ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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Burning Skin Detection System in Human Body
Get PDFEarly accurate burn depth diagnosis is crucial for selecting the appropriate clinical intervention strategies and assessing burn patient prognosis quality. However, with limited diagnostic accuracy, the current burn depth diagnosis approach still primarily relies on the empirical subjective assessment of clinicians. With the quick development of artificial intelligence technology, integration of deep learning algorithms with image analysis technology can more accurately identify and evaluate the information in medical images. The objective of the work is to detect and classify burn area in medical images using an unsupervised deep learning algorithm. The main contribution is to developing computations using one of the deep learning algorithm. To demonstrate the effectiveness of the proposed framework, experiments are performed on the benchmark to evaluate system stability. The results indicate that, the proposed system is simple and suits real life applications. The system accuracy was 75%, when compared with some of the state-of-the-art techniques
Detecting Deepfakes with Deep Learning and Gabor Filters
Get PDFThe proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue color information. The purpose of this paper is to give the reader a deeper view of (1) enhancing the efficiency of distinguishing fake facial images from real facial images by developing a novel model based on deep learning and Gabor filters and (2) how deep learning (CNN) if combined with forensic tools (Gabor filters) contributed to the detection of deepfakes. Our experiment shows that the training accuracy reaches about 98.06% and 97.50% validation. Likened to the state-of-the-art methods, the proposed model has higher efficiency
Human Body Posture Recognition Approaches: A Review
Get PDFHuman body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential hardware technologies are used in posture recognition systems. These systems capture and collect datasets through accelerometer sensors or computer vision. In addition, this paper presents a comparison study with state-of-the-art in terms of accuracy. We also present the advantages and limitations of each system and suggest promising future ideas that can increase the efficiency of the existing posture recognition system. Finally, the most common datasets applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202
Medicinal Plants Traditionally Used in the Management of COVID-19 in Kurdistan Region of Iraq
Get PDFCoronaviruses are infectious respiratory tract illnesses, but they can also affect the digestive tract and infect both humans and animals. The new coronavirus results in complicated health problems all over the world. The most urgent concern of all researchers around the world has been the treatment of the virus. The following study aimed to use quantitative ethnobotany to help scientist in addressing the deadly virus. Expert sampling method was adopted with the aid of an in-depth interview guide. Thirty-nine respondents were interviewed. Eighty-one medicinal plant species from 35 families were documented. Males 25 (64.1%) constitute the greater percentage of the total respondents. Majority of the respondents had formal education. Eighty-one medicinal plant species from 35 families were documented. Leaves are the most utilized 25.8 followed by seed 17.7 and fruits 12.1%, respectively. Relative frequency of citation ranged from 0.5 to 0.9, whereas the FL value ranged from 0.4 to 0.85, revealing how effective the documented plant species are in the management of COVID-19 in the region. A greater amount of research into documented medicinal plants is warranted because of the high likelihood that they contain many active ingredients
Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements
Get PDFK-nearest neighbor (KNN) is a lazy supervised learning algorithm, which depends on computing the similarity between the target and the closest neighbor(s). On the other hand, min-max normalization has been reported as a useful method for eliminating the impact of inconsistent ranges among attributes on the efficiency of some machine learning models. The impact of min-max normalization on the performance of KNN models is still not clear, and it needs more investigation. Therefore, this research examines the impacts of the min-max normalization method on the regression performance of KNN models utilizing eight different similarity measures, which are City block, Euclidean, Chebychev, Cosine, Correlation, Hamming, Jaccard, and Mahalanobis. Five benchmark datasets have been used to test the accuracy of the KNN models with the original dataset and the normalized dataset. Mean squared error (MSE) has been utilized as a performance indicator to compare the results. It’s been concluded that the impact of min-max normalization on the KNN models utilizing City block, Euclidean, Chebychev, Cosine, and Correlation depends on the nature of the dataset itself, therefore, testing models on both original and normalized datasets are recommended. The performance of KNN models utilizing Hamming, Jaccard, and Mahalanobis makes no difference by adopting min-max normalization because of their ratio nature, and dataset covariance involvement in the similarity calculations. Results showed that Mahalanobis outperformed the other seven similarity measures. This research is better than its peers in terms of reliability, and quality because it depended on testing different datasets from different application fields
