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Successful medical treatment for patients with COVID-19 requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system to diagnose the virus on Chest X-Ray (CXR) images. CXR images are frequently used in healthcare as they offer the potential for rapid and accurate disease diagnosis. SARS-CoV-2 targets the respiratory system, resulting in pneumonia with additional symptoms, such as dry cough, fatigue, and fever, which could be misdiagnosed as pneumonia, TB, or lung cancer. There is difficulty in differentiating the features of COVID-19 from other diseases that have similarities in CXR images. Automated Computer-Aided Diagnosis (CAD) systems incorporate machine or deep learning methods to improve efficiency and accuracy. CNNs are among the most widely used methods, as they have shown encouraging accuracy in identifying COVID-19 in CXR images. This study presents a hybrid deep learning model to provide faster diagnosis of COVID-19 infection using CXR images. The Densenet201 model was used for feature extraction and a Multi-Layer Perceptron (MLP) was used for classification. The proposed method achieved 98.82 % accuracy and similar sensitivity, specificity, precision, recall, and F1 score. These results are promising when compared to other DL models trained in similar datasets. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryption (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme outperforms existing ones, reducing server-side search costs significantly. [ABSTRACT FROM AUTHOR]

Copyright of Jornal of Biotechnology Research Center is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The high rate of microbes and their biological activity in the patient's mouth is a concern in the domains of dental caries and periodontal disease. The study aimed to shed light on the relationship between graphene oxide's nanoparticles (nGOs) antimicrobial properties and the growth of dental pathogenic bacteria. The forty swab samples were frequently collected from the patient's cavity mouth between November 2019 and January 2020, from patients who visited dentist clinics in Baghdad by taking swabs from mouth cavities with various dental caries with two age groups (5–17) and (18–60) from male and female to streaking them on Brain–Heart Infusion (BHI) agar, then identified by re-streaking on Mitis Salivarius Bacitracin (MSB) agar. All isolates were confirmed as Streptococcus mutans after API 20 Strep method. As well as the Colony Forming Units (CFU) were then determined after diluting the bacterial cell suspensions to obtain cell samples containing 1.5 × 108 CFU/ ml. The collagen-binding adhesin (cnm) and glucosyltransferases (gtf) of S. mutans genes were identified using polymerase chain reaction (PCR) method before and after exposure to the nGOs, which were prepared in different pulse laser energy (500, 600, and 700 mJ) with presence and absence of the magnetic field, and the data have been analyzing. After counting the CFU, the nGOs shows high effectiveness inhibiting the growth of S. mutans. This research provides definitive answers about the relationship between nGOs, antibacterial caries, and periodontal disease. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) was a new concept that connected various physical things to the internet. The IoT is fast expanding and will soon significantly impact everyday life. Although the increasing number of connected IoT devices is simplifying life, they risk our data. Radiofrequency identification (RFID) assists in the automated identification of connected IoT devices. Furthermore, the primary challenges for RFID tag-connected technology are privacy and security. With the improving safety of solutions RFID for many apps, RFID necessitates a centralized database widening. Blockchain techniques are speedily establishing themselves together as a new distributed and decentralized replacement. And it provides enhanced transparency, data security, immutability, dependability, and lower maintenance costs. Because of its inherent benefits, RFID is projected to play a significant part in enabling identifying technology on the IoT. However, due to its association with sensor technology, it has the potential to be applied in a wide range of industries. Security, however, is one of the more complex components of creating an RFID system. RFID Security focuses around authentication and privacy concerns. From the previous works, we found that the computing cost and uptime are higher. To solve these problems, we offer improving lightweight authentication using Arnold Chaotic map and Markov chain for IoT applications. The proposed method uses random algorithms (Markova chain, Arnold map chaotic, and ECC), which add high security. We also used blockchain technology to transfer keys through a non-security channel. Based on parameters such as (communication cost, throughput rate, storage requirements, transmission delay, and computational cost) the results of the proposed RFID protocol were compared with several RFID-based security solutions. As a result, the proposed lightweight scheme is more efficient and secure than existing RFID systems in terms of performance, according to simulation results, and is fully compatible with actual applications. [ABSTRACT FROM AUTHOR]

