J
Javier Andreu-Perez
Researcher at University of Essex
Publications - 54
Citations - 2883
Javier Andreu-Perez is an academic researcher from University of Essex. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 12, co-authored 48 publications receiving 1938 citations. Previous affiliations of Javier Andreu-Perez include Imperial College London & University of Jaén.
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Deep Learning for Health Informatics.
Daniele Ravi,Charence Wong,Fani Deligianni,Melissa Berthelot,Javier Andreu-Perez,Benny Lo,Guang-Zhong Yang +6 more
TL;DR: A comprehensive up-to-date review of research employing deep learning in health informatics is presented, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook.
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Big Data for Health
TL;DR: Some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled are discussed.
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From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare
TL;DR: The opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media are discussed.
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The contribution of neuroscience to consumer research: A conceptual framework and empirical review
TL;DR: In this paper, the authors developed a semantic cluster analysis of the boundaries of consumer neuroscience, followed by a comprehensive empirical review from 34 selected studies and proposed a novel approach to classify findings and facilitate the assessment of evidence around the topics of decision-making, rewards, memory and emotions.
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A Self-Adaptive Online Brain–Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System
TL;DR: The novel online learning method presented consists of a self-adaptive GT2 FS that can autonomously self- adapt both its parameters and structure via creation, fusion, and scaling of the fuzzy system rules in an online BMI experiment with a real robot.