COVID-19 health crisis highlights the fragility of European industrial strategies and leads us to develop more agile, distributed, and resilient production models at a territorial level. There are two major challenges in this regard: one is to find solutions to secure supplies and/or industrial value chains, and the other is to identify companies that have the potential to transform their production quickly to cope with an emergency situation. We extended the Word2Vec vector space with products and economic activities allowing us to calculate proximities. We present a methodology based on semantic proximity and productive complexities to assess the ability of an A-company to produce a product B and to anticipate customer/supplier-type collaboration according to industrial quality standards. We consider recommendation topics by intertwining machine learning techniques with semantic approaches, referring to area ontologies incorporating territorial dimensionality.
CCS CONCEPTS• Natural language processing; • Recommender systems; • Supply chain management;
Biopsy remains the gold standard for the diagnosis of chronic liver diseases. However, the concordance between readers is subject to variability causing an increasing need of objective tissue description methods. A complete framework has been implemented to analyze histological images from any kind of tissue. Based on the feature selection approach, it computes the most relevant subset of descriptors in terms of classification from a wide initial list of local and global descriptors. In comparison with equivalent methods, this implementation is able to find lists of descriptors which are significantly shorter for an equivalent accuracy and furthermore it enables the classification of slides using combinations of global and local measurements. The results have pointed that it could reach an accuracy of 82.8% in a human liver fibrosis grading approach by selecting 6 descriptors from an initial set of 258 global and local descriptors.
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