The design of a cipher system is essential and required with the development of the technology era. The internet of things concept is modern technology requiring lightweight, secure algorithms to investigate the requirement of the light device. Furthermore, IoT devices have limited resources (IoT devices should minimize battery life, limited memory size, and response time). A stream cipher system is faster than a block cipher system and has an easy feature with hardware and software implementation. This paper designs a lightweight authentication encryption algorithm based on stream cipher and chaotic maps with sponge structure for IoT applications. Currently, the suggested approach uses text data for encryption and authorization. Additionally, the suggested algorithm will be able to use it with image data in the future. Analysis results of the proposed algorithm based on NIST randomness tests, execution time, memory space, and functional features. The results are compared with other previous works which used sponge structure (e.g. Ascon, Elephant, ISAP, Photon-Beetle, and Xoodyak, etc.). Randomness tests show that the proposed system is random and secure with investigate all the functional features except the parallelization feature which will work it in the future. And also it has good fast speed and less memory space. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attention Deficit Hyperactivity Disorder (ADHD) is a common and heritable disease that has an environmental influence on brain function. The diseases affects multiple aspects of the lives of college students, not only on their study but also on the relationships with other people. The problem with ADHD attention involves short term memory. The purpose of this paper is to investigate the capability of improving short term working memory for ADHD patients by the aid of technology a proper VR environment is built for ADHD, who are isolated from the real circumference. Electroencephalography (EEG) is taken as biofeedback to read the brain signal from the patient. A deep learning approach and an artificial neural network method, are employed to efficiently and accurately process EEG. The findings of the trial indicate that the virtual reality recommended system will play a greater role in improving the attention to the ADHD patient. [ABSTRACT FROM AUTHOR]

Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption. [ABSTRACT FROM AUTHOR]

The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image's features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola's approach, T.R Singh's approach, Niblack's approaches, Bernsen's approach Bruckstein's and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image encryption has become an important application aspect of information security. Most attempts are focused on increasing the security aspect, the quality of the resulting image, and the time consumed. On the other hand, dealing with the color image under the spatial domain in this filed is considered as another challenge added to the proposed method that make it sensitivity and difficulty. The proposed method aims to encode a color image by dealing with the main color components of the red (R), green (G), and blue (B) components of a color image to strengthen the dependence of each component by modifying a two dimensional logistic-sine coupling map (2DLSCM). This is to satisfy the statistical features and reduce timeconsumption, and benefit from a mixing step of the second of advanced encryption standard (AES) candidates (serpent block cipher) and modified it to achieve in addition of confusion and diffusion processes. The experimental results showed that our proposed method had the ability to resist against statistical attacks and differential attacks. It also had a uniform histogram, a large key space, complex and faster, closer Shannon entropy to 8, and low correlation values between two adjacent pixels compared with other methods. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tuberculosis is known to be the second most common cause from substantial morbidity and mortality through infectious diseases worldwide following the human HIV Sample selection was performed at the Chest and Respiratory Diseases Institute / Baghdad Medical City in Bagdad. The samples collecting were done from January to October 2019, at which time 90 suspected TB patients were investigated. The results revealed when comparing between RT-PCR test and microbiological test we have calculated sensitivity and specificity for each test. The RT-PCR test showed 100 percent for both sensitivity and specificity while the Microbiological tests showed 0.067 specificity and 0.933 sensitivity. These findings our results showed a high sensitivity and specificity of RT-PCR technique targeting BfrB gene for the detection of MTB by using specific probe. [ABSTRACT FROM AUTHOR]

