An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning

The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using chest X-ray and computed tomography (CT) images. This work introduces a model for the automatic detection of COVID-19 using CT images. A novel handcrafted feature generation technique and a hybrid feature selector are used together to achieve better performance. The primary goal of the proposed framework is to achieve a higher classification accuracy than convolutional neural networks (CNN) using handcrafted features of the CT images. In the proposed framework, there are four fundamental phases, which are preprocessing, fused dynamic sized exemplars based pyramid feature generation, ReliefF, and iterative neighborhood component analysis based feature selection and deep neural network classifier. In the preprocessing phase, CT images are converted into 2D matrices and resized to 256 × 256 sized images. The proposed feature generation network uses dynamic-sized exemplars and pyramid structures together. Two basic feature generation functions are used to extract statistical and textural features. The selected most informative features are forwarded to artificial neural networks (ANN) and deep neural network (DNN) for classification. ANN and DNN models achieved 94.10% and 95.84% classification accuracies respectively. The proposed fused feature generator and iterative hybrid feature selector achieved the best success rate, according to the results obtained by using CT images

Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation

E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “e-learning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly

A novel Covid-19 and Pneumonia Classification Method based on F-transform

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.</p

Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets

This study aims to investigate up-to-date career opportunities and in-demand competence areas and skill sets for cloud computing (CC), which plays a crucial role in the rapidly developing teleworking environments with the COVID-19 pandemic. In this paper, we conducted a semantic content analysis on 10,161 CC job postings using semi-automated text-mining and probabilistic topic-modeling procedures to discover the competency areas and skill sets as semantic topics. Our findings revealed 22 competency areas and 46 skills, which reflect the interdisciplinary background of CC jobs. The top five competency areas for CC were identified as “Engineering”, “Development”, “Security”, “Architecture”, and “Management”. Besides, the top three skills emerged as “Communication Skills”, “DevOps Tools”, and “Software Development”. Considering the findings, a competency-skill map was created that illustrates the correlations between CC competency areas and their related skills. Although there are many studies on CC, the competency areas and skill sets required to deal with cloud computing have not yet been empirically studied. Our findings can contribute to CC candidates and professionals, IT organizations, and academic institutions in understanding, evaluating, and developing the competencies and skills needed in the CC industry

Cross-resistance and associated resistance in Escherichia coli isolates from nosocomial urinary tract infections between 2004-2006 in a Turkish Hospital

In this study, antimicrobial resistance profiles were determined for 748 isolates of Escherichia coli from patients with acute nosocomial urinary tract infections (UTIs) at a Turkish Training Hospital. Thirteen antibiotics were included. Resistance to ampicillin alone (45.1%) and ciprofloxacin alone (20.6%) were the most commonly identified 'single resistances'. Multiple resistance was found in 49.7% of the strains. The most common multiple antibiotic resistance profiles included ampicillin-sulbactam/amoxycilline-clavulonate (4.0%) and ampicillin-sulbactam/trimethoprim-sulfamethoxazole/amoxycilline-clavulonate (2.8%). From 2004 to 2006, ampicillin, trimethoprim-sulfamethoxazole and ciprofloxacin resistant strains increased to 76% from 57%, 53% from 43% and 55% from 41%, respectively. The percentage of extended-spectrum beta-lactamase (ESBL) producing strains was 7.8% and imipenem resistance was seen in 5.2% of ESBL positive strains. We conclude that clinically important E.coli strains have now emerged with broader multidrug resistance. Periodical evaluation of laboratory results and clinical surveillance are crucially important for optimal antibiotic management of UTIs and infection control policies

Ameliorative effect of caffeic acid phenethyl ester on histopathological and biochemical changes induced by cigarette smoke in rat kidney

It was aimed to investigate the histopathological and biochemical changes in kidney tissues of rats exposed to cigarette smoke and possible protective effects of caffeic acid phenethyl ester (CAPE) on these changes. Twenty one male Wistar albino rats were divided into three equal groups. Animals in group I were used as control. Rats in group II were exposed to cigarette smoke and rats in group III were exposed to cigarette smoke and daily administration of CAPE. At the end of the 60-day experimental period, all the animals were sacrificed by decapitation. The serum samples obtained from the animals were studied for uric acid, creatinine and blood urine nitrogen (BUN) levels. Following routine histological procedures, kidney tissue specimens were examined under a light microscope. In addition, dismutase (SOD) and glutathione peroxidase (GSH-Px) enzyme activities and malondialdehyde (MDA) and nitric oxide (NO) contents were determined spectrophotometrically in tissue samples. It was found that serum uric acid and BUN levels of the rats exposed to cigarette smoke alone were elevated, although serum creatinine levels did not significantly change. Furthermore, renal SOD, GSH-Px, NO and MDA levels were significantly increased. These increases in serum BUN, and renal SOD, GSH-Px, NO and MDA levels were significantly inhibited by CAPE treatment. In light microscopic observations of tissues from rats exposed to smoke, mesangial cell proliferation in the renal corpuscles, dilatation and congestion in the peritubular capillaries and degenerative alterations in the proximal tubules were noted. There were also atrophic renal corpuscles. However, these histopathological changes were partially disappeared in the rats exposed to cigarette smoke plus CAPE. The present findings indicate that cigarette smoke causes impairment in renal structure and function, which can be prevented by CAPE administration

The efficacy of shock wave lithotripsy for renal calculi in upper, middle and lower calices

WOS: 00023990620130

Comparative results of shockwave lithotripsy for renal calculi in upper, middle, and lower calices

WOS: 000250086200001PubMed ID: 17941767Background and Purpose: To assess the results of shockwave lithotripsy (SWL) for renal calculi in upper, middle, and lower calices according to the stone burden. Patient and Methods: A series of 52 female and 66 male patients with a mean age of 47.8 years and isolated single caliceal stones who underwent SWL monotherapy were enrolled. Stone burden, stone location, number of sessions/shockwaves, and auxiliary procedures were noted for each patient. Stones were located in the upper, middle, and lower calices of 35, 43, and 40, patients respectively, with mean stone burdens of 81.4 mm(2), 75.2 mm(2), and 96.3 mm(2), respectively. Patients were evaluated with intravenous urography, plain film, or ultrasonography. Success was determined 3 months after the last session. Re-treatment rates were calculated. The effect of anatomic factors on the success of treatment for lower-caliceal stones also was determined. Results: The mean stone burden, median number of treatment sessions, and mean number of shockwaves were 84.2 mm(2), 2, and 4344, respectively. The auxiliary procedure rate was 16.1%, and the re-treatment rate was 71.2%. Failure was noted in 26 patients (22%). The stone-free rates for stones in the upper, middle, and lower calices were 82.8%, 83.4%, and 67.5%, respectively (P = 0.14). The stone-free rates for stones <100 mm(2) and 100 to 200 mm(2) were 91.2% and 65.5%, respectively (P = 0.001). The efficiency quotient was 49.8, 44.8, and 32.5 for upper-, middle-, and lower-caliceal stones, respectively. Infundibular length (P = 0.006) and infundibular width (P = 0.036) were significant in determining the stone-free rate after treatment of lower-caliceal stones. Conclusions: We recommend SWL as the first choice for treatment of stones <200 mm(2) in the upper and middle calices. Extracorporeal lithotripsy is one of the options for lower-caliceal stones <200 mm(2) but has high re-treatment and auxiliary-procedure rates in these cases