Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury.
#Greating and sharing files with tribler software#
Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.Ī brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets) (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets and (c) for measuring the connectivity among two or more datasets. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. Through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and Connectivity among two or more datasets can be achieved The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets.
More generally, in this article we discuss various methods and techniques from AI to do this. We argue that these examples of Social AI techniques can be used to enable and improve personalized health care even in times where health care is economized upon like we face in The Netherlands. These companions are able to monitor the behavior of patients, help them remind of taking medication, but also can have conversations with them giving them the feeling that they are cared for. In particular, we’ll discuss artificial emotions, serious games for healthcare, and artificial companions to assist with the care of patients in a hospital or home setting. Use in related work on data collection for Value-Based Health Care.
We show techniques from Social AI can be effectively applied in developing Behavior Change Management systems, and illustrate its To this end, we review various examples of work to illustrate and argue that AI, and Social AI in particular, has great potential for improving healthcare and enabling a more personalized approach. It is this focus on a more personalized health care that we want to highlight in this paper. In many of the projects we have worked on there is a shared focus and interest in creating a more personalized approach to health care and medicine. Inspired by work that we have done in various projects at the Alan Turing Institute Almere (ATIA), Delft University of Technology (TUD), Utrecht University (UU), and Vrije University (VU), we’ll review a number of applications of AI in health care and medicine, mainly from a personal viewpoint, expertise and interest. Artificial Intelligence (AI) is becoming more and more ubiquitous, and AI also invades health care and related fields ever more.
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