Smart and Predictive Strategies in Data Fusion

Dear Colleagues,

It has become increasingly common in this day and age to not be able to handle a system that produces huge amounts of different data, especially with the emergence of our hyper-connected and hyper-digitalized world (IoT, ubiquitous sensing, etc.). Tremendous volumes of raw data are available; their smart management, in order to compute decisions or for enriched data, is a new paradigm for data fusion. Embracing this paradigm is the objective of this Special Issue. Data (multi-source information) can be heterogeneous, redundant, erroneous, uncertain, etc., implying the need for complex data-fusion strategies. In this context, smart methods are necessary to provide coherent decisions (predictive or not) or more informative data (high-level information). Moreover, strong predictions of merged data could also serve as data inputs in a smart decision system or for AI approaches. Predictive and smart fusion often outperform basic decisions when complex and/or huge amounts of data are involved. For instance, smart-fusion-based machine learning could allow the weighting of contextual data to take their uncertainty into account.

For this Special Issue, we invite authors to submit academic and industrial research papers dealing with fundamental theoretical analyses as well as engineering applications.

Dr. Bruno Marhic
Dr. Laurent Delahoche
Guest Editors

Manuscript Submission Information

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