Purpose:  The research aims to explore the impact Business Intelligence System (BIS) and Knowledge Conversion Processes (KCP) in the Building Learning Organization (LO) in KOREK Telecom Company in Baghdad city.

Design/methodology/approach: in order to achieve the objectives of the research has been the development of a questionnaire prepared for this purpose and then has tested the search in the telecommunications sector, representatives of one of the telecommunications companies in Baghdad city, has therefore chosen KOREK Telecom company as a sample for research, and the choice was based on the best standard international companies to serve mobile communications in terms o

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

A few examinations have endeavored to assess a definitive shear quality of a fiber fortified polymer (FRP)- strengthened solid shallow shafts. Be that as it may, need data announced for examining the solid profound pillars strengthened with FRP bars. The majority of these investigations don't think about the blend of the rigidity of both FRP support and cement. This examination builds up a basic swagger adequacy factor model to evaluate the referenced issue. Two sorts of disappointment modes; concrete part and pulverizing disappointment modes were examined. Protection from corner to corner part is chiefly given by the longitudinal FRP support, steel shear fortification, and cement rigidity. The proposed model has been confirmed util

This research aims to demonstrate the impact of the going concern assumption in different accounting applications to provide a realistic look and more accurate result of activity and financial situation, as well as determining the responsibility of the Company's administration in compliance with the going concern assumption during the preparation for their financial statements, and to clarify the concept of integration between internal audit and external audit about going concern assumption, besides its importance and usefulness on the work of both of the internal auditor and the external auditor, as well as on the company under auditing process.This research purports preparing an internal audit program, including a set of auditing actio

Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms,

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Objective(s): To determine the prevalence of ADHD among elementary school pupils; identify the association between pupil's level of ADHD and age, etc., and investigate the differences in pupils ADHD based on gender, and grade.

Methodology: A descriptive study was conducted on elementary school pupils. The study started from the period of 16th of September 2019 to the 1st of October 2020. A cluster sample of 800 pupils was selected. The questionnaire was constructed and developed and include two parts: the first part includes the pupil's general information and the second part includes scale of ADHD prevalence.

Results: The results of the present study indicated that 38(4.