Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

Despite its widespread use, liver US has several important limitations that healthcare providers should recognize, particularly because of its low sensitivity. Using a combination of echographic parameters, liver US showed a significant improvement in its diagnostic performance, but still was of limited value for monitoring treatment over time.

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Multisectorial community programs to promote healthy living in public spaces are crucial for building a "culture of health" and could contribute to achieving the specific 2030 agendas of sustainable development goals, including reduction of inequalities; provision of inclusive, safe, resilient, and sustainable cities; and promotion of just, peaceful, and inclusive societies. In this context, the Recreovía program of Bogotá (Colombia) provides physical activity classes in parks mainly for vulnerable communities. We address the challenge of efficiently locating new Recreovía hubs through a novel robust data envelopment analysis (DEA) centric location-based decision support system (DSS) that helps the District Institute of Sports and Recreation of Bogotá (IDRD) to locate the best hubs to expand the Recreovía program throughout the city. The tool is applied to analyze different scenarios including one that was implemented in Bogotá and yielded an improvement of 28% in compound monthly growth rate of the average attendance to the hubs.

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