Examinando por Autor "Ghani, Mohd Khanapi Abd"

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    Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆
    (Elsevier B.V., 2024-12) Abed Mohammed, Mazin; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Ghani, Mohd Khanapi Abd; Marhoon, Haydar Abdulameer; Kadry, Seifedine; Nedoma, Jan; Martinek, Radek; García-Zapirain, Begoña
    Introduction: The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. Objective: This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. Methods: We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. Results: Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. Conclusion: Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture
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    Multi-agent systems in fog–cloud computing for critical healthcare task management model (CHTM) used for ECG monitoring
    (MDPI, 2021-10-19) Mutlag, Ammar Awad; Ghani, Mohd Khanapi Abd; Mohammed, Mazin Abed; Lakhan, Abdullah; Mohd, Othman ; Abdulkareem, Karrar Hameed ; García-Zapirain, Begoña
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
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    Voice pathology detection and classification using convolutional neural network model
    (MDPI AG, 2020-05-27) Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A.; Ghani, Mohd Khanapi Abd; Maashi, Mashael S.; García-Zapirain, Begoña; Oleagordia Ruiz, Ibon; Alhakami, Hosam; Al-Dhief, Fahad Taha
    Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrucken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.