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    Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence
    (Mdpi, 2024) Yagin, Fatma Hilal; El Shawi, Radwa; Algarni, Abdulmohsen; Colak, Cemil; Al-Hashem, Fahaid; Ardigo, Luca Paolo
    Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.
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    Optimal allocation of hybrid PVDG and DSVC devices into distribution grids using a modified NRBO algorithm considering the overcurrent protection characteristics
    (Nature Portfolio, 2025) Belbachir, Nasreddine; Zellagui, Mohamed; Mahmoud, Haitham A.; Hashim, Fatma A.; El Shawi, Radwa; Yagin, Fatma Hilal; Al-Tam, Riyadh M.
    The never-ending issue of inadequate energy availability is constantly on the outermost layer. Consequently, an ongoing effort has been made to improve electric power plants and power system configurations. Photovoltaic Distributed Generators (PVDG) and compensators such as Distributed Static Var Compensator (DSVC) are the center of these recent advances. Due to its high complexity, these devices' optimum locating and dimensions are a relatively new issue in the Electrical Distribution Grid (EDG). A modified version of Newton Raphson Based Optimizer (mNRBO) has been carried out to optimally allocate the PVDG and DSVC devices in tested IEEE 33 and 69 bus EDG. The mNRBO algorithm integrates four parameters to enhance NRBO's performance by addressing its limitations in balancing exploration and exploitation. The article suggested novel Multi-Objective Functions (MOF), which have been considered to optimize concurrently the overall amount of active power loss (APL), voltage deviation (VD), relays operation time (TRELAY), as well as improve the coordination time interval (CTI) between primaries and backup relays set up in EDG. The proposed mNRBO algorithm surpasses its basic NRBO version, as long as another alternative algorithm, while providing very good results, such as minimizing the APL from 210.98 kW until 26.482 kW and 224.948 kW until 18.763 kW for the IEEE 33 and 69 bus respectively. Which proves the capability of the mNRBO algorithm of solving such power system challenges.