The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Due to the lack of statistical researches in studying with existing (p) of Exogenous Input variables, and there contributed in time series phenomenon as a cause, yielding (q) of Output variables as a result in time series field, to form conceptual idea similar to the Classical Linear Regression that studies the relationship between dependent variable with explanatory variables. So highlight the importance of providing such research to a full analysis of this kind of phenomena important in consumer price inflation in Iraq. Were taken several variables influence and with a direct connection to the phenomenon and analyzed after treating the problem of outliers existence in the observations by (EM) approach, and expand the sample size (n=36) to

The current study aims to identify:The meta-motivation and Uniqueness seeking of the study sample. The correlated  relationship among them. The present study sample consists of (400) students from the colleges of engineering, University of Baghdad, and the University of Technology in the academic year (2019-2020), and the researcher has adopted the Chen Scale (1995)to measure the meta-motivation after its translation into Arabic by(Al-Samawi,2011).The scale includes six dimensions. The researcher has also adopted the Snyder&Fromkin scale (1980) to measure the uniqueness  seeking after translating and adapting it into the Arabic environment. The scale consists of three dimensions. The results show that students of the Facult

   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

In a world of fierce competition companies of different activities strive to strengthen their competitiveness in order to be able to deliver greater value to their customers and gain a distinct sites in competition with other companies in the market at the local and international levels. Every company seeks to focus on one or more of the competitive capabilities in order to turn it into an obvious advantage or a number of competitive advantages to contribute in improving the performance and superiority over its competitors. Therefore, the management of companies no longer need only useful information for the internal aspects of the environment, but also need to include the external environment that includes various and constantly changin

     This study research to Showing The impact of the integration process on the quality of Insurance service and analyze the impact financially to determine whether there is any impact on the quality of the insurance service, in addition to identifying the obstacles that impede the merger between the insurance companies, insurance and the appropriate proposals for its' application and benefit from the benefits achieved.

The importance of The study lies in the fact that it is an important and vital spotlight that is becoming increasingly important in the world to confront the various difficulties and crises that have occurred  recently in light of the liberalization of international trade, globalization a

The financial crises that occurred in South East Asia and America, and the discovery of the financial and administrative corruption that affected these companies, which led to the crisis. Since then, organizations and institutions interested in accounting have begun new ways to further rationalize the management of these companies. Hence the concept of governance, which means good governance. Al-Susiya confirmed its adoption of the financial reporting standards and adopted internal and external auditing for the purpose of increasing the reliability and credibility of the financial and reports that increase the confidence of investors in making their investment decisions in the financial markets The study dealt with the contributi

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T