Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The Iraqi construction industry suffers from many issues that lead to many design errors, clashes, delays and cost overruns. Therefore, applying constructability will prevent these issues from happening, as it has proven its positive effect in different projects around the world. The goal of this paper is to use building information modelling (BIM) to assess the constructability, provide the opportunities for the project stakeholders to choose the best constructable design alternative and find the affection of applying constructability on project cost. The practical side of this research consists of two parts: in the first part, 37 factors are collected from the literature review as factors that effect on constructability. After tha

In the framework of this study, the phenomenon of transfer is probed pragma-linguistically and socio-linguistically concerning marriage situations among Iraqi EFL learners. The study also strives to look at the refusal strategies most commonly employed by Iraqi female English as a foreign Language (EFL) learners compared to their counterparts, American native speakers of English. The study involved 70 female participants who answered a Discourse Completion Task (DCT), which contained ten marriage proposals to be refused. Each situation entailed refusal of a person from a higher, an equal, and lower status. The researchers adapted Beebe, Takahashi, and Uliss Weltz’s (1990) taxonomy of refusal for analyzing the data comprehensively. The

Multiple single-nucleotide polymorphisms (SNPs) located in the intergenic region between estrogen receptor 1 and CCDC170 (especially at rs3757318) are thought to be associated with breast cancer risk. additionally, the serum level of vitamin D is believed to be linked to different aspects of breast carcinogenesis.

In the framework of this study, the phenomenon of transfer is probed pragma-linguistically and socio-linguistically concerning marriage situations among Iraqi EFL learners. The study also strives to look at the refusal strategies most commonly employed by Iraqi female English as a foreign Language (EFL) learners compared to their counterparts, American native speakers of English. The study involved 70 female participants who answered a Discourse Completion Task (DCT), which contained ten marriage proposals to be refused. Each situation entailed refusal of a person from a higher, an equal, and lower status. The researchers adapted Beebe, Takahashi, and Uliss Weltz’s (1990) taxonomy of refusal for analyzing the data comprehensively.