sampling

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

Today, there is a growing trend towards qualitative research in different areas of academic investigations. However, students face difficulties in deciding on qualitative methods of research, because most of the literature on qualitative research focuses on the philosophical and theoretical aspects, and they hardly offer any practical guidelines. This problem is rooted in the essence of qualitative research, which makes the details of the research process unachievable at the beginning. As a result, the features of the sampling plan—like the accurate size and form of the sampling—remain uncertain until after the data has been gathered, and this makes the jury doubtful about the approval of the research proposal. Hence, framing an appropriate and assayable plan for sampling can be of great help in the approval and execution of qualitative research plans. From this perspective, the present article aims to design a framework for a sampling plan in qualitative researches using theoretical sources of the grounded theory method and researchers’ suggestions based on practical involvement, as well as the recent experience of the authors in grounded theory research. By studying these sources, and considering the feedback for and practical involvement in the grounded theory method, a two-stage plan of sampling is proposed. The initial stage aims to ensure adequate diversity for the appropriate distribution of the samples, and the second, as a theoretical sampling stage, guarantees the theoretical attribute of sampling in qualitative research.