Distance education (DE), which has evolved under the wings of information technologies in the last decade, has become a fundamental part of our modern education system. DE has not only replaced the traditional education method as in social sciences and lifelong learning opportunities but also has significantly strengthened traditional education in mathematics, science, and engineering fields that require practical and intensive study. However, it is deprived of supporting some key elements found in traditional educational approaches such as (i) modern computer laboratories with installed special software suitable for the student’s field of interest; (ii) adequate staff for maintenance and proper functioning of laboratories; (iii) face-to-face technical support; (iv) license fees. For students to overcome these shortcomings, a virtual application pool is needed where they can easily access all the necessary applications via remote access. This research aims to develop a platform-independent virtual laboratory environment for DE students. This article has been developed specifically to guide DE institutions and to make a positive contribution to the literature. Technology Acceptance Model (TAM) has been used to explain student behaviors. It was concluded that students using the platform performed more successful grades (12.89%) on laboratory assessments and that the students using the developed platform were found to be more satisfied with the education process.

The most important key features of this study are high performance, easy scalability and serverless architecture. In this way, the system can work with fewer hardware elements and be more robust than others that use name node architecture. Also, both the reliability and performance of the system are significantly increased by separating replication nodes from data nodes. As a result, a complete big data solution that is easy to manage and performs well has been produced and successfully used.

Information technologies have invaded every aspect of our lives. Distance education was also affected by this phase and became an accepted model of education. The evolution of education into a digital platform has also brought unexpected problems, such as the increase in internet usage, the need for new software and devices that can connect to the Internet. Perhaps the most important of these problems is the management of the large amounts of data generated when all training activities are conducted remotely. Over the past decade, studies have provided important information about the quality of training and the benefits of distance learning. However, Big Data in distance education has been studied only to a limited extent, and to date no clear single solution has been found. In this study, a Distributed File Systems (DFS) is proposed and implemented to manage big data in distance education. The implemented ecosystem mainly contains the elements Dynamic Link Library (DLL), Windows Service Routines and distributed data nodes. DLL codes are required to connect Learning Management System (LMS) with the developed system. 67.72% of the files in the distance education system have small file size (<=16 MB) and 53.10% of the files are smaller than 1 MB. Therefore, a dedicated Big Data management platform was needed to manage and archive small file sizes. The proposed system was designed with a dynamic block structure to address this shortcoming. A serverless architecture has been chosen and implemented to make the platform more robust. Moreover, the developed platform also has compression and encryption features. According to system statistics, each written file was read 8.47 times, and for video archive files, this value was 20.95. In this way, a framework was developed in the Write Once Read Many architecture. A comprehensive performance analysis study was conducted using the operating system, NoSQL, RDBMS and Hadoop. Thus, for file sizes 1 MB and 50 MB, the developed system achieves a response time of 0.95 ms and 22.35 ms, respectively, while Hadoop, a popular DFS, has 4.01 ms and 47.88 ms, respectively.

Due to the large use of MRI in hospitals, large storage areas are needed to store these images. Also, if you want to access these images over the system repeatedly, a large bandwidth is required. To solve this problem, it will be necessary to compress and store the medical imaging system quickly and without disruption. It has been seen that in the studies made on MRIs, the non-used regions (NON-ROI) occupy a large space and the image size can be reduced significantly when the unnecessary area in the image is cleaned. In this method developed with CUDA, the region of interest (ROI) in the MRI is detected by sending a 3x3 Kirsch filter matrix to the CUDA cores and the NON-ROI region is extracted from the image with CUDA. These operations are first executed by the serial application on CPU, then by a parallel application on GPU. As a result, the application running on the GPU produced 34 times faster results than the application on the CPU. When images are compressed with this new improved method, it takes up 89% less than the original image size and 15% less than the original compressed image.

Software process models have been developed since 1968. When software process models are implemented in the software sector, it is considered that more suitable projects will be developed in terms of customer satisfaction and cost. In this study, the use of Agile, which is a frequently used software process model, in industry is investigated. In the research, software process models are explained and industrial sectors using Agile method are examined. It has been observed that customer satisfaction, time saving, a project success increase in sectors where Agile method is applied. Likewise, the project has also increased efficiency and competence.