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

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

This research deals with the case of the Iraqi joint-stock companies listed on the Iraq Stock Exchange study in terms of compliance with the requirements of IAS 33 "Earnings per share" and the research problem Alrtash concentrated in a statement the commitment of those companies the requirements of the International Standard 33, which may adversely affect the quality of financial reporting where and in particular the quality of accounting information and content of the primary and secondary characteristics make them be of interest to the decisions of its users, so the aim of this research to the statement of financial reporting earnings per share on the quality of financial reporting in listed shareholding in Iraq Stock Exchange

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Brainstorming is one of the fundamental and necessary concepts for practicing 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