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 lung CT-scans into two groups (COVID-19 and NonCOVID-19) had been proposed. A dataset used is 960 slices of CT scan collected from Iraqi patients /Ibn Al-Nafis teaching hospital. The performance metrics are used in this study (accuracy, recall, precision, and F1 scores). The results indicate that the proposed approach generated a high-quality model for the collected dataset, with an overall accuracy of 98.95% and an overall recall of 97 %.
The significant shift in the Fine Arts, who led the avant-garde movements and creative which appeared in the modern, specifically in the mid-nineteenth century and early twentieth century era path, embodied in a clear show of new aesthetic value rejects traditional methods put in shape and color qualities She described the movements subjectivity or no formal, was intended to determine the direct function of expression and speech language in the visual arts away from the representation and simulation of reality was accompanied by the appearance of those fine movements directed towards a global approach to design carries features and clear in its organizations formalism of the most important move away from the symmetry and the adoption of
... Show MoreNowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of
... Show MoreSoftware Defined Network (SDN) is a new technology that separate the control plane from the data plane. SDN provides a choice in automation and programmability faster than traditional network. It supports the Quality of Service (QoS) for video surveillance application. One of most significant issues in video surveillance is how to find the best path for routing the packets between the source (IP cameras) and destination (monitoring center). The video surveillance system requires fast transmission and reliable delivery and high QoS. To improve the QoS and to achieve the optimal path, the SDN architecture is used in this paper. In addition, different routing algorithms are used with different steps. First, we eva
... Show MoreCOVID-19 is a unique viral infectious illness that causes a variety of symptoms and health hazards, particularly to the respiratory system and has been declared a worldwide pandemic. The disease is characterized by a cytokine release in severe conditions. Interleukin-6 (IL-6), a proinflammatory cytokine, mediates an important immunomodulatory process. Also, vitamin D was identified to have a role in the innate immunity of individuals. Our study was designed to find the role of IL-6 and vitamin D in COVID-19 patients, as well as, to see whether there is a link between vitamin D deficiency and cytokine syndrome development. The study included 90 COVID-19 patients and 30 control people from Baghdad, Iraq. The age of the participants was non-s
... Show MoreAn aircraft's landing stage involves inherent hazards and problems associated with many factors, such as weather, runway conditions, pilot experiences, etc. The pilot is responsible for selecting the proper landing procedure based on information provided by the landing console operator (LCO). Given the likelihood of human decisions due to errors and biases, creating an intelligent system becomes important to predict accurate decisions. This paper proposes the fuzzy logic method, which intends to handle the uncertainty and ambiguity inherent in the landing phase, providing intelligent decision support to the pilot while reducing the workload of the LCO. The fuzzy system, built using the Mamdani approach in MATLAB software, considers critical
... Show MoreWithin this paper, we developed a new series of organic chromophores based on triphenyleamine (TPA) (AL1, AL-2, AL-11 and AL-22) by engineering the structure of the electron donor (D) unit via replacing a phenyle ring or inserting thiophene as a π-linkage. For the sake of scrutinizing the impact of the TPA donating ability and the spacer upon the photovoltaic, absorptional, energetic, and geometrical characteristic of these sensitizers, density functional theory (DFT) and time-dependent DFT (TD-DFT) have been utilized. According to structural characteristics, incorporating the acceptor, π-bridge and TPA does not result in a perfect coplanar conformation in AL-22. We computed EHOMO, ELUMO and bandgap (Eg) energies by performing frequency a
... Show MoreThis study aims to find the chemosensitive dysfunction incidence in COVID-19-positive patients and its recovery.
We collected the data from sixty-five patients, all COVID-19 positive, quarantined in-hospital between 5 April 2020 and 17 May 2020, by a questionnaire distributed in the quarantine ward.
Smell dysfunction appeared in 89.23% with or without other symptoms of COVID-19. 39.66% of them recovered the sense of smell. Taste dysfunction found in 83.08% patients with other COVID-19 symptoms. Only 29.63% of them recovered. The recovery took 1–3 weeks, and most