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Authors:Tamunobarafiri; Amavey, Aghili, Shaun, Butakov, Sergey Pages: 1 - 22 Abstract: Cloud computing has been massively adopted in healthcare, where it attracts economic, operational, and functional advantages beneficial to insurance providers. However, according to Identity Theft Resource Centre, over twenty-five percent of data breaches in the US targeted healthcare. The HIPAA Journal reported an increase in healthcare data breaches in the US in 2016, exposing over 16 million health records. The growing incidents of cyberattacks in healthcare are compelling insurance providers to implement mitigating controls. Addressing data security and privacy issues before cloud adoption protects from monetary and reputation losses. This article provides an assessment tool for health insurance providers when adopting cloud vendor solutions. The final deliverable is a proposed framework derived from prominent cloud computing and governance sources, such as the Cloud Security Alliance, Cloud Control Matrix (CSA, CCM) v 3.0.1 and COBIT 5 Cloud Assurance. Keywords: IT Security and Ethics; Security & Forensics; Biometrics Citation: International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), Volume: 5, Issue: 4 (2017) Pages: 1-22 PubDate: 2017-10-01T04:00:00Z DOI: 10.4018/IJMSTR.2017100101 Issue No:Vol. 5, No. 4 (2017)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Skapura; Nicholas, Dong, Guozhu Pages: 23 - 37 Abstract: Understanding diseases and human activities, and constructing highly accurate classifiers are two important tasks in bio-medicine, healthcare, and wearable sensor technology. Being able to mine high-quality patterns is useful here, as such patterns can help improve understanding and build accurate classifiers. However, most pattern mining algorithms only operate on discrete data; applying them often requires a binning step to discretize continuous attributes. This article presents a new discretization technique, called Class Distribution Curve based Binning (CDC Binning); the main idea is to use a so-called class distribution curve, which measures the class purity in sliding windows over an attribute's range, to construct binning intervals. Experiments show that (1) CDC Binning outperforms existing binning methods in discovering high-quality patterns, especially when the class distribution curve is complicated (e.g. when the two classes are two fairly similar human activities), and (2) it can outperform other binning methods by 10% in classification accuracy when using discovered patterns as features. CDC Binning is particularly useful for applications where the classes/activities to be distinguished are similar to each other. This is especially important in wearable sensor technology where detection of behavioral or activity changes in a person (e.g. fall detection) could indicate a significant medical event. Keywords: IT Security and Ethics; Security & Forensics; Biometrics Citation: International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), Volume: 5, Issue: 4 (2017) Pages: 23-37 PubDate: 2017-10-01T04:00:00Z DOI: 10.4018/IJMSTR.2017100102 Issue No:Vol. 5, No. 4 (2017)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Bousdekis; Alexandros, Papageorgiou, Nikos, Magoutas, Babis, Apostolou, Dimitris, Mentzas, Gregoris Pages: 38 - 62 Abstract: The evolution of Internet of Things (IoT) has significantly contributed to the development of the sensing enterprise concept and to the use of appropriate information systems for real-time processing of sensor data that are able to provide meaningful insights about potential problems in a proactive way. In the current article, the authors outline a conceptual architecture and describe the system design requirements for deciding and acting ahead of time with the aim to address the Decide and the Act phases of the “Detect-Predict-Decide-Act” proactive principle, which are still underexplored areas. The associated developed information system is capable of being integrated with systems addressing the Detect and the Predict phases in an Event Driven Architecture (EDA). Keywords: IT Security and Ethics; Security & Forensics; Biometrics Citation: International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), Volume: 5, Issue: 4 (2017) Pages: 38-62 PubDate: 2017-10-01T04:00:00Z DOI: 10.4018/IJMSTR.2017100103 Issue No:Vol. 5, No. 4 (2017)
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