Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You only look once”) neural network algorithm, which is an efficient real-time object identification algorithm, an intelligent system was developed in this thesis to distinguish which faces are wearing a mask and who is not wearing a wrong mask. The proposed system was developed based on data preparation, preprocessing, and adding a multi-layer neural network, followed by extracting the detection algorithm to improve the accuracy of the system. Two global data sets were used to train and test the proposed system and worked on it in three models, where the first contains the AIZOO data set, the second contains the MoLa RGB CovSurv data set, and the third model contains a combined data set for the two in order to provide cases that are difficult to identify and the accuracy results that were obtained. obtained from the merging datasets showed that the face mask (0.953) and the face recognition system were the most accurate in detecting them (0.916).
Steganography is an important class of security which is widely used in computer and network security nowadays. In this research, a new proposed algorithm was introduced with a new concept of dealing with steganography as an algorithmic secret key technique similar to stream cipher cryptographic system. The proposed algorithm is a secret key system suggested to be used in communications for messages transmission steganography
استهدف البحث تشخيص وتحليل الاسباب التي تجعل المرأة العراقية أكثر تأثراً من الرجل بعوامل التغيرات المناخية، فضلاً عن زيادة الوعي بقضية المرأة في هذا المجال، لأن موضوع دمجها في صياغة السياسات والاستراتيجيات المتعلقة بالتغير المناخي أصبح قضية عالمية، ويحتل مرتبة متقدمة في مؤشرات تحقيق أهداف التنمية المستدامة، وأستخدمت الباحثة المنهج الوصفي والتحليلي والاحصائي في تحليل محاور البحث، وتوصل البحث الى إستن
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
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Fusidic acid (FA) is a well-known pharmaceutical antibiotic used to treat dermal infections. This experiment aimed for developing a standardized HPLC protocol to determine the accurate concentration of fusidic acid in both non-ionic and cationic nano-emulsion based gels. For this purpose, a simple, precise, accurate approach was developed. A column with reversed-phase C18 (250 mm x 4.6 mm ID x 5 m) was utilized for the separation process. The main constituents of the HPLC mobile phase were composed of water: acetonitrile (1: 4); adjusted at pH 3.3. The flow rate was 1.0 mL/minute. The optimized wavelength was selected at 235 nm. This approach achieved strong linearity for alcoholic solutions of FA when loaded at a serial concentrati
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In recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of how the
... Show MoreAmong the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On the
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Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.
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