current research aims to build an intellectual framework for concept of organizational forgetting, which is considered one of the most important topics in contemporary management thought, which is gain the consideration of most scholars and researchers in field of organizational behavior, which is to be a loss of intentional or unintentional knowledge of any organizational level. It turned out that just as organizations should learn and acquire knowledge, they must also forget, especially knowledge obsolete and worn out. And represented the research problem in the absence of Arab research dealing with organizational forgetting, and highlights the supporting infrastructure core, and show a close relationship with organizational learning and knowledge, and thus contributing to the embodiment of its contents in our organizations Arabic, which is the latest gap caused the omission of one of the vital topics in the field of organization theory and organizational behavior. And then rising of necessity to exploring the hidden aspects of the topic, to The review search method adopted in the methodology through the analysis of the relevant literature through three chapters, the research found a set of conclusions and recommendations that can help the Arab Director in the adoption of this concept and considered it as business philosophy in managing of his organization
Background: Cardiovascular disease (CVD) is the number one cause of death worldwide. Objective: To determine of coronary care unit nurses' knowledge regarding patient rehabilitation after myocardial infarction. Methodology: A cross- sectional study was designed in the Cardiac Care Unit of Al-Diwaniyah Teaching Hospital and the Specialized Center for Cardiac Surgery and Catheterization for the period from (November 7, 2022) to (May 12, 2023). A non-probability (purposive) sample consisting of (75 nurses) working in the above-mentioned study site. To determination of coronary care unit Nurses' knowledge regarding patient rehabilitation after myocardial infarction, the researcher used a tool that consists of two parts: The first part:
... Show MoreObjectives:This study aimed to identify women perception and experience regarding family planning(FP) methods
Methodology:Descriptive cross-sectional hospital based study,was conducted at Omer Sawi teaching hospital,from august to September 2019.Sample of 320 women, were selected randomly after their agreement.Data were collected through interview questionnaire and analyzed using a statistical package for social sciences (SPSS)and descriptive and inferential statistical methods were used.With accepted P.< 0.05.for the correlation significant.
Results:Age group between 21-25 years represent (53.1%),most common education levels were secondary school 56%.Majority of women had 2-5 children.Half of the wo
This research aims toknow the learning styles according to the model of Felder and Silverman and its relationship to effectively self- perceived mathematicalamong students of the Faculty of Education Pure Sciences - Ibn al-Haytham. By answering the following questions: 1. What are the preferred methods of learning among students in the mathematics department according to the model Felder and Silverman? 2. What is the mathematicalself-perceived levelof the students at the Department of Mathematics effectiveness level? 3. What is the relationship between learning styles according to the Felder model and Silverman and the effectiveness of mathematical self-perceived of the students of the Department of Mathematics? The research sample consiste
... Show MoreObjectives: To identify the effectiveness of instructional program concerning premarital screening of sexual transmitted disease on student's knowledge at Baghdad University and examine the relationship between students' knowledge and certain studied variables. And hypothesis for this study; There is a difference in university student’s knowledge toward premarital screening between pre and posttests of instructional program. Methodology: A quasi-experimental design (pretest-posttest approach) was conducted at six colleges and its college of education ibn rushd, college of political science, college of law, college of literatur
BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.
Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
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
... Show MoreWhenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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