جامعة بلاد الرافدين - Bilad Alrafidain University - M A Chyad

م.د محمد احمد جياد

M A Chyad

مقرر قسم : هندسة تقنيات الحاسوب

Department rapporteur : DEPARTMENT OF COMPUTER ENGINEERING TECHNIQUES

دكتوراه

Ph.D

Mohammed.chyad@bauc14.edu.iq

Mohammed.chyad1@gmail.com

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Research

	2024	Information Fusion
Deep learning, a robust framework for complex learning, outperforms previous machine learning approaches and finds widespread use. However, security vulnerabilities, especially in fusion in multi-color channel skin detection applications using adversarial machine learning (AML) and generative adversarial networks (GANs), lead to misclassifications. Researchers are actively exploring AML's and GANs' impact on misclassification, focusing on vulnerabilities in lighting conditions, skin-like patches in lesion segmentation, and insufficient data in facial emotion recognition. Yet, these areas only scratch the surface of potential AML vulnerabilities and GANs. To comprehensively address challenges, an in-depth investigation into AML and GANs components is crucial to uncover underlying reasons for misclassifying skin detection. This study addresses challenges of fusion in multi-color channel skin detection by creating a diverse dataset with 17M patches for enhanced feature fusion/training and meeting dataset criteria, investigating misclassifications using various deep learning models belonging to AML and GANs and color spaces (e.g., RGB, YCbCr, HSV, YUV), and exploring binary and multiclass scenarios. Notably, YCbCr outperformed RGB, achieving 98 % for binary skin classification, 84 % and 69 % for multiclass four and five-class scenarios. Binary classification for skin tones and their skin-like counterparts (e.g., black skin tone and black skin-like) yielded 97 %, 81 %, 60 %, and 51 % for black, brown, medium, and fair, respectively. Exploration of darker skin tones showed improved accuracy. Benchmarking with a CNN and RNN hybrid achieved 99 % accuracy, surpassing the initial 91 %, while SAE reached 97 %. The study explores implications of overlapping between skin and skin-tone recognition, offering insights for developing a generalized skin detector. The investigation demonstrates that improper color space selection can make lighting conditions exploitable in AML attacks and GANs, emphasizing the crucial role of color space choice in mitigating vulnerabilities.

	2024	International Journal on Informatics Visualization
The term "Metaverse" has recently gained significant attention. It refers to a concept aiming to immerse users in real-time 3D virtual worlds using XR devices like AR/MR glasses and VR headsets. When this idea is applied to industrial settings, it's termed the "Industrial Metaverse," where operators leverage cutting-edge technologies. These technologies align closely with those associated with Industry 4.0, evolving towards Industry 5.0 and prioritizing sustainable and human-centric industrial applications. The Industrial Metaverse stands to benefit from Industry 5.0 principles, emphasizing dynamic content and swift human-to-machine interactions. To facilitate these advancements, this article introduces the concept of the "Meta-Operator," essentially an industrial worker guided by Industry 5.0 principles, engaging with Industrial Metaverse applications and surroundings through advanced XR devices. It also delves into the key technologies supporting this concept: Industrial Metaverse components, the latest XR technologies, and Opportunistic Edge Computing (OEC) for interacting with surrounding IoT/IIoT devices. Furthermore, the paper explores strategies for developing the next generation of Industrial Metaverse applications based on Industry 5.0 principles, such as standardization efforts, integrating AR/MR devices with IoT/IIoT solutions, and advancing communication and software architectures. Emphasis is placed on fostering shared experiences and collaborative protocols. Lastly, the article presents a comprehensive list of potential Industry 5.0 applications for the Industrial Metaverse and an analysis of the main challenges and research directions. It offers a holistic perspective and practical guidance for developers and researchers venturing into Industrial Metaverse applications.

	2024	Artificial Intelligence Review

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