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Fulltext COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, et al.

Results Phys, 2021 Dec;31:105045.
PMID: 34840938 DOI: 10.1016/j.rinp.2021.105045

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.
Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆

Mohammed MA, Lakhan A, Abdulkareem KH, Abd Ghani MK, Marhoon HA, Kadry S, et al.

J Adv Res, 2023 Oct 13.
PMID: 37839503 DOI: 10.1016/j.jare.2023.10.005

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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