This research aims to explore the status effects of the Sudden, and gradual of AI-Driven Cyber Security (AIDC), Big data analytics and Business Intelligence (BI) The strategic flexibility (SF) and entrepreneurship (ENT) on Iraqi internet services providers ISPs. The work fills an important lacunae in what the advanced dimensions of technology have with regard to its influencing both SF and ENT outcomes in developing economy context, where empirical evidences are scant. A cross-sectional survey based on a quantitative research methodology was conducted on 113 professionals in Iraqi telecommunication sector. A partial least squares structural equation modeling (PLS-SEM) approach was adopted to examine 10 hypotheses concerning the relationships between technology capabilities and SF and ENT. The results reveal the in-depth direct influences of AIDC, BDAC and BI on the SF and ENT factors. SF is found to be an important mediating factor that significantly strengthens the influence of technological competence on ENT outcomes. This indicates two paths (direct and indirect) to reach SF and ENT, facilitated by AT. The present study also extends the literature by adding further empirical evidence from Iraq which indicates that integrating AIDC, BDAC and BI has a synergetic impact on SF but also on ENT. The findings apply only to the Iraqi context in geographical terms. The results show that spending on these technologies are strategic spending rather than operational expenditure. “Institutions should structure responses that are flexible to fit in with people and things, in order to harness the maximum benefit from technology.” These capabilities may be viewed as bases of competitive advantage (CA). This study is first of its kind in Iraq which examines the synergistic effect of AIDC, BDAC and BI on OSC and ENT performance where insight into developing economy perspective becomes present.
The study aimed to get acquainted with kindergarten teachers in the development of
emotional intelligence in children, To achieve this a study too, which consisted of 40 items,
within four areas was condncted: (managing emotions, emotional knowledge, empathy, social
networking) The study tool was applied to the sample amounting (200) teachers of the
kindergarten teachers in the province of Jerash and after analyzing the results statistically
using arithmetic averages standard deviations and variance analysis quartet the following
results were reached :
- presence of statistically significant differences at the level of (α =0,05) is attributable to the
impact of the educational level in the areas of empathy and so
The Intelligence of the Child in Relation to some Variables
In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method
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This review discusses precision agriculture techniques that help reduce the effects of soil degradation and improve soil health, based on an analysis of studies published in scientific databases such as Web of Science, Scopus, IEEE Xplore, Google Scholar, and ScienceDirect, with an emphasis on recent field research. The methodology included a qualitative analysis of case studies and application experiments in different areas to evaluate the impact of technologies such as controlled traffic farming (CTF), mechanized guidance (MG), precision fertilization (PF), precision irrigation (PI), conservation tillage (CT), and precision tillage (PT). Research results showed, CT to maintain soil structure and reduce organic matter loss increases soil f
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
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This research focuses on detecting the financial corruption cases in Iraq in light of adoption the strategic audit, the paper deals with the problem of the proliferation corruption cases particularly financial in Iraq and dramatically in the presence of audit and control devices as well as inspection and integrity devices, which indicates the existence of deficiencies and weaknesses in those devices in the implementation of audit and control functions in order to detect the corruption cases in the economic units in Iraq.
Stems objective of this research through the provision of approach of strategic audit concepts and indicate the extent importance of adopting of strategic audit as a means to detect the f
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In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.
Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the
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