Internet of things (IoT) has become an interesting topic in the field of technological research. It is basically interconnecting of devices with each other over the internet. Beside its general use in terms of autonomous cars and smart homes, but some of the best applications of IoT technology in fields of health care monitoring is worth mentioning. The main purpose of this research work is to provide comport services for patients. It can be used to promote basic nursing care by improving the quality of care and patient safety from patient home environment. Rural area of a country lacks behind the proper patient monitoring system. So, remote monitoring and prescribing by sharing medical information in an authenticated manner is very effective for betterment of medical facilities in rural area. We have proposed a healthcare system which can analyze ECG report using supervise machine learning techniques. Analyzing report can be stored in cloud platform which can be further used to prescribe by the experienced medical practitioner. For performance evaluation, ECG data is analyzed using six supervised machine learning algorithms. Data sets are divided into two groups: 75 percent data for training the model and rest 25 percent data for testing. To avoid any kind of anomalies or repetitions, cross validation and random train-test split was used to obtain the result as accurate as possible.

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Cloud computing is a type of emerging computing technology that relies on shared computing resources rather than having local servers or personal devices to handle applications. It is an emerging technology that provides services over the internet: Utilizing the online services of different software. Many works have been carried out and various security frameworks relating to the security issues of cloud computing have been proposed in numerous ways. But they do not propose a quantitative approach to analyze and evaluate privacy and security in cloud computing systems. In this research, we try to introduce top security concerns of cloud computing systems, analyze the threats and propose some countermeasures for them. We use a quantitative security risk assessment model to present a multilayer security framework for the solution of the security threats of cloud computing systems. For evaluating the performance of the proposed security framework we have utilized an Own-Cloud platform using a 64-bit quad-core processor based embedded system. Own-Cloud platform is quite literally as any analytics, machine learning algorithms or signal processing techniques can be implemented using the vast variety of Python libraries built for those purposes. In addition, we have proposed two algorithms, which have been deployed in the Own-Cloud for mitigating the attacks and threats to cloud-like reply attacks, DoS/DDoS, back door attacks, Zombie, etc. Moreover, unbalanced RSA based encryption is used to reduce the risk of authentication and authorization. This framework is able to mitigate the targeted attacks satisfactorily.