Dhafar Hamed Abd

American scientific publishing group

In an era of rapidly evolving technology, traditional cloud computing struggles to meet the demands of resource-intensive smart devices. This necessitates a shift towards Edge Computing (EC), which brings computation and data storage closer to the network’s edge, enhancing efficiency and reducing latency. This is particularly crucial for the Internet of Things (IoT), where supporting mobility, location awareness, and real-time processing are paramount. However, the scalability of EC applications is significantly influenced by network parameters and the capabilities of the computing system. This paper proposes a novel system architecture for Industry 5.0 that leverages the synergy between 6G networks, autonomous vehicles, Augmented Reality (AR), IoT, and edge intelligence to revolutionize transportation systems. Our approach integrates AR for enhanced user interfaces, utilizes IoT for data acquisition and control, and employs edge computing for real-time decision-making. Our experimental results demonstrate a strong correlation between processing speed and network bandwidth. While increasing either parameter individually enhances overall system performance. The two-tier architecture, combined with the Entity Objects (EO) model, demonstrates superior scalability compared to traditional approaches. By distributing processing tasks and leveraging the resources of other edge servers, the system can handle increasing numbers of AVs and data loads without compromising performance.

In this article, a new combination of an explainable machine learning approach with a fuzzy evaluation framework is proposed to improve the diagnostic performance and interpretation of rheumatic and autoimmune diseases. This work addresses three major challenges: (i) overlapping symptoms and complex clinical presentations, (ii) the lack of interpretability in traditional machine learning models, and (iii) the difficulty of selecting the best diagnosis model. To overcome these challenges, a new dataset was collected from Iraq’s hospitals and health centers between 2019 and 2024. The size of dataset is 12,085 patients and includes 14 features in seven classes (rheumatoid arthritis, reactive arthritis, ankylosing spondylitis, Sjogren syndrome, systemic lupus erythematosus, psoriatic arthritis, and normal). The dataset is subjected to extensive preprocessing with attribute imputation (mean and mode), encoding categorical features, and balancing the data to pass it to 12 different machine learning models. Performance is evaluated based on precision, recall, F-score, kappa, Hamming loss, Matthews correlation coefficient, and accuracy to identify the best model. To select the optimal model, we apply fuzzy decision by opinion score method (FDOSM). The FDOSM process involves assessments from three domain experts to ensure a robust and well-rounded evaluation. Furthermore, the explainable artificial intelligence (XAI) technique provides global and local explanations for model predictions. Local interpretable model explanations (LIME) were used as explanations and significantly increased the transparency and reliability of the clinical decision-making process. The results show that the FDOSM yields gradient boosting with a 0.1333 score and a rank of 1, is the best model with an accuracy of 86.89%, precision of 87.35%, and kappa of 84.51%. The best model using XAI to increase confidence and trustworthiness in clinical decision-making and healthcare applications.

An accurate prediction of mortality in intensive care units (ICUs) is crucial to improving patient outcomes and optimizing resource allocation. However, existing methods often lack high-dimensional data and interpretability, rely on outdated equipment, or fail to integrate multifaceted clinical data effectively. This study aims to develop a hybrid predictive model integrating machine learning (ML), the fuzzy decision-by-opinion score method (FDOSM), and explainable artificial intelligence (XAI) to enhance the accuracy, transparency, and clinical applicability of the prediction of mortality forecasts. The dataset was used from Zigong Fourth People’s Hospital and consisted of 1,210 patients and 182 attributes. Using Chi-square feature selection, we identified statistically significant features (e.g. vital signs, lab results) from ICU patient records, reducing dimensionality while preserving predictive power. We evaluated multiple ML models (including LightGBM, Extra Trees, Support Vector Machine, K -Nearest Neighbours, XGBoost, Random Forest, and Artificial Neural Network) using a comprehensive dataset of ICU patient records, which includes vital signs, laboratory results, and clinical interventions. The FDOSM was then integrated to assess model outputs against domain-specific criteria, enabling nuanced risk stratification and enhancing decision support in critical care. XAI techniques were used to interpret the outputs of the best-performing model, improving trust in the predictions. Our hybrid approach achieved superior performance, with the Extra Tree algorithm trained on the refined feature set obtaining the highest rank 1, with a weight of 0.14375, an AUC of 88.173%, a precision of 90.244%, and an accuracy of 88.167%. The results demonstrate that combining Chi-square-driven feature selection with ML-FDOSM and XAI integration significantly improves mortality prediction, offering a reliable and transparent tool for critical care settings.

Comorbidity, the simultaneous existence of multiple medical conditions in a patient, is a major challenge in healthcare. Comorbidity is highly threatening for healthcare systems, which requires innovative solutions over traditional methods. The medical field is challenged by accurately diagnosing these intertwined diseases of coexisting ailments and anticipating their rise. The current diagnostic approaches are time-consuming and inaccurate, hinder effective treatment, and delay accurate results for the patient. Artificial intelligence can provide an effective method for early prediction of comorbidity risks. In this study, various artificial intelligence models are used, and a clinical dataset of 271 patients is utilized to diagnose comorbidity. In which a hybrid diagnosis model is proposed based on the intersection between machine learning (ML) and feature selection techniques for the detection of comorbidity. Fuzzy decision by opinion score method is utilized as a sophisticated tool to select the most representative ML for prediction. Extensive simulation results showed an accuracy rate of 91.463 using AdaBoost ML. Furthermore, utilizing the fuzzy decision by opinion score technique, we were able to confirm that the best model using all features as well as the chi square and KBest features is the AdaBoost, which scored the smallest value of 0.204 and hence confirm that it is the best selected ML model for comorbidity.