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).
An automatic text summarization system mimics how humans summarize by picking the most significant sentences in a source text. However, the complexities of the Arabic language have become challenging to obtain information quickly and effectively. The main disadvantage of the traditional approaches is that they are strictly constrained (especially for the Arabic language) by the accuracy of sentence feature functions, weighting schemes, and similarity calculations. On the other hand, the meta-heuristic search approaches have a feature tha
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Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti
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OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtain
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In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
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Aim: To evaluation the effect of Lactobacillus acidophilus on Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 with detection of some virulence factors. Methods: Two hundred and fifty specimens (stool) from children under five years for both sexes were collected from some hospitals. All isolates were diagnosed according to morphological characteristics, biochemical tests. Monoplex pattern of PCR was used also for detection different genes in (7) Escherichia coli )O157:H7 (isolates; include 16SrRNA, eae, lifA, Stx1,Stx2 that encoded for ribosomal RNA, intimin, lymphocyte inhibitory factor, shiga toxins. Three types of probiotics strains were obtained, Lactobacillus fermentum, Lactobacillus plantarum and Lactobacillus acidophilus (A
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The simulation study has been conducted for the harmonics of Nd: YAG laser, namely the second harmonic generation SHG, the third harmonic generation THG, and the fourth harmonic generation FHG. Determination of beam expander's expansion ratio for specific wavelength and given detection range is the key in beam expander design for determining minimum laser spot size at the target. Knowing optimum expansion ratio decreases receiving unit dimensions and increases its performance efficiency. Simulation of the above mentioned parameters is conducted for the two types of refractive beam expander, Keplerian and Galilean. Ideal refractive indices for the lenses are chosen adequately for Nd: YAG laser harmonics wavelengths, so that increasing transm
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