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Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

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ABSTRACT:At the nexus of education and technology, blended learning is growing rapidly. Integrating faceto-face and online learning, blending can enhance learning and optimize seat time. This paper describes the use of the blended e-learning model in a course "Data Structures and Algorithms" given at the Afyon Kocatepe University, Turkey. This model is realized as a combination of a face-to-face environment and online learning, using the University's Learning Management System (LMS) named @KU-UZEM. The LMS consists of many applications in accordance with SCORM standards, such as, student records, user roles, courses, exams security applications, student affairs, counseling services, internal communication, director processes and evaluation. It provides whole software infrastructure by a virtual academic institution and is currently used in Afyon Kocatepe University. This paper describes both the technology for course design and programme redesign adopted a blended learning approach with both face-to-face and online learning aimed at enhancing the students' control over their own learning. According to the obtained results with the performed experimental evaluation, the realized blended learning model provided more effective and efficient educational experience rather than traditional, face-to-face learning. A survey conducted at the end of the course also showed that students were satisfied with the pedagogical approach, and their academic achievements were also better than expected. Particularly important is that the dropout rate was greatly diminished, which could be related to students' satisfaction with the support they received from the teacher and the system.

Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed selflearning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex Optimization Algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. ß 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:142-156, 2017; View this article online at wileyonlinelibrary.com/journal/cae;