Focus on: New trends, challenges and perspectives on healthcare cognitive computing: from information extraction to healthcare analytics

The focus of this special issue is cognitive computing in healthcare, due to the ever-increasing interest it is gaining for both research purposes and clinical applications. Indeed, cognitive computing is a challenging technology in many fields of application (Banavar, 2016) such as, e.g., medicine, education or eco- nomics (Coccoli et al., 2016) especially for the management of huge quantities of information where cognitive computing techniques push applications based on the use of big data (Coccoli et al., 2017). An unprecedented amount of data is made available from a heterogeneous variety of sources and this is true also in the case of health data, which can be exploited in many ways by means of sophisticated cognitive computing solutions and related technologies, such as, e.g., information extraction, natural language processing, and analytics. Also, from the point of view of programming they set challenging issues (see, e.g., Coccoli et al., 2015). In fact, the amount of healthcare that is now available and, potentially useful to care teams, reached 150 Exabytes worldwide and about 80% of this huge volume of data is in an unstructured form, being thus somehow invisible to systems. Hence, it is clear that cognitive computing and data analytics are the two key factors we have for make use – at least partially – of such a big volume of data. This can lead to personalized health solutions and healthcare systems that are more reliable, effective and efficient also re- ducing their expenditures. Healthcare will have a big impact on industry and research. However, this field, which seems to be a new era for our society, requires many scientific endeavours. Just to name a few, you need to create a hybrid and secure cloud to guarantee the security and confidentiality of health data, especially when smartphones or similar devices are used with specific app (see, e.g., Mazurczyk & Caviglione, 2015). Beside the cloud, you also need to consider novel ar- chitectures and data platforms that shall be different from the existing ones,because 90% of health and biomedical data are images and also because 80% of health data in the world is not available on the Web. This special issue wants to review state-of-the-art of issues and solutions of cognitive computing, focusing also on the current challenges and perspecti- ves and includes a heterogeneous collection of papers covering the following topics: information extraction in healthcare applications, semantic analysis in medicine, data analytics in healthcare, machine learning and cognitive com- puting, data architecture for healthcare, data platform for healthcare, hybrid cloud for healthcare

IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Introduction to the Special Issue: The AgentLink III Technical Forums

This article introduces the special issue of ACM Transactions on Autonomous and Adaptive Systems devoted to research papers arising from the three Technical Forum Group meetings held in 2004 and 2005 that were organized and sponsored by the European FP6 Coordination Action AgentLink III

Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness

This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking

New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data

In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future

Mathematical problems for complex networks

Copyright @ 2012 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available through the Brunel Open Access Publishing Fund.Complex networks do exist in our lives. The brain is a neural network. The global economy is a network of national economies. Computer viruses routinely spread through the Internet. Food-webs, ecosystems, and metabolic pathways can be represented by networks. Energy is distributed through transportation networks in living organisms, man-made infrastructures, and other physical systems. Dynamic behaviors of complex networks, such as stability, periodic oscillation, bifurcation, or even chaos, are ubiquitous in the real world and often reconfigurable. Networks have been studied in the context of dynamical systems in a range of disciplines. However, until recently there has been relatively little work that treats dynamics as a function of network structure, where the states of both the nodes and the edges can change, and the topology of the network itself often evolves in time. Some major problems have not been fully investigated, such as the behavior of stability, synchronization and chaos control for complex networks, as well as their applications in, for example, communication and bioinformatics