The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

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This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare's core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source clustercomputing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients' satisfaction.

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