Emergence 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. A total of 70 E. coli isolates from clinically assumed cases of UTI patients during the 9 months 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.
{"title":"The Coexistence of extended spectrum β lactamases and AmpC production among uropathogenic isolates of Escherichia coli and its antibiogram pattern","authors":"Aryan R. Ganjo","doi":"10.14500/aro.10898","DOIUrl":"https://doi.org/10.14500/aro.10898","url":null,"abstract":" \u0000 Emergence 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. A total of 70 E. coli isolates from clinically assumed cases of UTI patients during the 9 months 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.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80706675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study was undertaken to investigate the influence of static magnetic field (SMF) on bone marrow cellular density (BMCD) variation proportionally to bone trabeculae. Female albino Wistar rats exposed with 2.4 ± 0.2 millitesla for 1–4 weeks duration continuously versus 1 h, 2 h, 6 h, and 8 h/day. Trephine biopsy of femurs bone was examined under optical microscope. Data analyzed with ImageJ software. Results showed that short time exposure per day did not enhance the BMCD compare to high exposure period/ day. Six hours/day exposure during 1 week increased the marrow cellular density (hypercellularity) significantly (P ≤ 0.05) compares to bone trabeculae. Contrarily, 8 h/day exposure reduced the BMCD slightly and significantly (hypocellularity, about 50% reduction) due to 1 week and 4 weeks exposure duration, respectively. The SMF has associated bone marrow cellularity tendency of rat’s femur.
{"title":"Effect of Static Magnetic Field on Bone Marrow Cellular Density","authors":"Bestoon T. Mustafa, Sardar P. Yaba, A. Ismail","doi":"10.14500/aro.10946","DOIUrl":"https://doi.org/10.14500/aro.10946","url":null,"abstract":" \u0000 This study was undertaken to investigate the influence of static magnetic field (SMF) on bone marrow cellular density (BMCD) variation proportionally to bone trabeculae. Female albino Wistar rats exposed with 2.4 ± 0.2 millitesla for 1–4 weeks duration continuously versus 1 h, 2 h, 6 h, and 8 h/day. Trephine biopsy of femurs bone was examined under optical microscope. Data analyzed with ImageJ software. Results showed that short time exposure per day did not enhance the BMCD compare to high exposure period/ day. Six hours/day exposure during 1 week increased the marrow cellular density (hypercellularity) significantly (P ≤ 0.05) compares to bone trabeculae. Contrarily, 8 h/day exposure reduced the BMCD slightly and significantly (hypocellularity, about 50% reduction) due to 1 week and 4 weeks exposure duration, respectively. The SMF has associated bone marrow cellularity tendency of rat’s femur.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80197468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K-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.
k -最近邻(KNN)是一种懒惰监督学习算法，它依赖于计算目标与最近邻之间的相似度。另一方面，据报道，最小-最大归一化是一种有用的方法，用于消除属性之间不一致范围对某些机器学习模型效率的影响。最小-最大归一化对KNN模型性能的影响尚不清楚，需要进一步研究。因此，本研究利用8种不同的相似性度量，即City block、Euclidean、Chebychev、Cosine、Correlation、Hamming、Jaccard和Mahalanobis，考察了最小-最大归一化方法对KNN模型回归性能的影响。用5个基准数据集对原始数据集和归一化数据集的KNN模型的准确性进行了测试。均方误差(MSE)被用作比较结果的性能指标。综上所述，最小-最大归一化对使用City block、Euclidean、Chebychev、Cosine和Correlation的KNN模型的影响取决于数据集本身的性质，因此，建议在原始数据集和归一化数据集上测试模型。使用Hamming, Jaccard和Mahalanobis的KNN模型的性能不会因为采用最小-最大归一化而产生差异，因为它们的比率性质和数据集协方差涉及相似性计算。结果表明，马哈拉诺比斯的相似性优于其他7种相似性指标。这项研究在可靠性和质量方面优于同行，因为它依赖于测试来自不同应用领域的不同数据集。
{"title":"Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements","authors":"Peshawa J. Muhammad Ali","doi":"10.14500/aro.10955","DOIUrl":"https://doi.org/10.14500/aro.10955","url":null,"abstract":" K-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.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84721644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human 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 2020
{"title":"Human Body Posture Recognition Approaches","authors":"M. Ali, A. Hussain, A. Sadiq","doi":"10.14500/aro.10930","DOIUrl":"https://doi.org/10.14500/aro.10930","url":null,"abstract":"Human 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 2020","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89496199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain-computer interface (BCI) is an evolving technology having huge potential for rehabilitation of patients suffering from disorders of the nervous system, besides many other nonmedical applications. Multichannel electroencephalography (EEG) is widely used to provide input signals to a BCI system. Significant research in methodology employed to implement different stages of BCI system, has led to discovery of new issues and challenges. The raw EEG data includes artifacts from environmental and physiological sources, which is eliminated in preprocessing phase of BCI system. It is then followed by a feature extraction stage to isolate a few relevant features for further classification to a particular motor imagery (MI) activity. A feature extraction approach based on spectrally weighted common spatial pattern (SWCSP) is proposed in this paper to improve overall accuracy of a BCI system. The reported literature uses SWCSP for feature extraction, as it has outperformed other techniques. The proposed approach enhances its performance by optimizing its parameters. The independent component analysis (ICA) method is used for detection and removal of irrelevant data, while linear discriminant analysis (LDA) is used as a classifier. The proposed approach is executed on benchmark data-set 2a of BCI competition IV. It yielded classification accuracy of 70.6% across nine subjects, which is higher than all the reported approaches.
