Purpose: aims the study to show How to be can to enhance measurement management by incorporating a risk-based approach and the six sigma method into a more thorough assessment of metrological performance.   Theoretical framework: Recent literature has recorded good results in analyzing the impact of Six Sigma and risk management on the energy sector (Barrera García et al., 2022) (D'Emilia et al. 2015). However, this research came to validate and emphasize the most comprehensive assessment of metrological performance by integrating Risk management based approach and Six Sigma analysis.   Design/methodology/approach: This study was conducted in Iraqi petroleum refining companies. System quality is measured in terms of sigmas, and t

Purpose: aims the study to show How to be can to enhance measurement management by incorporating a risk-based approach and the six sigma method into a more thorough assessment of metrological performance.   Theoretical framework: Recent literature has recorded good results in analyzing the impact of Six Sigma and risk management on the energy sector (Barrera García et al., 2022) (D'Emilia et al. 2015). However, this research came to validate and emphasize the most comprehensive assessment of metrological performance by integrating Risk management based approach and Six Sigma analysis.   Design/methodology/approach: This study was conducted in Iraqi petroleum refining companies. System quality is measured in terms of sigmas, and t

The purpose of this resesrh know (the effectiveness of cooperative lerarning implementation of floral material for calligraphy and ornamentation) To achieve the aim of the research scholar put the two zeros hypotheses: in light of the findings of the present research the researcher concluded a number of conclusions, including: -
1 - Sum strategy helps the learner to be positive in all the information and regulations, monitoring and evaluation during the learning process.
2 - This strategy helps the learner to use information and knowledge and their use in various educational positions, and to achieve better education to increase its ability to develop thinking skills and positive trends towards the article.
In light of this, the

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

In recent years, English language teaching and second language acquisition has demonstrated a significant accentuation upon basic reasoning abilities improvement in the language capability advancement. Encouraging a point of view of duty to training basic intuition aptitudes in accordance with the English language courses, this paper gives an account of an investigation directed at theoretical meanings of basic deduction, drifts about the centrality of basic speculation for language educating and associations between critical thinking and language learning. The educators have the focal pretended by basic intuition in successful language teaching method, identified to Ennis’ (2011) critical thinking categories. The skill of thinking critic

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

In this paper, a dynamic investigation is done for strip, rectangular and square machine foundation at the top surface of two-layer dry sand with various states (i.e., loose on medium sand and dense on medium sand). The dynamic investigation is performed numerically using finite element programming, PLAXIS 3D. The soil is expected as a versatile totally plastic material that complies with the Mohr-Coulomb yield criterion. A harmonic load is applied at the base with an amplitude of 6 kPa at a frequency of (2 and 6) Hz, and seismic is applied with acceleration – time input of earthquake hit Halabjah city north of Iraq. A parametric study is done to evaluate the influence of changing L/B ratio (Length=12,6,3 m and width=3 m), type of sand

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil