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
The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach
... Show MoreTo determine the evaluation of education program on women's knowledge regarding managing BSE. The present quasi- experimental study, Non-probability (purposive), sample consisting of (260) women who are employee, and students in both colleges (Nursing college, Medical and Health Techniques College). The sample consist of two groups, study group (130) including those in (Nursing college), and control group (130) in (Medical and Health Techniques College). A questionnaire was constructed which included, Demographic information, Reproductive information, Family history, Previous medical history, and information about women's knowledge toward management of breast self examination (BSE). Instrument validity and reliability was determined. Data w
... Show MoreBP 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.
This research has taken to the knowledge of the scope of applying the international specification of (ISO 10015) which is regarded to training by the Iraqi ministry of municipalities and public works ,in order to determine its training quality .By using the checklist made based upon the items of the specification ,after translating the English copy into Arabic ,which takes the indications of training depending on qualitative bases. The results of the analysis emphasized that occurred total average by comparison the evaluation of the training activity in the mentioned ministry with the international specification in all of its main items, which was (%55) ,and totally documented ,which finally refer to the existence of great
... Show MoreFace 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 MoreObjectives: To determine the impact of an educational program on nurses’ knowledge
and practices concerning neurogenic bladder rehabilitation for spinal cord injured persons
through a follow-up approach each two months post program implementation for six
months.
Methodology: "Follow-up" longitudinal design by using time series approach of data
analysis and the application of pre-post tests approach for the study and the control
groups. The study was carried out at Ibn Al-Kuff hospital for (SCI) in Baghdad governorate
from 5th of July 2010 to 15th of October 2011. To achieve the objectives of the study, a
non-probability (purposive) sample of (60) nurses (males and females) were working in SCI
units were selec
The current study aims at investigating the effect of cooperative learning (Jigsaw) on motivation of female students. Department of kindergarten to learn Human biology. This is of be dove through verification of the hypothesis that there is no significant difference at the 0.05 level between the motivation of experiment of group subjects who study according to (Jigsaw) cooperative learning and that of the control group subjects who study traditionally.
The study is limited to female students al the first year-Department of kindergarten college of Education for women university of Baghdad during the academic year 2007-2008.
An experiment of design of partial control and post-test for two groups is used. The experiment groups consist
Objectives: To determine the effectiveness of the instructional program on patients’ knowledge about home safety while receiving anti-cancer treatment at Al- Karama Teaching Hospital in Al-Kut City.
Methodology: A quasi-experimental design is conducted through the application of a pre-test and post-test approach for the study and control groups from February 5th, 2020 to April 25th, 2020. A non–probability (purposive) sample of (50) patients treated at the Blood Disease and Oncology Center is selected and divided into two groups. Each group contains (25) patients as control and study groups. An instrument is constructed that is comprised of two parts; t
... 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