The Local and Global Corporations are suffering of managerial and financial corruption phenomena, which leads them to loss and bankruptcy. So, it is necessary to search for tools which help prevent phenomena like this, and one of these tools is (corporate governance) which represent controlling tool that contribute in reducing corruption. this study aims at modifying (corporate governance system)in order to make it suitable with Iraqi government Corporations. the study depend upon main hypothesis which is (the performance level of the strategic perspective of governance system is depending upon work according to the perspective dimensions itself.  From the main conclusions:-It is possible to building a strategic perspective

Abstract:

Viral marketing has become one of the modern strategies adopted by organizations in the marketing of products and services. The idea of viral marketing focuses on the social relations between individuals and groups. As a result of the technological development, most organizations have resorted to using the Internet and its applications and social media to market and promote their products. To reach the largest number of consumers to display their products and services in many ways, including text, audio, visual or video and thus affect the behavior of the consumer.

The problem of the study was the following question (do viral marketing technologies have an impact on consumer behavior?)

The research aims to measure the effect of sensory marketing (visual marketing, audio marketing, olfactory marketing, taste marketing, tactile marketing) in enhancing customer loyalty (behavioral loyalty, situational loyalty, perceptual loyalty) and the mediating role of marketing knowledge (product knowledge, price knowledge, promotion knowledge knowledge of distribution, knowledge of employees, knowledge of physical evidence, knowledge of the process) in a group of large single market markets in Baghdad and the researcher chose it because of the challenges faced by large single market in satisfying the customer and maintaining it as a permanent visitor and enhancing his loyalty, and the research problem was identified with a main

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be