Iraqi Journal of Intelligent Computing and Informatics (IJICI) 2022-10-14T00:00:00-04:00 Associate Professor Dr.Hayder Naser Khraibet AL-Behadili Open Journal Systems <div> <p>Iraqi Journal of Intelligent Computing and Informatics (IJICI) is a double-blind peer-reviewed, international academic journal published twice a year (June, and December) by Shatt Al-Arab University College. This journal covers all aspects of computer, informatics, electrical, electronical and communication technology, its theories, and applications. </p> </div> Congestion Avoidance using Traffic Load Balancing (CATLB) in Computer Networks 2022-10-03T07:11:11-04:00 Ahmed Malik Altameemi Khulood A Nassar <p>Congestion in computer networks is one of the most crucial subjects in the world of networks since it has a significant influence on the network and the quality of service and decreased efficiency of it, which frequently results in service interruptions, In order to preserve the continuity of data flow in the network, it is necessary to design approaches and processes to avoid congestion or minimize its effects. Congestion is avoided at two levels, knot and link, by using different techniques, including close loop or open loop. This paper proposed a new method (CATLB) to avoid congestion by using the traffic load balancing technique, CATLB is based on the popular algorithm Round Robin in the process of distributing packets coming to a particular node on the links connected to it, which leads to the same destination to avoid congestion on one of the links and out of service and thus leads to more load on the other links until the network collapses. Method has been simulated using OMNET++ and got around 15% improvement in the performance from the most popular similar algorithms in this field ECMP and TinyFlow.</p> 2022-11-02T00:00:00-04:00 Copyright (c) 2022 Iraqi Journal of Intelligent Computing and Informatics (IJICI) A Review of DNA-Based Color Image Encryption Algorithms 2022-08-16T07:21:21-04:00 ghofran shraida <p>Several encryption techniques based on DNA encoding have been presented in recent years for color image encryption. Image encryption is one of the most significant fields of study that has piqued the worldwide attention of scholars, and it is relevant in transferring important images via communication channels, that are insecure. In this paper, a review of color image encryption algorithms based on DNA coding is conducted from 2015 to 2021. The comparison findings on 12 included experiments in relation to correlation coefficient, key space, information entropy, NPCR, and UACI revealed that the encryption methods improved significantly. In conclusion, the DNA-based image encryption approaches offer a superior trade-off between security and computational complexity, and have been highlighted as an essential component in the construction of a trustworthy and authenticated cryptosystem.</p> 2022-10-14T00:00:00-04:00 Copyright (c) 2022 Iraqi Journal of Intelligent Computing and Informatics (IJICI) The application of the radar approach in the European Foundation Quality Management (EFQM) 2022-10-10T06:49:42-04:00 Miami Hameed <p>In managing today's dynamic and challenging business environment, managers want to show that their firms stand apart. The most common way to do this is to raise performance levels consistently. An organization's progress toward organizational excellence can be measured using the European Foundation for Quality Management (EFQM) Excellence Model, which is the most accurate and relevant method. The EFQM model is used in this research to provide a novel integrated strategy for improving overall organizational Performance. RADAR's approach and the proposed technique based on fuzzy Logic were used in a case study at the Iraqi Oil Tanker Company to demonstrate the applicability of the proposed method, which was demonstrated by using the European Foundation for Quality Management framework to identify strengths and opportunities for improvement ( EFQM). The Matlab software was used to implement the recommended strategy.</p> 2022-11-11T00:00:00-05:00 Copyright (c) 2022 Iraqi Journal of Intelligent Computing and Informatics (IJICI) Big Data in E-government: Classification and Prediction using Machine Learning Algorithms 2022-08-16T07:54:30-04:00 Mohammed Altamimi <p>Many countries have used big data to develop their institutions, such as Estonia in policing, India in health care, and the development of agriculture in the United Kingdom, etc. Data is very important as it is no longer oil that is the most valuable resource in the world, but data. This research examines ways to develop the Iraqi state institutions by using the big data of one of its institutions (electronic civil registry) (ECR) using the mining and analysis of this data. The pre-processing and analysis of this data are carried out depending on the needs of each institution and then using Machine Learning (ML) techniques. Its use has shown remarkable results in many areas, especially in data analysis, classification and forecasting. We applied five ML algorithms that are Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Naive Bayes (NB) carried out in the Orange Data Mining tool. According to the simulation results of the proposed system, the accuracy of the classifications was around 100%, 99%, and 100% for the military department by the SVM classifier, the social welfare department by the RF classifier, and the statistics-planning department by the SVM classifier, respectively.</p> 2022-10-14T00:00:00-04:00 Copyright (c) 2022 Iraqi Journal of Intelligent Computing and Informatics (IJICI)