Yazar "Gwassi, Omar Abboosh Hussein" seçeneğine göre listele

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    Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest
    (2025) Uçan, Gülfem Özlü; Gwassi, Omar Abboosh Hussein; Apaydın, Burak Kerem; Uçan, Bahadır
    Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R2 values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.
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    Öğe
    Cyber-XAI-Block: an end-to-end cyber threat detection & fl-based risk assessment framework for iot enabled smart organization using xai and blockchain technologies
    (Springer, 2024) Gwassi, Omar Abboosh Hussein; Uçan, Osman Nuri; Navarro, Enrique A.
    The growing integration of the Internet of Things (IoT) in smart organizations is increasing the vulnerability of cyber threats, necessitating advanced frameworks for effective threat detection and risk assessment. Existing works provide achievable results but lack effective solutions, such as detecting Social Engineering Attacks (SEA). Using Deep Learning (DL) and Machine Learning (ML) methods whereas they are limited to validating user behaviors. Like high false positive rates, attack reoccurrence, and increases in numerous attacks. To overcome this problem, we use explainable (DL) techniques to increase cyber security in an IoT-enabled smart organization environment. This paper firstly, implements Capsule Network (CapsNet) to process employee fingerprints and blink patterns. Secondly, the Quantum Key Secure Communication Protocol (QKSCP) was also used to decrease communication channel vulnerabilities like Man In The Middle (MITM) and reply attacks. After Dual Q Network-based Asynchronous Advantage Actor-Critic algorithm DQN-A3C algorithm detects and prevents attacks. Thirdly, employed the explainable DQN-A3C model and the Siamese Inter Lingual Transformer (SILT) transformer for natural language explanations to boost social engineering security by ensuring the Artificial Intelligence (AI) model and human trustworthiness. After, we built a Hopping Intrusion Detection & Prevention System (IDS/IPS) using an explainable Harmonized Google Net (HGN) model with SHAP and SILT explanations to appropriately categorize dangerous external traffic flows. Finally, to improve global, cyberattack comprehension, we created a Federated Learning (FL)-based knowledge-sharing mechanism between Cyber Threat Repository (CTR) and cloud servers, known as global risk assessment. To evaluate the suggested approach, the new method is compared to the ones that already exist in terms of malicious traffic (65 bytes/sec), detection rate (97%), false positive rate (45%), prevention accuracy (98%), end-to-end response time (97 s), recall (96%), false negative rate (42%) and resource consumption (41). Our strategy's performance is examined using numerical analysis, and the results demonstrate that it outperforms other methods in all metrics.