Examinando por Autor "Abed Mohammed, Mazin"

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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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    Ítem
    A new multi-agent feature wrapper machine learning approach for heart disease diagnosis
    (Tech Science Press, 2021-01-12) Elhoseny, Mohamed; Abed Mohammed, Mazin; Mostafa, Salama A.; Abdulkareem, Karrar Hameed; Maashi, Mashael S.; García-Zapirain, Begoña; Mutlag, Ammar Awad; Maashi, Marwah Suliman
    Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%-83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.