Journal ArticleDOI
Cloud based intelligent system for delivering health care as a service
Pankaj Deep Kaur,Inderveer Chana +1 more
TLDR
This paper designs a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes and proposes infrastructure level mechanisms to provide dynamic resource elasticity for CBIHCS.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2014-01-01. It has received 137 citations till now. The article focuses on the topics: Cloud computing & Cloud computing security.read more
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Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0
TL;DR: By selectively analyzing the literature, this paper systematically survey how the adoption of the above-mentioned Industry 4.0 technologies (and their integration) applied to the health domain is changing the way to provide traditional services and products.
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The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges
TL;DR: An up-to-date picture of the novel healthcare applications enabled by the ICTs advancements, with a focus on their specific hottest research challenges is provided, to help the interested readership not to lose orientation in the complex landscapes possibly generated when advanced ICTS are adopted in application scenarios dictated by the critical healthcare domain.
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Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System
TL;DR: Experimental evaluation of the proposed P PHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system.
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A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing
TL;DR: SVM model with a weighted kernel function method is significantly identifies the Q wave, R wave and S wave in the input ECG signal to classify the heartbeat level to prove the effectiveness of the proposed Linear Discriminant Analysis (LDA) with an enhanced kernel based Support Vector Machine (SVM) method.
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A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges
TL;DR: This work considers ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet.
References
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The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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Wireless sensor networks: a survey
TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.
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Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.
K. G. M. M. Alberti,Paul Zimmet +1 more
TL;DR: A WHO Consultation has taken place in parallel with a report by an American Diabetes Association Expert Committee to re‐examine diagnostic criteria and classification of diabetes mellitus and is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.
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Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.