Atheer Hadi Issa Alrammahi

The world has experienced a health crisis with the outbreak of the COVID-19 virus. The mask has been identified as the most effective way to prevent the spread of the virus. This has led to the need for a face mask recognition device that not only detects the presence of the mask but also provides the accuracy with which a person is wearing the face mask. In addition, the face mask should also be recognized from all angles. The project aims to create a new and improved real-time face mask recognition tool using image processing and computer vision approaches. A dataset consisting of images with and without a mask was used. For the purposes of this project, a pre-trained MobileNetV2 convolutional neural network was used. The performance of the given model was evaluated. The model presented in this project can detect the face mask with an accuracy of 99.21%. The face mask recognition tool can effectively detect the face mask in the side direction, which makes it more useful. The optimization function which contains the learning loops and the optimization function are also used.

Real-time image classification is one of the most challenging issues in understanding images and computer vision domain. Deep learning methods, especially Convolutional Neural Network (CNN), has increased and improved the performance of image processing and understanding. The performance of real-time image classification based on deep learning achieves good results because the training style, and features that are used and extracted from the input image. This work proposes an interesting model for real-time image classification architecture based on deep learning with fully connected layers to extract proper features. The classification is based on the hybrid GoogleNet pre-trained model. The datasets that are used in this work are 15 scene and UC Merced Land-Use datasets, used to test the proposed model. The proposed model achieved 92.4 and 98.8 as a higher accuracy.

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In this paper, “Building a common concept of analytical services for analyzing structured data” was proposed to build an analytical service to provide forecasts, descriptive and comparative data summaries using modern Microsoft technologies. This service will allow users to perform flexible viewing of information, receive arbitrary data slices and perform analytical operations of drill-down, convolution, pass-through distribution, the comparison in time. With the help of data mining, it is possible to detect previously unknown, non-trivial, practically useful and accessible interpretations of knowledge that are necessary for the organization's decision-making. Also, each client can interact with the service and thus monitor the displayed analytical information. In the process of work the following tasks were solved: investigated the subject area; studied materials relating to systems and technologies for their implementation; designed service architecture and applications to configure the service; selected technologies and tools for the implementation of the system; implemented the main frame of the system; modules for interaction with analysis services, data mining (a priori algorithm) and partially a module of neural networks; a report was written and a presentation of the results was prepared; The developed service will be useful to all organizations that are interested in obtaining analytical reports and other previously unknown information on their accumulated data. For example, organizations can analyze the impact of advertising, customer segmentation, search for signs of profitable customers, analyze product preferences, forecast sales volumes, and more.