To maintain a sustained competitive position in the contemporary environment of  knowledge  economy,  organizations  as an open social systems must have an ability to learn and know  how to adapt to rapid changes  in a proper fashion so that organizational objectives will be achieved efficiently and effectively.  A multilevel approach is adopted proposing that organizational learning suffers from the lack of interest about the strategic competitive performance of the organization. This remains implicit almost in all models of organizational learning and there is little focus on how learning organizations achieve sustainable competitive advantage . A dynamic model that captures t

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

      Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.

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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Collagen triple helix repeat containing-1 (CTHRC1) is an essential marker for Rheumatoid Arthritis (RA), but its relationship with pro-inflammatory, anti-inflammatory, and inflammatory markers has been scantily covered in extant literature. To evaluate the level of CTHRC1 protein in the sera of 100 RA patients and 25 control and compare levels of tumour necrosis factor alpha (TNF-α), interleukin 10 (IL-10), RA disease activity (DAS28), and inflammatory factors. Higher significant serum levels of CTHRC1 (29.367 ng/ml), TNF-α (63.488 pg/ml), and IL-10 (67.1 pg/ml) were found in patient sera as compared to that in control sera (CTHRC1 = 15.732 ng/ml, TNF-α = 33.788 pg/ml, and IL-10 = 25.122 pg/ml). There was no significant correlation be

E-learning is a lifeline for the educational process, which contributed to the sustainability of working educational organizations and prevented them from stopping, so the study came to measure the compatibility between E-learning quality dimensions (information technology, educational curricula, teaching methods, and intellectual capital of educational institution) as an independent variable, and educational services quality dimensions represented by (safety, tangibility, reliability and Confidence) as a dependent variable. The sample was 150 teachers was drawn from the College of Administration and Economics community of 293 teachers through the use of several statistical methods to measure the degree of correlation and impact between the

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

The research aims to identify the impact of the teaching methods Breathe test and imperative training method in learning some basic skills in Volleyball. The sample included 30 students of the first intermediate level from Al-Tawaia for boys / the public directorate of the education of Baghdad province – Al-Rasafa /2 ( The second). The samples are chosen randomly and divided into three groups : The systematic (Imperative method), first experimentary (training method), second experimentary (training method). Ten students are chosen for each group . The syllabus of the ministry of education is adopted on the systematic group while educational unites, which are prepared by the researcher, are used for the first and second experimenting group