Iraqi Journal of Intelligent Computing and Informatics

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) in the national identity 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 by python language and the Orange Data Mining (DM) tool. Choosing the workbook with the highest accuracy to work with to predict the needs of three government departments (military, social welfare, and statistical planning). 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

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

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

Managers seek to demonstrate that their businesses are exceptional in the dynamic and tough business world of today. The most typical method for doing this is to consistently improve your performance. The most precise and pertinent tool for measuring an organization's progress toward organizational excellence is the European Foundation for Quality Management (EFQM) Excellence Model. In this study, a novel integrated approach for enhancing overall organizational performance was presented using the EFQM model. To show the applicability of the suggested method, which is presented using the (EFQM) framework to identify strengths and chances for development, the RADAR methodology and the proposed technique based on fuzzy logic were employed in a case study at the Iraqi Oil Tanker Company.It produced excellent outcomes for the Iraqi Oil Tanker Company after identifying the vulnerabilities using the radar methodology and contrasting the two methods. The provided approach has helped to improve the level of the company's evaluation over the last three years, and the suggested strategy was implemented using a Matlab program because the differences between them were so minimal that they weren't even noted.

The protection of information sent over the Internet has become very important because of the possibility of being hacked and leaking important or confidential information. Therefore, many studies have been conducted in the field of information security and many ways have emerged to protect the information, but blockchain technology is the most prominent technology currently due to its high potentialin maintaining the study was conducted on the most important blockchain algorithms, namely the Proof of Authority (POA) algorithm and the Proof of Work (POW) algorithm. The study aimed now at the best and most reliable algorithm to protect the parameters sent via the Internet of Things technology. The POA algorithm is the first candidate to win the advantage in theory, but After applying the study and verifying the actual results, it was confirmed that the POA algorithm is superior and highly capable of protecting information, in addition to speed in executing calculations, less memory consumption, less execution time, fewer Nonce to obtain the correct hash, and many other advantages, and the obtained result wasthe PoAalgorithm is significantly faster with a difference of 46/s to create the blockchain, and it also requires less memory than the PoW algorithm, with a difference of 1024KB.Blockchain (B.C)algorithms will be applied to the Internet of Things (IoT) technology to obtain IoT technology that is completely encrypted from hacking and information cannot be tampered with. The transmission within the network, whether it is a local or global network. The new technology will be applied to a gas station information transmission system to generate electric power. The aim of this paper are to make the smart grid of the gas station more secure and private.

The mining and processing of data for wireless sensor networks (WSNs) have been a significant research area in a number of computer science disciplines, including distributed applications, database management systems, and information gathering. The main goal of implementing applications based on WSNs is to make real-time choices, which have proven very difficult due to the limitations of computing, communication, and data mining capacity for WSNs. Thus, due to the nature and unique characteristics of sensor data and the limitations of WSNs, traditional data mining methods cannot be easily applied to them. This paper presents a new approach, the Kalman Filter (KF)with K-Nearest Neighbors (KNN) named (KF-KNN) which is proposed for data classification and collection as well as noise elimination in WSNs, increasing the network efficiency and lifetime. The proposed technique is compared to KNN to illustrate the effectiveness of the recommended ways in increasing energy consumption and prolonging network lifetime.

The use of medical imaging for the purposes of medical monitoring and diagnosis has seen tremendous growth in recent years, which has resulted in a rising need for suitable data storage and accessibility. The primary goal of image enhancement is to generate a processed image that is suitable for a specific application. This can be accomplished by acquiring the image in the highest resolution possible or by using a variety of techniques to extract the important information while ignoring the irrelevant information. Image processing can then be performed on the processed image. Image enhancement techniques are used in medical imaging to increase the quality of the visual representation of an image by reducing the amount of noise present and sharpening the features. This study examines the Curvelet transform-based approach for increasing medical image quality. The contribution of the proposed review two steps are advised for optimizing the images. The first step is the optimization step utilizing the Curvelet transform, which featured a series of image-preparation processors. Curvelet's ability to extract complete spectrum information from an image with varying edge orientations makes it valuable for assessing data about wave propagation. In light of this, the curvelet transform is considered an excellent filter for improving and denoising images, and it is intended to deal with components in several orientations and scales. The primary objective of this treatment is to boost visibility and eliminate noise, which is superior to conventional techniques of enhancement in which the enhancement step is performed by discrete curvelet transform through wrapping.The contribution of the proposed review the usual vector is the second step of this review approach. The vector image is distributed randomly to each pixel in images that are unclear and noisy. This procedure eliminates the unpredictability in the previously computed directions and makes them regular, producing more precise results and enhancing image clarity, particularly at the image's edges. By comparing the results of the proposed technique of improvement to the results of existing improvement methods, it was determined that the proposed approach yields superior performance and is more efficient. The efficiency and efficacy of the suggested technique may be determined based on the results of the trials, which demonstrated that the method performs very well and yields accurate and precise results

One of the critical challenges facing operators around the world is how to ensure that everything is running smoothly as well as how to analyze the performance of the network. Nonetheless, the analytic system must be precise, user-friendly, and quick enough to depict network performance in realtime. Network performance is essential for ensuring service quality. In this light, the Network Simulator NS-2 is generally employed for network research on the widely-used UNIX and Windows systems. Next, thenetwork scenarios are generated using network simulation scripts, and upon completion of the simulation, trace files that record network events are generated. Information that might be utilized in a performance study could be recorded in trace files. Thiscould include the total number of packets sent, their arrival times, any delays or losses, and so on. This trace file analysis problem arises mostly because NS-2 does not provide any visualization options for analyzing simulation results (trace files). A system is proposed to analyze the NS2 trace file and facilitate data extraction and evaluation. The main feature of this system is the Java language for network performance evaluation. And to prove the validity of the system, the system was compared with the Trace graph, where the speed of the new system was very high.