{"title":"An Optimized SWCSP Technique for Feature Extraction in EEG-based BCI System","authors":"Navtej S. Ghumman, B. Jindal","doi":"10.14500/aro.10926","DOIUrl":"https://doi.org/10.14500/aro.10926","url":null,"abstract":"Brain-computer interface (BCI) is an evolving technology having huge potential for rehabilitation of patients suffering from disorders of the nervous system, besides many other nonmedical applications. Multichannel electroencephalography (EEG) is widely used to provide input signals to a BCI system. Significant research in methodology employed to implement different stages of BCI system, has led to discovery of new issues and challenges. The raw EEG data includes artifacts from environmental and physiological sources, which is eliminated in preprocessing phase of BCI system. It is then followed by a feature extraction stage to isolate a few relevant features for further classification to a particular motor imagery (MI) activity. A feature extraction approach based on spectrally weighted common spatial pattern (SWCSP) is proposed in this paper to improve overall accuracy of a BCI system. The reported literature uses SWCSP for feature extraction, as it has outperformed other techniques. The proposed approach enhances its performance by optimizing its parameters. The independent component analysis (ICA) method is used for detection and removal of irrelevant data, while linear discriminant analysis (LDA) is used as a classifier. The proposed approach is executed on benchmark data-set 2a of BCI competition IV. It yielded classification accuracy of 70.6% across nine subjects, which is higher than all the reported approaches. ","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89207407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Type 2 diabetes mellitus is the most common chronic endocrine disorder that affecting 5%–10% of adults globally. Recently, the disease has rapidly spread throughout the Kurdistan Region. This study investigates DNA methylation status in the ABCC8 gene among the study population, and it possibly used as a biomarker. One hundred and thirteen individuals were included in this study, and they were divided into three categories (47 diabetes, 36 prediabetic, and 30 controls). Blood samples were collected to investigate DNA methylation status in patients who attended private clinical sectors in Koya city, Kurdistan Region of Iraq, between August and December 2021. Methylation-specific PCR (MSP) uses paired primers for each methylated and unmethylated region. In addition, the X2 Kruskal–Wallis statistical and Wilcoxon signed-rank tests were run with a significance level of p 0.05. In comparison to the healthy group, hypermethylation of DNA is detected in the promoter region of diabetes and prediabetes. In addition, age, gender, BMI, alcohol use, family history, and physical activity all influence the degree of DNA methylation in people who have had coronavirus illness. The abovementioned findings suggest that DNA methylation alterations in the ABCC8 promoter region might be exploited as a possible predictive biomarker for type 2 diabetes mellitus diagnosis.
{"title":"Identification DNA Methylation Change of ABCC8 Gene in Type 2 Diabetes Mellitus as Predictive Biomarkers","authors":"Harem O. Smail, Dlnya A. Mohamad","doi":"10.14500/aro.10947","DOIUrl":"https://doi.org/10.14500/aro.10947","url":null,"abstract":"Type 2 diabetes mellitus is the most common chronic endocrine disorder that affecting 5%–10% of adults globally. Recently, the disease has rapidly spread throughout the Kurdistan Region. This study investigates DNA methylation status in the ABCC8 gene among the study population, and it possibly used as a biomarker. One hundred and thirteen individuals were included in this study, and they were divided into three categories (47 diabetes, 36 prediabetic, and 30 controls). Blood samples were collected to investigate DNA methylation status in patients who attended private clinical sectors in Koya city, Kurdistan Region of Iraq, between August and December 2021. Methylation-specific PCR (MSP) uses paired primers for each methylated and unmethylated region. In addition, the X2 Kruskal–Wallis statistical and Wilcoxon signed-rank tests were run with a significance level of p 0.05. In comparison to the healthy group, hypermethylation of DNA is detected in the promoter region of diabetes and prediabetes. In addition, age, gender, BMI, alcohol use, family history, and physical activity all influence the degree of DNA methylation in people who have had coronavirus illness. The abovementioned findings suggest that DNA methylation alterations in the ABCC8 promoter region might be exploited as a possible predictive biomarker for type 2 diabetes mellitus diagnosis.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74133130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The identical twins (Monozygotic) are siblings created from the division of one fertilized egg (zygote), so they will be identical in their genetic characteristics and therefore in their phenotypic traits to a very large extent. Among these traits is the voice or the voiceprint of these twins. This research aims to suggest a method to determine the extent of the similarity and the difference between the voiceprints between the brothers of the identical twins and thus, it is possible to distinguish between their voices. This study relied on using a number of audio clips collected from 35 identical twins. The proposed method is based on the use of the spectrogram that represents the voiceprint of the twins. The spectrogram is a two-dimensional function that can be used in the Neutrosophic Transformation to convert the voiceprints to the Neutrosophic domain represented by three membership functions (True, False, and Indeterminate). The results showed that the average extent of the similarity ratio between twins’ voices (True membership) is 67.6%, the difference ratio (False membership) is 32.3%, and the indeterminacy membership function ratio is 18.2%.
