Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray images from the hospital, and the proposed model has been applied to them. Image augmentation has been performed in this research as it's the most significant task required to be initiated to increase the dataset's size. Also, the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy. As the output, different settings of the network architecture undoubtedly have achieved different accuracy, among which the highest accuracy of the model is 97.61%. Compared with the other models, these results have proven to be superior to this proposed method in achieving state-of-the-art results.

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Privacy can be a key aspect of the user experience with computers, online systems, and new technologies. Knowing what to consider about users and their views of computer systems can only improve privacy mechanism. Human Computer Interaction (HCI) is the sub field of Computer Science that studies how people interact with and through computational technologies. In this paper, a three-level model-based approach is provided for the end user privacy in the human computer interaction as the end user’s privacy in the area of HCI is emerging as critical design element for interacting systems in areas as diverse as e-commerce, health care, office work, personal communications etc. The data privacy of end user as well as the resource access privacy is needed to be considered. As of now, no proper solution exists for all types of interface users which is a major privacy issue. Hence, to provide the end users, their personal data protection as well as the resource access security, this paper focuses on analyzing and developing the theory of technological acceptance related to user privacy