The Organizational Transparency is regarded one of the effective means in the attempts of the restoration and working to prevent the corruption and reduce the negative managerial economic social and political effects.

Accordingly, this study aims at importance of performing an explorative analytical study in order to stand on the effect of the Organizational Transparency in the reduction of environment of Iraqi Organizations.

To achieve this study, three hypotheses were chosen; the first is the correlation relation and the second is the effect and the third is the differences, and statistical means represented by correlation coefficient "Spearman", "", "Mann- Whit

The expenditures of the general budget, in its operational and investment divisions, are a basic factor in the economic and social growth of any country, and its impact on various economic activities such as income, employees , and the standard of living of members of society. This was based on a basic premise: Does increasing or decreasing investment expenditures have an effect on increasing or decreasing the tax proceeds, What is the level of relationship between them? and to achieve the goal of the research, an inductive and analytical method was chosen to measure the impact of the investment budget expenditures on the tax outcome quantitatively using the financial data obtained from The General Authority for Taxes, Ministry of Financ

This research analyses the tweets of Iraqi politicians (leaders) that took place simultaneously with the formation of the Iraqi government after the elections in 2018. The formation of the Iraqi government was considered one of the most critical issues that emerged in the political process to which the Iraqi media as well as social networking sites paid considerable attention. In this regard, Iraqi political leaders have published many tweets concerning the formation of the government, some of them have caused great controversy in the political climate. As Twitter is one of the most digital platforms that have been widely used on the global scale in recent years, politicians have employed it to publish their opinions, ideas, and to excha

Plagiarism is becoming more of a problem in academics. It’s made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has ”taken” and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and

Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto

The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.

Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.

The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple

The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz