Information technology (IT) plays an increasingly important and prominent role in the health sector. Data security is more important than ever to the healthcare industry and in world in general. The number of data breaches compromising confidential healthcare data is on the rise. For data security, cloud computing is very useful for securing data. Due to data storage issue, there is a need to use the electronic communication, and a number of methods have been developed for data security technology. Health Insurance Portability and Accountability Act (HIPAA) is one of the methods that can help in healthcare research. On stored database of patient in hospital or clinic, we can develop a conservational and analytical method so as to keep the medical records of the patients in a well-preserved and adequate environment. The method includes the improvement of working possibilities by delivering all the details necessary for the patient. All the information must be identified clearly. The protection of the privacy of the patients and the security of their information are the most imperative obstacles to obtain their intakes when considering the adoption of useful health data in the electronic field of healthcare industries.
This paper aims to study energy consumption in a house. Home energy management system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be scalable reusable and interoperable. The study suggests a technique for decreasing cost of energy that HEMS is using and various perspectives in system. The method that proposed is K-NN (K-Nearest Neighbor) which helps us to analyze the classification and regression datasets. This paper has the result from the data relevant in October 2018 from some buildings of Nanjing University of Information Science and Technology.
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