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Öğe Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, S.R.; Sonuc, E.; Ahmed, M.R.; Duru, A.D.Deep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content. © 2022 IEEE.Öğe Applications of Machine Learning in Battling Against Novel COVID-19(Institute of Electrical and Electronics Engineers Inc., 2022) Saeed, R.R.; Yaseen, O.M.; Rashid, M.M.; Ahmed, M.R.In this paper, is to strongly analyze the Coronavirus Diseases (Covid-19) via utilizing the machine learning depended on classification as well as clustering method. Researchers' prediction will not only allow detection and pipeline to predict how much money their detection method for COVID-19 will make, but it will also allow them to justify their characteristics, such as type of infection and choice of vaccine to reach a certain detection using machine learning based model. In this way, it overcomes the challenge of new COVID-19 forecasting: the lack of historical data. With the machine learning algorithm, researchers provide prediction at 15 to 20 different methods with an accuracy above 80% after training. The training is performed on 80% of data while the testing is done on remaining 20% of data. Such prediction will also allow other interested third parties to predict the success of a COVID-19 detection before it is released on open-source community. In the process of prediction, some researchers found the variables most associated with COVID-19 detection, and to see how the various prediction models are affected by them. Nevertheless, those machines learning based methods can greatly benefit from modern artificial intelligence techniques for this purpose that can handle complex features and give out great prediction results. Therefore, employing historical COVID-19 data and using them in machine learning algorithms to predict disease could save companies millions of dollars on rather unsuccessful detection. © 2022 IEEE.Öğe Information Retrieval System of Arabic Alphabetic Characters by Using Hidden Markov Model(Institute of Electrical and Electronics Engineers Inc., 2022) Shaker, A.S.; Khaleel, M.F.; Ismael, O.A.; Majeed, R.S.; Ahmed, M.R.In this paper, recognizing Arabic printed text is presented. A long time ago Recognizing the Arabic text has received great interest in information technology applications, as the Arabic language is among the different languages, and what distinguishes it from other languages is also shared by the Persian language and the ancient Turkish language (Ottoman) the difficulty of recognizing the letter because the letter has several forms in The beginning of the word and a shape in its middle and a shape at the end of the word. In languages other than the letters are written, the shape of the letter is one in the word, so it is easy to manipulate it through ready-made Optical Character Recognition (OCR) programs as letters A to Z. As a result, this study proposes a method for distinguishing Arabic printed text from other printed text. He's been looking forward to this moment for a long time. We demonstrate an off-line system that recognizes printed Arabic text by employing a Hidden Markov Model and an algorithm that divides the recognize reviews literature into sections and characters. © 2022 IEEE.Öğe SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS(College of Education, Al-Iraqia University, 2022) Ahmed, S.R.; Abbood, Z.A.; Farhan, H.M.; Yasen, B.T.; Ahmed, M.R.; Duru, A.D.This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker's presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases. © 2022 Iraqi Journal for Computer Science and Mathematics. All rights reserved.Öğe Survey On Recognition Hand Gesture By Using Data Mining Algorithms(Institute of Electrical and Electronics Engineers Inc., 2022) Khalaf, L.I.; Aswad, S.A.; Ahmed, S.R.; Makki, B.; Ahmed, M.R.We investigated the usage of hand motion recognition using data mining approaches. In gesture detection applications, I've discovered that employing data mining to recognize hand motions has several advantages. To begin, we employ data mining to recognize bandwidth hand motions. Second, we have a high temporal resolution, which is great for data mining. A short-range, close-pulse broadcast signal is required in most cases. The frequency components of the data mining for hand gesture recognition are the same as the pulse, but the phase components are different. Our vision must be strong enough to differentiate a far hand from distant things. Data mining is equal to recognizing hand motions by heart rate divided by length in these categorized articles. Obtaining significant power for a brief period. © 2022 IEEE.