation of intelligent data analysis and applications. In the contemporary milieu, a diverse cohort of scholars and practitioners has collectively woven a rich fabric of intelligent data analysis and applications from a myriad of vantage points. These encompass disciplines spanning data mining, machine learning, natural language processing, granular computing, social networks, machine vision, cognitive computation, and other hybrid paradigms.
Given the inundation of intricate data in the tangible world, the exploration of intelligent data analysis and applications assumes paramount significance across an array of scenarios in the epoch of big data. Such undertakings not only serve to confront immediate challenges but also to enrich the tapestry of the computer science and engineering community, propelling us toward a future characterized by enhanced data literacy and technological advancement.
The inaugural volume of the Special Issue "Advances in Intelligent Data Analysis and its Applications" has been successful, featuring a collection of high-quality papers. Building upon this initial achievement, the objective of this Special Issue is to continue gathering recent advancements in the field of intelligent data analysis and exploring their practical applications across a spectrum of real-world domains. These domains encompass finance, medical diagnosis, business intelligence, engineering, environmental science, and more. We invite submissions of original research contributions, substantially extended renditions of conference papers, and comprehensive review articles. The topics of interest span a broad spectrum and include, but are not limited to, the following areas:
- Intelligent data mining algorithms and their practical applications;
- Utilizing machine learning techniques for intelligent data analysis;
- Advancements in natural language processing for data analysis;
- Intelligent granular computing models and their real-world use cases;
- Applying intelligent data analysis to glean insights from social networks;
- Harnessing machine vision for data analysis and interpretation;
- Innovations in hybrid models that combine cognitive computation and intelligent data analysis.
Dr. Chao Zhang
Dr. Wentao Li
Dr. Huiyan Zhang
Dr. Tao Zhan
Guest Editors
Manuscript Submission Information
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Keywords
- data mining
- data analysis
- cloud computing
- machine learning
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