An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method
Get PDFData normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset
Role of Laser Produced Silver Nanoparticles in Reversing Antibiotic Resistance in Some MultidrugResistant Pathogenic Bacteria
Get PDFSilver nanoparticles (Ag NPs) were produced through nanosecond laser in deionized water. These nanoparticles were characterized by UV–VIS spectrometer and transmission electron microscopy. VITEK®2 compact system was used to identify Escherichia coli (ESBL strain) and Staphylococcus aureus (MRSA strain) as multidrug-resistance (MDR) bacteria. The antibacterial activity of Ag NPs, ampicillin (AMP), and their combinations was tested against both bacterial isolates through standard microbiological culturing techniques. Our data show that both of E. coli and S. aureus were highly resistant to AMP. Ag NPs alone reduced growth in both bacterial isolates considerably. Growth declined drastically in both bacteria when AMP was used in combination with Ag NPs. The minimal inhibitory concentration of combined agents for E. coli was 20 µg/ml Ag NPs + 1 mg AMP/ml and for S. aureus was 10 µg/ml Ag NPs + 1 mg AMP/ml. The results show that the Ag NPs have great potentials in enhancing the antimicrobial activities of drugs that used to be ineffective against MDR bacteria. Administering combinations of antibiotic(s) with AgNPs may help in treating patients suffering from infections caused by MDR bacteria. Further in vivo and in vitro investigations are required to evaluate the side effects of these combinations
Investigation of Bacterial Persistence and Filaments Formation in Clinical Klebsiella pneumoniae: First Report from Iraq
Get PDFBacterial persistence is recognized as a major cause of antibiotic therapy failure, causing biofilms, and chronic intractable infections. The emergence of persisters in Klebsiella pneumoniae isolates has become a worldwide public health concern. The goal of the present study is to investigate the formation of persister cells beside filaments in Iraqi K. pneumoniae isolates. A total of fifty clinical K. pneumoniae isolates were collected from different clinical specimens and identified using the genotypic identification by using specific primer (rpoB gene) from housekeeping genes. Persister cells investigation is performed by exposure of stationary phase K. pneumoniae isolates to a high concentration of ciprofloxacin (×10 MIC) and counting the number of viable persister cells by CFU counts. Bacterial filament formation is detected and measured by light microscope scanning electron microscope. The results show the bility of these pathogenic bacteria to form persister cells to survive the bactericidal antibiotics and to cause chronic infection.Furthermore, persistent isolates have the ability to change in shape and size extensively, about 4 times increase in cell length than their normal length. These phenomena are possibly the initial stages of bacterial resistance prevalence
Extended-Spectrum β-lactamases and AmpC Production among Uropathogenic Isolates of Escherichia coli and Antibiogram Pattern
Get PDFEmergence of drug resistance in Escherichia coli due to various mechanisms makes the treatment choices very limited. The objective of this research was to investigate extended-spectrum beta-lactamases (ESBLs) and AmpC lactamases in E. coli isolates from urinary tract infections (UTIs) and to assess their antibacterial susceptibility patterns in a health-care context. Atotal of 70 E. coli isolates from clinically assumed cases of UTI patients during the 9months period. The isolates with bacteriuria (105 CFU/ml) were identified. ESBL and AmpC were detected phenotypically. Out of the 70 isolates of uropathogenic E. coli, ESBL production was detected in 34(48.6%) isolates and AmpC producer in 27(38.6%) of isolates in which 14(20%) of them showed coexistence phenotype of both ESBLs and AmpC and 23(32.9%) E. coli isolates were both ESBL and AmpC non-producer. The findings donated information regarding drug resistance. The level of resistance recorded in ESBL-and AmpC-producing uropathogenic E. coli of this study was raising; therefore, it is crucial to have a strict infection control measures and routine monitoring of ESBL-and AmpC-producing bacteria in clinical laboratory
Application of Experimental Design Methodology for Adsorption of Brilliant Blue onto Amberlite XAD-4/Agaricus campestris as a New Biocomposite Adsorbent
Get PDFThis research presents a new biocomposite adsorbents using response surface methodology (RSM) to find the best conditions for highest adsorption of Brilliant Blue G250 (BBG) from aqueous solution by Amberlite XAD-4/Agaricus campestris. The most effective parameters are determined by Plackett–Burman design (PBD) with specific ranges initial dye concentration (5–150 mg.L-1), temperature (20–50°C), contact time (5–100 min), pH (3–11), shaking speed (150–300 rpm), sample volume (5–75 mL), and adsorbent dosage (0.05–0.6 g). Then, in the second step, the optimum condition of effective factors is predicted using steepest ascent design. Finally, optimal medium conditions of effective parameters with central composite design are located. According to RSM, the best adsorbent amount, contact time, initial dye concentration, and sample volume for maximum removal% of BBG (96.72%) are 0.38 g, 60.78 min, 107.13 mg.L-1, and 28.6 mL, respectively. The adsorption of brilliant blue is approved by scanning electron microscopy. Under optimum conditions, it is concluded that XAD4/A. campestr is biocomposite is a suitable adsorbent for removing BBG from aqueous solution