Problem statement: Image recognition is a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination, chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of interest may be as an approach for dealing with such application. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. Approach: In this study, our aim was to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on size and shape measurements, that were extracted by measuring the distance and geometrical measurements. Results: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image features extraction is performed relying on size and shape measurements. Our system has been applied on 20 different fish families, each family has a different number of fish types and our sample consists of distinct 350 of fish images. These images were divided into two datasets: 257 training images and 93 testing images. An overall accuracy was obtained using the neural network associated with the back-propagation algorithm was 86% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a features extraction method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently without any problems. Eventually, the classifier is able to categorize the given fish into its cluster and categorize the clustered fish into its poison or non-poison fish and categorizes the poison and non-poison fish into its family.
Following the outbreak of the novel coronavirus (COVID-19) in China in late December 2019, more than 217 countries became almost immediately infected in the resulting pandemic. Consequently, many of them decided to close their educational institutions as a way of preventing the spread of this virus. For many of them, though, the closure made them unable to deliver learning materials to students owing to their inability to provide the right technology for the purpose. To assist with the digitalizing of learning during this time, this study reviews the most common technologies used in the delivery of learning materials, with the experience of most infected countries being considered. Major challenges in online learning are discussed in this study as well. Further, Saudi Arabia was considered as a case study for the effectiveness of distance learning during the 2020 spring semester, where 300 undergraduate students were surveyed on their opinions of distance learning. The responses to the survey indicated that distance learning was effective in providing the required knowledge to the students during the outbreak of COVID-19. The findings showed that although the lack of interaction and poor internet connections were factors affecting comfortable and successful learning of physics and mathematics, 63% of students were satisfied with learning management systems, 75% of students found it easy to understand course materials, and 67% of students found it easy to understand assignments and could deal with them comfortably. The study findings can encourage educational institutions to digitalize their learning materials in the future.
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