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Showing 1 to 20 of 25 for “"Shapley Additive Explanations"”.
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Explainable Deep Learning Approach for Detecting Money Laundering Transactions in Banking System
… and explaining the predictions using SHapley Additive exPlanations (SHAP) XAI method. The results showed that the CNN model outperformed other models, indicating better handling of compliance risk. On the contrary, CNN model showed higher number of false positives compared to other …
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Data Analytics of Wettability Alteration with Surfactants in Carbonate Reservoirs
… surfactant performance. Based on RF model, Shapley additive explanations approach was applied to obtain new insights to promote the understanding of wettability alteration with surfactants. Also, a new procedure integrating RF model with Powell’s method was built to optimize surfactant …
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A data-driven approach to evaluating the effectiveness and adverse outcomes of antidepressant exposure using longitudinal real-world EHR data
… achieved through the integration of SHAP (SHapley Additive exPlanations), which enhances the interpretability of the models for clinical practitioners by detailing how patient characteristics and treatment variables contribute to model decisions. The findings suggest that personalized …
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A Multitask Deep Learning Framework for Clinical Decision-Making in Assisted Reproductive Technology
… baselines. To enhance clinical trust, we apply SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), which consistently uncover biologically meaningful variables, including biomarkers such as Anti-Müllerian Hormone (AMH), patient age, and the day-3 …
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Revamping Manufacturing Systems: Utilization of Data Driven Models, Interpretable Machine Learning, and Data-Product Stakeholder Flow Analysis
… learning models in the Semiconductor Fab. The SHapley Additive exPlanations (SHAP) methodology was applied to generate beeswarm and bar plots for the SHAP results, which identified the most important features to improve the throughput prediction. The study showed that Machine E utilization has …
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Three Essays on Corporate Governance and CEO Dismissal
… across multiple performance dimensions. Using SHapley Additive exPlanations (SHAP) values, the study decomposes model predictions to reveal the relative importance of firm-specific factors, where market-based performance metrics contribute materially to dismissal likelihood. Long-short …
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An analysis of the performance and interpretability of machine learning classification algorithms to predict long-term share returns on the JSE
… Forest were further investigated using SHAP (SHapley Additive exPlanations) global summary plots to identify the most influential input features and to analyse the interpretability of these algorithms. The study found that ensemble-based classification algorithms, i.e. XGBoost, Random Forest …
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Machine Learning Methods for Churn Prediction and Infrastructure Resilience
… future infrastructure vulnerabilities. Employing SHapley Additive exPlanations (SHAP), I interpret model predictions, highlighting critical factors such as precipitation, windspeed, and atmospheric pressure. Additionally, I propose frameworks for quantifying financial impacts of future outages and …
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Prediction of Gravel Streambed Embeddedness Using Explainable AI and Machine Learning Techniques
… work was the use of XAI techniques, particularly SHapley Additive exPlanations, to interpret model outputs and uncover the physical processes driving embeddedness. Bankfull shear velocity consistently emerged as the most important predictor, with soil depth, basin relief, and land cover also …
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Adaptive systems for DDoS attacks detection and mitigation in IoT networks
… DDoS Attack Detection (SHIELD) system uses SHapley Additive exPlanations (SHAP) for interpretability of individual predictions. The final objective addresses adaptive mitigation through a Game-Theoretic DDoS Defense Strategy Model (GT-DDSM) that dynamically adjusts defense strategies based …
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The Role of Explainable Artificial Intelligence in Data Science [Il Ruolo dell'Intelligenza Artificiale Esplicabile nella Scienza dei Dati]
… Local Interpretable Model-agnostic Explanations (LIME) e SHapley Additive exPlanations (SHAP), dimostrando la versatilità insita nelle metodologie di interpretabilità. Come primo passo, applichiamo questi approcci interpretativi per estrarre caratteristiche fondamentali dai dati …
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Convolutional Neural Networks for Robust Fynbos Leaf Classification: Enabling Trustworthy Machine Learning in Botanical Science
… model. The model evaluation process makes use of SHapley Additive exPlanations (SHAP), a tool for visualising model predictions, to contribute to the explian-ability of the model and to develop trust and confidence in machine-learning algorithms, with the ultimate aim of providing a tool to merge …
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A novel approach by integrating physically and Machine leaning-based models for landslide susceptibility assessment
… of ML methods (i.e., black box nature) using SHapley Additive exPlanations (SHAP) algorithm. The proposed method was tested at Chukha, Bhutan (area of 1,879.5 km²), a frequent landslide-prone area in the Himalayan region. As the first objective, the study develops a novel model based on …
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Intelligent Pricing Systems for Hotels: From Prediction to Optimization [Sistema di prezzi intelligente per hotel: dalla previsione all'ottimizzazione]
… Spiegabile (XAI). In particolare, gli SHapley Additive ExPlanations (SHAP) vengono utilizzati per fornire spiegazioni chiare e accessibili delle previsioni del modello, sia a livello globale che locale. Inoltre, la tesi presenta l'architettura dell'intero sistema, mostrando come la …
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Predicting household poverty with machine learning methods: the case of Malawi
… smaller feature subsets. The Filter method and SHapley Additive exPlanations method were used to rank the importance of the features, and smaller data subsets were selected based on these rankings. The highest prediction accuracy achieved for the full panel data set of 486 features was 87%. When …
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Machine Learning Enabled Inorganic Synthesis Planning and Materials Design
… evaluate model interpretability using the SHAP (SHapley Additive exPlanations) approach to gain insight into factors influencing suitability of synthesis route and reaction conditions. We find that the aforementioned models are capable of learning subtle differences in target material …
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Predictive Maintenance in Rail Transportation: An Explainable Machine Learning Approach
… feature importance, permutation importance, and SHapley Additive exPlanations, are integrated to deliver global and local interpretability. These insights enable the derivation of actionable threshold-based rules for maintenance alerts, bridging the gap between predictive modelling and …
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Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia
… Memory (LSTM) network (MAE: 0.41 vs. 0.46). SHapley Additive exPlanations (SHAP) analysis identified lagged population growth and unmet demand as the most influential predictors, alongside temperature and drought-related variables. To support adaptive water governance, a Non-Dominated Sorting …
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Enhancing Neural Network Performance through SHAP-based Latent Class Integration
… Integration Network)—that integrate SHAP (SHapley Additive exPlanations)-based feature importance into the model training process to uncover and leverage latent substructures in data. Rather than clustering in the raw feature space, both architectures rely on absolute SHAP values to group …
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Analysis of Association Between Demographic, Socioeconomic, and Built Environment Factors and Pedestrian Safety Using Traditional and AI Approaches
… of machine learning models, specifically SHapley Additive exPlanations (SHAP), in explaining how these factors affect Equivalent Property Damage Only (EPDO) rates. The findings reveal that auto-oriented network density is consistently associated with higher pedestrian crash risks, while …
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