ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY最新文献_第8页

The Coexistence of extended spectrum β lactamases and AmpC production among uropathogenic isolates of Escherichia coli and its antibiogram pattern 尿路致病性大肠埃希菌广谱β内酰胺酶和AmpC共存及其抗生素谱图

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-06-21 DOI: 10.14500/aro.10898

Aryan R. Ganjo

{"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}

引用次数: 0

Effect of Static Magnetic Field on Bone Marrow Cellular Density 静磁场对骨髓细胞密度的影响

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-06-21 DOI: 10.14500/aro.10946

Bestoon T. Mustafa, Sardar P. Yaba, A. Ismail

引用次数: 0

Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements 研究最小-最大数据归一化对不同相似性度量下k -近邻回归性能的影响

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-06-21 DOI: 10.14500/aro.10955

Peshawa J. Muhammad Ali

{"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}

引用次数: 6

Human Body Posture Recognition Approaches 人体姿势识别方法

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-06-13 DOI: 10.14500/aro.10930

M. Ali, A. Hussain, A. Sadiq

引用次数: 1

An Optimized SWCSP Technique for Feature Extraction in EEG-based BCI System 基于脑电图的脑机接口系统特征提取的优化SWCSP技术

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-05-30 DOI: 10.14500/aro.10926

Navtej S. Ghumman, B. Jindal

{"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}

引用次数: 3

Identification DNA Methylation Change of ABCC8 Gene in Type 2 Diabetes Mellitus as Predictive Biomarkers ABCC8基因甲基化变化在2型糖尿病中作为预测性生物标志物的鉴定

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-05-30 DOI: 10.14500/aro.10947

Harem O. Smail, Dlnya A. Mohamad

{"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}

引用次数: 2

Measuring the Voice Resemblance Extent of Identical (Monozygotic) Twins Using Voiceprints Neutrosophic Domain 用声纹中性区测量同卵(同卵)双胞胎的声音相似程度

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-05-27 DOI: 10.14500/aro.10925

Yazen A. Khaleel, Caroline Y. Daniel, S. Yahya

{"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}

引用次数: 0

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking 基于人脸和眼动追踪的灰狼优化检测驾驶员睡意

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ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-05-05 DOI: 10.14500/aro.10928

S. Jasim, A. A. Abdul Hassan, Scott Turner

{"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}

引用次数: 4

Detection of SARS-CoV-2 Reinfections by Rapid Inexpensive Methods 快速廉价方法检测SARS-CoV-2再感染

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-05-03 DOI: 10.14500/aro.10916

S. Al-jaf, S. Niranji

{"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}

引用次数: 3

Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor 网络传输标志基于k近邻的数据亲和力分类

IF 0.6

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY Pub Date : 2022-04-25 DOI: 10.14500/aro.10880

N. Aljojo

{"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}

引用次数: 1