Information Entropy
- Submission status
- Open
- Submission deadline
The realm of information entropy research represents a multidisciplinary field, merging mathematical theories with real-world data. At its core, information entropy is the study of uncertainty in information to extract meaningful patterns from vast and chaotic datasets, a concept with profound implications in diverse fields, from cryptography to machine learning. In the era of big data, there is a growing demand for advanced methods that strike a balance between data compression and the preservation of information integrity. The emergence of quantum computing adds an exciting yet challenging dimension, promising unprecedented computational power while also posing new theoretical and practical challenges in understanding and harnessing quantum information entropy. Furthermore, the quest to apply entropy in machine learning algorithms aims to teach machines not only to process data but also to comprehend and predict the inherent unpredictability of the information they analyze. This collection invites original research submissions that explore the complexity and dynamics of large networks and foster deeper integration of entropy concepts with emerging technologies like machine learning and quantum computing.
Editors
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Alexander V. Mantzaris, PhD
University of Central Florida Dept of Statistics and Data Science, USA
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Constantinos Patsakis, PhD
University of Piraeus, Greece
