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    Clean medical data and predict heart disease
    (Institute of Electrical and Electronics Engineers Inc., 2020) Alkhafaji, Mohammed Jasim A.; Aljuboori, Abbas Fadhil; Ibrahim, Abdullahi Abdu
    The enormous data provided by the health care environment needs many important and powerful tools for analyzing and extracting data and accessing useful knowledge. Many researchers have been interested in applying many statistical tools as well as many different data mining tools in order to improve an analysis process and extract data from a different data set. The only thing that proves the success and robustness of data mining tool is accurate diagnosis of the disease. According to the (WHO), the biggest cause of death in the last ten years or so in this vast world is heart disease. The statistical exploration tools that researchers use are tools that help decision-makers in health care to predict and diagnose heart disease. The tools used in the diagnostic process for heart disease have been thoroughly tested in order to demonstrate sufficient and acceptable accuracy. A set of patient data divided into 665 records was used, of which 300 were for males, with 365 for females, with 10 different related characteristics. The decision-making department still suffers from a lack of performance and decision-making. Our paper aims to process data in different ways before the process of accessing knowledge to make the appropriate decision through expectations of classification analysis and then using techniques to extract data with acceptable accuracy. Our goal proposed in this paper is to purify the data before the disease prediction process to get the best possible prediction and compare the results with the results of a group of previous researchers to reach an accurate diagnosis and prediction. The second part of our goal is to compare between different technologies on different data sets such as decision tree technology and the second technique is Bayesian classification technology and the last technology is neural networks and the results were (98.85%, 98.16%, 91.31%), respectively. In the end, we hope to obtain acceptable results with high accuracy in the future, enhance clinical diagnosis, and promote appropriate decision-making for early treatment specialists. © 2020 IEEE.
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    Enhancing Cold Cases Forensic Identification with DCGAN-based Personal Image Reconstruction
    (University of Baghdad, 2025) AL-Muttairi, Hasan Sabah K.; Kurnaz, Sefer; Aljuboori, Abbas Fadhil
    With the improvement of artificial intelligence and deep learning techniques, especially deep convolutional generative adversarial network (DCGAN), there has been a significant development in personal identity and generating images through facial reconstruction systems. This study focuses on proposing a model of personal image reconstruction from forensic sketches using DCGAN. The model comprises two networks: a generator to convert sketch images into real images and a feature network to determine the similarity of the generated images to real ones. Forensic sketches provided by relevant authorities are used as inputs to the proposed model. These sketches include details and information on the perpetrators or missing persons obtained from witnesses or the missing person parents. Prominent facial features extracted from the reconstructed images aid in the process of personal image reconstruction. The proposed model shows good results, achieving up to 99% accuracy in the generated images. The error ratio is reported to be as low as 0.92% based on the evaluation using the CUHKFaces dataset. This study presents a new approach to reconstructing human face images from forensic sketches using DCGAN.
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    The impacts of social media on university students in Iraq
    (Cairo Univ, Fac Computers & Information, 2020) Aljuboori, Abbas Fadhil; Fashakh, Abdulnaser M.; Bayat, Oğuz
    The rapid increase in the era of the technological revolution and the Internet, especially the social media, have created a new reality in the daily life of the societies in general and of the university students in particular, so this new reality without any doubt imposes on us the general effects of this increasing use of social media has affected all areas and societies The effects are positive and negative. These social means have become a vast space for exchanging ideas, making new friends, proposals, sources of information, business and e-shopping. In this study, we have decided to shed light on the academic, political and economic effects of the study by comparing them to the general effects of social media by looking at the demographic variables of Iraqi university students. Three universities (Kerbela University in the Middle Euphrates, UOITC University in the capital Baghdad and Tikrit University in western Iraq) where we considered spatial, cultural and social differences. Data collected through a survey consisting of four categories, General Influences, Academic Influences, Political Influences and Influences Business (distributed over 40 questions) We tried to cover most of the students' common uses of social media and their impact on them. The questionnaire was distributed through 100 questionnaires to each university. The total number of participants was (201) distributed on (77) participants from Karbala University, (50) participants from Tikrit University and (74) participants from the University of Information and Communication Technology (UOITC). (C) 2020 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.