The audit profession today gets an increasing interest by the financial , economic and legal contemporary societies , because of the importance of the technical & neutral auditor’s opinion to the financial reports beneficiaries in order to enable them making their investment decisions , but some of them whom suffered damage or loss when they made that decisions according to the mentioned financial statements which consolidated with auditor’s report , exposed the function to a responsibility and credibility crisis in addition to missing the trust especially at the latest years. They always ask why or what is the reason that cause the auditor didn’t giving us any warning signs about the economic mater for that companies.

Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

The theme of love has been and still is the interest of authors and writers for it is closely related to people’s lives. That great passion has helped them express genuine thoughts pertinent to that theme that has given rise to many debates, some are vague and others controversial. In his (strait is the gate), (La Porte étroite), André Gide has sought to shed light on his characters perspective of love and its relationship with sacredness and divine happiness. Indeed these characters have found often themselves restrained, chained and exhausted, by tough religious commands that have imposed on them spiritual commitments and duties too hard to break.

André Gide has revealed a protestant deviation from the perfection theme tha

The research has  focused on accounting for marketing risks faced by banks because they are exposed to a high level of risk as a result of their great utilization of financial instruments, which may make them liable to insolvency or bankruptcy and collapse, and accordingly to major economic crises. Therefore, banks are required to manage (reduce) risks so as to prevent them. As Iraqi banks don^'t manage these risks according to modern methodology of using derivatives, as well as lack of both the standard accounting system for banks and the Iraqi accounting criterion (standard) No. 10 to an obvious methodology for these risks identification, classification, hedging and disclosure, the researchers was prompted to concentrate

The wastewater arising from pulp and paper mills is highly polluted and has to be treated before discharged into rivers. Coagulation-flocculation process using natural polymers has grown rapidly in wastewater treatment. In this work, the performance of alum and Polyaluminum Chloride (PACl) when used alone and when coupled with Fenugreek mucilage on the treatment of pulp and paper mill wastewater were studied. The experiments were carried out in jar tests with alum, PACl and Fenugreek mucilage dosages range of 50-2000 mg/L, rapid mixing at 200 rpm for 2 min, followed by slow mixing at 40 rpm for 15 min and settling time of 30 min. The effectiveness of Fenugreek mucilage was measured by the reduction of turbidity and Chemical Oxygen Demand

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which ca