Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.

Occult hepatitis C virus (HCV) infection (OCI) was reported in an apparently disease-free state in the absence of liver disease, anti-HCV and HCV-RNA in the serum. The existing data examining the clinical significance of OCI and its potential as a source of HCV infection among hemodialysis patients are very limited. We examined the presence of OCI among patients on maintenance hemodialysis at Minia Governorate, Egypt; an HCV endemic country. A total of 81 subjects with negative markers for HCV were enrolled. HCV-RNA was tested in PBMCs by real-time PCR. For the 81 subjects, the average dialysis duration was 32.7 ± 21.7 months and the average ALT level (±SD) was 26 ± 12 U/L while that of AST was 29 ± 16 U/L. Out of the 81 subjects, three (3.7%) were HCV-RNA positive in PBMCs in the absence of serum anti-HCV and HCV-RNA indicating OCI. The viral load of the OCI subjects ranged from 172 to 4150 IU/ml. History of liver disease was positive in one of the three positive patients. These results highlight the potential risk of HCV transmission from patients within hemodialysis units in Egypt. J. Med. Virol. 88:1388-1393, 2016. © 2016 Wiley Periodicals, Inc.

Four biofertilizers (active dry yeast, phosphorene, rhizobacterium, and nitrobein) were used to fertilize `Red Roomy' grapevines. Growth and nutritional status of the vines as affected by such fertilizers during 1995 and 1996 were studied. Results showed that fertilizing the vine with all biofertilizers caused a material improvement in shoot length, leaf area, and cane thickness, and effectively enhance the nutritional status of the vines. The favorable effects of such fertilizers were in the following descending order: phosphorene, rhizobacterium, nitrobein, and active dry yeast. A great increase on growth and nutritional status of `Red Roomy' grapevines occurred as a result of supplying the vines with phosphorene or rhizobacterium as good, new biofertilizers.

Quicksort on the fly returns the input of n reals in increasing natural order during the sorting process. Correctly normalized the running time up to returning the l-th smallest out of n seen as a process in l converges weakly to a limiting process with path in the space of cadlag functions.

It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model’s training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.

Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and k -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.

tem rust has been one of the most serious diseases on wheat in Egypt, particularly on the late sowings. The locally produced wheat cultivars have been developed as field resistant to stem rust regardless their reaction in terms of infection types. Many of them served in agriculture for long time since their release showing low levels of disease severity. Out of these cultivars, ten were tested in a randomized complete block design experiment, in four replicates for three seasons at two locations, i.e. Nubariya and Sids Agricultural Research Stations in Egypt. The experiment was surrounded by a spreader area of highly susceptible cultivars, inoculated with a mixture of rust races as source of inoculum. Rust data were recorded as rust severity (%). The area under disease progress curve (AUDPC) was estimated as a reliable and good measure of adult plant resistance (APR), partial resistance. According to the levels of rust severity and AUDPC values, the tested cultivars could be classified into two main groups; a) slow rusting cultivars; including cvs. Sids 1, Sids 13, Gemmeiza 7, Giza 168, Sakha 93 and Sakha 94. b), and Fast rusting cultivars; including cvs. Giza 160, Giza 164, Sakha 8 and Sohag 3 showing high level of rust severity and high estimates of AUDPC. Under greenhouse conditions, the numbers of probable genes for stem rust resistance were postulated by testing the ten local wheat cultivars and twenty monogenic lines for stem rust against fifteen cultures of Puccinia graminis f.sp. tritici [the cv. Giza 160, characterized by high level of rust severity and high estimates of AUDPC was used as check cultivar]. Gene postulation showed that the cvs. Giza 160 and Sakha 8 do not have any of the tested genes. In addition, the fast rusting cultivars, i.e. cvs. Giza 160, Giza 164, Sakha 8 and Sohag 3, have low number of genes (2 genes of the tested genes or not have any genes). While, the slow rusting cultivars, i.e. Sids 1, Sids 13, Gemmeiza 7, Giza 168, Sakha 93 and Sakha 94 have higher number of genes (from 4 to 13 of the tested genes). As many as the resistance genes are accumulated in any wheat cultivar, both the rust severity and AUDPC value become lower and its resistance becomes more durable.

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