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Achievement of sustainable privacy preservation is mostly very challenging in a resource shared computer environment. This challenge demands a dedicated focus on the exponential growth of big data. Despite the existence of specific privacy preservation policies at the organizational level, still sustainable protection of a user's data at various levels, i.e., data collection, utilization, reuse, and disclosure, etc. have not been implemented to its spirit. For every personal data being collected and used, organizations must ensure that they are complying with their defined obligations. We are proposing a new clustered-purpose based access control for users' sustainable data privacy protection in a big data environment. The clustered-purpose based access control significantly contributes to handling the personal data for stated, unambiguous, and genuine purposes. The proposed algorithm picks specific records from the sample space. It ensures the sustainability and utilization of data for intended purposes by validating the existing privacy tags, assigning new privacy tags based on a clustered-purpose based approach. The proposed method equally ensures the security and sustainable privacy aspects of existing as well as new personal data managed inside large data-bases repositories. The comparative analysis of significant results presents the outperformance of the proposed algorithm as compared to existing non-purpose based conventional methods of sustainable privacy preservation. The proposed algorithm clusters the large datasets in a big data environment and allows only authorized access to users. The current study is limited to purpose-based access control based on privacy tags. However, future research can also consider other types of privacy protection scenarios in a shared environment. [ABSTRACT FROM AUTHOR]

Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain. Nowadays, in special schools, instructors are commonly use some conventional methods for managing special students for educational purposes. In this paper, we proposed a novel computer aided method for instructors at special schools where they can teach special students with the support of our system using wearable technologies. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness. [ABSTRACT FROM AUTHOR]