Background: Aspirin is one of the most frequently used and cheapest drugs in medicine. Since its first synthesis in 1897, several medicinal roles and mechanisms of action of Aspirin have become apparent, the latest among these being its role in cancer prevention and treatment. Objective: We present a review of Aspirin's biochemistry and pharmacology, as well as the clinical use of Aspirin. The communiqué also suggests possible strategies for maximizing the gain of Aspirin as a wonder-drug of the future. Methods: The literature search strategy covered printed and online sources, including manual library search (PubMed), Embase, Medline, and Cochrane Library. For papers written in English and published in the last ten years. A systematic analysis of available data was subsequently performed based on the review questions. An estimated 155 articles were found online, and twenty-eight articles utilized in the final analysis. Discussion: Aspirin belongs to the non-steroidal anti-inflammatory drugs with a wide range of pharmacological activities, including analgesic, antipyretic, and antiplatelet properties. Discovery of antiplatelet effects led to the increasing use of Aspirin as an anti-thrombotic agent in the prevention of cardiovascular diseases from the 1980s, and firm evidence supporting its usefulness has continued to accumulate. Aspirin irreversibly inhibits platelet function by acetylating cyclooxygenase (COX), which is involved in the production of a potent platelet stimulator, thromboxane A2. The inhibition of COX-2 by Aspirin forms the basis of its anticipated role in preventing colorectal cancer and Alzheimer's disease and the inhibition of the progression of these diseases. It has been pointed out that the incidence of cardiovascular events tends to be high among patients who are Aspirin resistant, but the reason for this increased incidence remains unclear. Conclusion: The emerging future interest is to accrue evidence in favor of Aspirin as the novel therapeutic drug for combat ing severe acute inflammation and thrombosis associated with the cytokine storm in COVID-19 patients. Notably, a randomized clinical trial, to test a range of potential treatments for COVID-19, includes low-dose Aspirin as anti-inflammatory and antiplatelet treatment. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal for Pharmaceutical Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Staphylococcus aureus is bacterium present in skin and upper respiratory tract. Although S.aureus is normal flora also become opportunistic pathogen cause many infection that difficulty treatment because of presence virulence gene. The purpose of this study is to screen the antibiotic susceptibility patterns and the prevalence of lmrS, mepA, nor Cgenes among bacterial isolates. In the present study, 213 samples were collected from different clinical sources from patients who admitted different hospitals in Baghdad city in period ranged 23/11/2018-18/3/2019. The isolates were diagnosed as S.aureus based on routine bacteriological methods and confirmed by molecular method using 16s rRNA gene. The antibiotic susceptibility test was performed to all identified isolates by Kirby-Bauer Disk Diffusion method using sixteen types of antibiotics. The results of antibiotics susceptibility test revealed high level of resistance toward Azithromycin80%, Erythromycin and Cefoxitin82%, meropenem and ceftriaxone 84% cefotaxime and colistin 100%, methicillin 76%, mecillinam 87% respectively. Finally, the prevalence of lmrS gene among bacterial isolates was detected as 98%, norC 100% and mepA 95%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of College of Education is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sixty specimens of African sharptooth catfish Clarias gariepinus (Clariidae) were collected during the period of March 2014-February 2015 from boat landing sites and fishermen of the River Nile at Beni Suef Governorate, Egypt, and examined for the presence of digenean parasites. The morphology of these parasites was studied by using light microscopy to describe morphological and morphometrically measurements for different body parts. Three digenean species belonging to three different genera were collected from various organs within the examined fish species. A certain degree of site specificity was also observed, with Thaparotrema botswanensis being found only in the gall bladder of C. gariepinus, while both Pseudoholorchis clarii and Glossidium pedatum were found only in the intestine and thus seem to occupy a certain niche within their host. This study represent as the first record of T. botswanensis and G. pedatum from C. gariepinus, as well as the first report of the genera from the River Nile in Egypt. In addition, re-description of P. clarii clarifies measurements for some body parts. [ABSTRACT FROM AUTHOR]