{"title":"Measuring the Voice Resemblance Extent of Identical (Monozygotic) Twins Using Voiceprints Neutrosophic Domain","authors":"Yazen A. Khaleel, Caroline Y. Daniel, S. Yahya","doi":"10.14500/aro.10925","DOIUrl":"https://doi.org/10.14500/aro.10925","url":null,"abstract":"The identical twins (Monozygotic) are siblings created from the division of one fertilized egg (zygote), so they will be identical in their genetic characteristics and therefore in their phenotypic traits to a very large extent. Among these traits is the voice or the voiceprint of these twins. This research aims to suggest a method to determine the extent of the similarity and the difference between the voiceprints between the brothers of the identical twins and thus, it is possible to distinguish between their voices. This study relied on using a number of audio clips collected from 35 identical twins. The proposed method is based on the use of the spectrogram that represents the voiceprint of the twins. The spectrogram is a two-dimensional function that can be used in the Neutrosophic Transformation to convert the voiceprints to the Neutrosophic domain represented by three membership functions (True, False, and Indeterminate). The results showed that the average extent of the similarity ratio between twins’ voices (True membership) is 67.6%, the difference ratio (False membership) is 32.3%, and the indeterminacy membership function ratio is 18.2%.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84196181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.
{"title":"Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking","authors":"S. Jasim, A. A. Abdul Hassan, Scott Turner","doi":"10.14500/aro.10928","DOIUrl":"https://doi.org/10.14500/aro.10928","url":null,"abstract":"It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85600523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
New SARS-CoV-2 infections are difficult to beverified, whether they are reinfections or persistent infections. The most prominent factors used for differentiating reinfections from persistent infections are whole-genome sequencing and phylogenetic analyses that require time and funds, which may not be feasible in most developing countries. This study explores reinfections with COVID-19 that harbors D614G and N501Y mutations by rapid inexpensive methods. It exploits the previously developed rapid economic methods that identified both D614G and N501Y mutations in clinical samples using real-time reverse transcriptase polymerase chain reaction (rRT-PCR) probes and conventional PCR specific primers. In the present study, an immunocompetent patient has been found with a SARS-CoV-2 N501Y reinfection without comorbidities. According to the obtained results, this study suggests that the initial infection was due to a variant that contained only D614G mutation whereas the reinfection was potentially a result of alpha variant contained three mutations confirmed by DNA sequencing, including D614G, N501Y, and A570D mutations. These techniques will support rapid detection of SARS-CoV-2 reinfections through the identification of common spike mutations in the developing countries where sequencing tools are unavailable. Furthermore, seven cases of reinfections were also confirmed by these methods. These rapid methods can also be applied to large samples of reinfections that may increase our understanding epidemiology of the pandemic.
{"title":"Detection of SARS-CoV-2 Reinfections by Rapid Inexpensive Methods","authors":"S. Al-jaf, S. Niranji","doi":"10.14500/aro.10916","DOIUrl":"https://doi.org/10.14500/aro.10916","url":null,"abstract":"New SARS-CoV-2 infections are difficult to beverified, whether they are reinfections or persistent infections. The most prominent factors used for differentiating reinfections from persistent infections are whole-genome sequencing and phylogenetic analyses that require time and funds, which may not be feasible in most developing countries. This study explores reinfections with COVID-19 that harbors D614G and N501Y mutations by rapid inexpensive methods. It exploits the previously developed rapid economic methods that identified both D614G and N501Y mutations in clinical samples using real-time reverse transcriptase polymerase chain reaction (rRT-PCR) probes and conventional PCR specific primers. In the present study, an immunocompetent patient has been found with a SARS-CoV-2 N501Y reinfection without comorbidities. According to the obtained results, this study suggests that the initial infection was due to a variant that contained only D614G mutation whereas the reinfection was potentially a result of alpha variant contained three mutations confirmed by DNA sequencing, including D614G, N501Y, and A570D mutations. These techniques will support rapid detection of SARS-CoV-2 reinfections through the identification of common spike mutations in the developing countries where sequencing tools are unavailable. Furthermore, seven cases of reinfections were also confirmed by these methods. These rapid methods can also be applied to large samples of reinfections that may increase our understanding epidemiology of the pandemic.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72621612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs.
{"title":"Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor","authors":"N. Aljojo","doi":"10.14500/aro.10880","DOIUrl":"https://doi.org/10.14500/aro.10880","url":null,"abstract":"Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75227284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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