Hands-on laboratory experiments are a vital part of engineering and physical sciences education, having a strong impact on students' learning outcomes. With the increased usage of electronic kits in the educational laboratories, and the need for training a huge number of students on these kits, it is imperative to enable to remote access to a physical laboratory, either as part of an on-site or distance learning course. In addition to the convenience provided to students, there are also cost and safety related benefits. Many institutions can not afford the expensive equipment provided in a physical lab. Such laboratories are very convenient and effective for learning hardware design concepts and elaborating hardware-based graduation projects. This paper addresses the hardware (HW) and software (SW) tools necessary for building a general framework for remote laboratory access. The proposed system allows performing experiments remotely across the Internet via web interface as well as locally in the classroom. In addition, it introduces enhancements for the remote lab activities leading to improving its performance and makes the data transfer more secure.
Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.
Fishing has become a major threat to marine fishes. Effective conservation requires timely identification of vulnerable fish species. However, evaluation of extinction risk using conventional methods is difficult due to limitations in data that should be gathered about the fish species and required by such methods. This paper presents a fuzzy expert system that integrates life history and ecological characteristics of marine fishes to estimate their intrinsic vulnerability. There are lots of general and special purpose expert systems that help society in a life particular sector. So, a professional one is selected and adapted for helping in marine wealth preservation. Finally, the proposed fuzzy expert system is used as a decision support tool in fishery management and marine conservation planning.
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