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Öğe Detection of Chronic Diseases Based on the Principles of Deep and Machine Learning(American Institute of Physics Inc., 2023) Ulsada, A.A.A.; Ramaha, N.T.A.Continuing care is referred to as a chronic disease. The most widespread and expensive medical illnesses worldwide are chronic diseases. Chronic diseases can result in hospitalization, long-term impairment, worse quality of life, and even death. These conditions include cancer, diabetes, hypertension, stroke, heart disease, respiratory conditions, and kidney diseases. In reality, the greatest cause of mortality and disability worldwide is chronic illnesses. In this paper, we present deep-based and machine-based models to diagnose chronic diseases, this system includes several stages, namely the stage of data pre-processing and the stage of disease detection, which is carried out in two ways, the first depending on a deep Convolution Neural Network (CNN) and the second based on five machine learning algorithms: Stochastic Gradient Descent (SGD), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The proposed model works on three data sets, namely (Pima Indians Diabetes Dataset, Cardiovascular Disease dataset, and UCI Heart Disease Data) to classify heart, diabetes, and kidney diseases. The experimental results proved the capability of the suggested system to classify the aforementioned diseases with an ideal accuracy of 100% using the CNN in the first model, and an accuracy of 94% in the second model using the SGD and LR algorithms. © 2023 American Institute of Physics Inc.. All rights reserved.Öğe Effective machine learning techniques for brain pathology classification on mr images(American Institute of Physics, 2024) Mahmood, R.M.; Ramaha, N.T.A.; Karas, I.R.Since a brain tumor is essentially a collection of aberrant tissues, it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification using machine learning from brain MRI scans are well-known to be challenging and important endeavors. Machine learning has the potential to be used in diagnostics, preoperative planning, and postoperative evaluations. Furthermore, it is crucial to get accurate measurements of the tumor's location on an MRI of the brain. The development of machine learning models and other technologies will let radiologists detect malignancies without having to cut into patients. Pre-processing, skull stripping, and tumor segmentation are the steps in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The methods' efficacy is measured by precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%. © 2024 Author(s).Öğe Exploring Lightweight Blockchain Solutions for Internet of Things: Review(Springer Science and Business Media Deutschland GmbH, 2024) Ismael, O.A.; Abdulrazzaq, M.M.; Ramaha, N.T.A.; Mukhlif, Y.A.; Al, Zakitat, M.A.S.The world is witnessing a major digital transformation and is moving towards more interaction, connectivity, ease, and intelligence through the Internet of Things (IoT). The IoT offers these advantages to the world by linking necessary devices with each other, making it easier to manage and deal with those devices. However, the IoT faces many challenges, such as authentication, privacy, security, and access management. The application of blockchain technology may provide a solution to these challenges. Nevertheless, applying blockchain technology may face limitations, such as the limited resources of the IoT devices used and the resource-intensive requirements of the blockchain. Therefore, to overcome these limitations, several studies have proposed using a lightweight blockchain; this blockchain is specifically designed for resource-limited IoT devices. In this paper, a comprehensive review has been made on the uses of lightweight blockchain in the IoT. Moreover, we identified some of the challenges facing the application of blockchain technologies in the IoT and the future directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Harnessing Advanced Techniques for Image Steganography: Sequential and Random Encoding with Deep Learning Detection(Springer Science and Business Media Deutschland GmbH, 2024) Al, Zakitat, M.A.S.; Abdulrazzaq, M.M.; Ramaha, N.T.A.; Mukhlif, Y.A.; Ismael, O.A.This study delves into the intricacies of steganography, a method employed for concealing information within a clandestine medium to enhance data security during transmission. Given that information is often represented in various forms, such as text, audio, video, or images, steganography offers a distinctive advantage over conventional cryptography by focusing on concealing the very existence of the message, rather than merely its content. This research introduces a novel steganographic technique that places equal emphasis on both message concealment and security enhancement. This study highlights two primary steganographic methods: sequential encoding and random encoding. By employing both encryption and image compression, these techniques fortify data security while preserving the visual integrity of cover images. Advanced deep learning models, namely Vgg-16 and Vgg-19, are proposed for the detection of image steganography, with their accuracy and loss rates rigorously evaluated. The significance of steganography extends across various sectors, including the military, government, and online domains, underscoring its pivotal role in contemporary data communication and security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe AN INTELLIGENT TUTORING SYSTEM TO MAINTAIN THE STUDENTS' MOTIVATION(International Society for Photogrammetry and Remote Sensing, 2022) Ramaha, N.T.A.; Abdellatef, H.; Karas, I.R.Recently, many educational institutions around the world has transformed to online education specially during the COVID-19 pandemic. This fast and in many cases unplanned transformation leads to the needs for more researches to find solutions for the problems of this rapid transformation. As it's the best economic options during this pandemic, this study focused on creating a web-based (asynchronous system) intelligent tutoring system (ITS) to support the teachers in the C programming language course. Nonetheless, the suggested system takes into consideration one of the biggest challenges for asynchronous system which is how to maintain the students' motivation for the entire learning process. Therefore, the current study suggested the use of an interactive ITS as a solution for this challenge. The created system C-ITS used a set of motivational state rules and tactics to assess and maintain the motivation of the students. Finally, after using the system by the students and the teachers for two weeks, we conducted an evaluation study to evaluate the quality of the system design, the usability, the functionality, the compatibility. The result of the evaluation study showed that C-ITS system acceptable from both the students and the teachers. © 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved.Öğe Students' Performance Prediction Using Machine Learning Based on Generative Adversarial Network(Institute of Electrical and Electronics Engineers Inc., 2023) Khudhur, A.; Ramaha, N.T.A.Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes. © 2023 IEEE.Öğe TOWARDS WEBCAM-BASED FACE DIRECTION TRACKING to DETECT LEARNERS' ATTENTION within ASYNCHRONOUS E-LEARNING ENVIRONMENT(International Society for Photogrammetry and Remote Sensing, 2021) Ramaha, N.T.A.; Karas, A.R.; Gül, E.; Bozkurt, M.R.; Yayvan, R.Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners' attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient. © Author(s) 2021. CC BY 4.0 License.
