This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits.

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The purpose of the study reported on here was to introduce the perceptions of senior academic administrators in the Northern Cyprus Ministry of Education on the structure of the current education system as a whole. In order to carry out this case study, the views of 14 senior academic administrators were obtained through semi-structured interviews. There is no doubt that in qualitative research semi-structured interviewing is a flexible and powerful tool to capture the voices and the ways in which people make meaning of their experiences (Kvale, 2007). As Yin (2009:18) states: “An empirical inquiry about contemporary phenomena (e.g., ‘case’), set within its own real-world context – especially when the boundaries between phenomenon and context are not clearly evident.” Therefore, by reflecting on the current education system in Northern Cyprus as a case, we tried to show the real context of the education system itself. The data collected from the semi-structured interviews were analysed through content analysis. According to the findings of this research study, the current education system must be reconstructed considering the curriculum, strategies in teaching and learning approaches, developing of collaborative and student-centred classrooms, applying active learning strategies and voicing the voices of the senior academic administrators during the decision-making process.

Deep learning (DL) is an effective method for medical object detection. Studies show that deep networks can achieve accuracy in medical segmentation and detection tasks. This is due to the depth and training methods of deep networks which allows them to derive different levels of abstractions of input mages. In this paper, the left ventricle detection task is carried out using a deep network called stacked auto-encoder (SAE). The networks take off this task as a binary classification task wherein left and non-left ventricles cropped images are being recognized by the SAE. Once the network recognizes left and non-left ventricles, the whole task starts by initiating a sliding window that moves through the whole magnetic resonance (MR) slice till a left ventricle is detected. Experimentally, the network showed effective detection performance when target images are noisy as it is seen that it can detect left ventricles in target images with up to 10% of salt and pepper noise.