Brainstorming is one of the fundamental and necessary concepts for practising the auditing profession, as auditing standards encouraged the implementation of brainstorming sessions to reach reasonable assurance about the validity of the evidence and information obtained by the auditor to detect fraud, as the implementation of brainstorming sessions and the practice of professional suspicion during the audit process lead To increase the quality of auditing and thus raise the financial community's confidence in the auditing profession again after it was exposed to several crises that led to the financial community losing confidence in the auditing profession.

The research aims to explain the effect of brain

Bendable concrete, also known as Engineered Cementitious Composite (ECC) is a type of ultra-ductile cementitious composites reinforced with fibres to control the width of cracks. It has the ability to enhance concrete flexibility by withstanding strains of 3% and higher. The properties of bendable concrete mixes (compressive strength, flexural strength, and drying shrinkage) are here assessed after the incorporation of supplementary cementitious materials, silica fume, polymer fibres, and the use of ordinary Portland cement (O.P.C) and Portland limestone cement (IL). Mixes with Portland limestone cement show lower drying shrinkage and lower compressive and flexural strength than mixes with ordinary Portland cement, due to the ratio o

he planning process is generally aimed at developing the city and making it meet the needs of different citizens. The green areas constitute one of the basic needs of the city and with the rapid and unusual growth in the size of cities, especially in the third world countries, which is often embodied in capitals. Which was achieved as a result of many reasons, including political, economic and social and even enshrined through some of the decisions that were issued and the city of Baghdad, but a clear example of these cities. The city and the environment are inseparable terms. The city is where people spend their lives and their daily experiences, and the environment is the center in w

The importance of the current study  lies in the  importance of the  Tax policy that  being considered one of the most important tools working on fulfilling  the  social,  financial  and economic  goals  and improving  the investment environment  in the country  to become  having the ability to  activate the  national economy. The current study  has  referred  that  (  Has  the  tax planning  practiced by  the Iraqi  contribution  companies  led to increase  the  far-term tax  outcome through  getting  benefit of   the monetary  funds  and expansion in&nbs

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog