Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 61 for “"SHAP"”.
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Enhancing Neural Network Performance through SHAP-based Latent Class Integration
… with Dynamic 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 …
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Igneous and metamorphic processes in the Shap Granite and its aureole
The Shap Granite outcrops in eastern Cumbria, N.W. England and is a post—orogenic granite intruded during the Lower Devonian (ie 394 Ma) into rocks of Ordovician to Siturian age. It is of adamellite composition and is notable in having megacrysts of orthoclase which crystallised late (relative to …
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IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
… using the Bayesian Optimization algorithm. SHAP analysis provides insightful individual and collective bot characteristic profiles. The model’s performance was evaluated using the augmented BCCC-Aposemat-Bot-IoT-24 dataset, built upon the Aposemat-Bot-IoT-23 dataset, and compared against …
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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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Adaptive systems for DDoS attacks detection and mitigation in IoT networks
… evolution for resource efficiency. The SHAP-Based Explanation and Lightweight 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 …
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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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A comparative study of the performance of machine learning methods and deep neural networks in intrusion detection
… normal and attack traffic. Then, we perform SHAP analysis to determine which features have greater effect on the models.
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Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning
… metrics from machine learning approaches such as SHAP values on tree models and Logistic Regression odds ratios to extract interactions of factors that can support policy decision making. Determinants of performance vary in these two countries, hence di erent policy implications and resource …
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Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather
… 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model.</p> <p>For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the …
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An Interpretable Multimodal Framework for Regional Organ Transplantation Outcomes
… these black-box models using a custom-designed SHAP analysis framework. Our study focuses on three distinct U.S. regions (Regions 1,2,3) with markedly different demographics and amounts of data on organ acceptances (Region 1: 43,126 offers with 2.19% acceptance rate, Region 2: 394,640 offers …
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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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Development and Validation of a Diagnostic Model - The Hypertension Population Risk Tool (HTNPoRT) - to Predict Hypertension and Describe Risk Profiles: A Population-Based Cross-Sectional Study of Canadians
… influential predictors of hypertension seen on SHAP-derived risk profiles, while predictability of adiposity measures differed across sex. Conclusions: The public and health policymakers can use the models and risk profiles of HTNPoRT to support planning and decision-making on addressing the …
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Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
… approaches implicitly approximate second-order Shapley-Taylor indices and extend CAM, GradCAM, LIME, SHAP, SBSM, and other methods to search engines. These extensions can extract pairwise correspondences between images from trained opaque-box models. We also introduce a fast kernel-based method …
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Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models
… explainable AI techniques such as LIME and SHAP to explain the models. By combining high precision with relatively low computational time, the SVM+VGG-19 hybrid model emerges as a robust way to deal with the MRI brain tumor segmentation problem, making it highly suitable for real-time image …
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Explainable AI: A Unified Approach Based on Cooperative Game Theory
… with the Möbius transform (MT) and Shapley Interactions (SIs) from cooperative game theory, we bridge perturbation- and gradient-based local explanations, as well as sensitivity-based and performance-based global explanations, providing a comprehensive perspective on feature …
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A comparative analysis of statistical and machine learning models with application in AI-powered stroke risk prediction
… with sampling methods improving data balance and SHAP analysis revealing key predictors. Across simulated and real-world data, the HNN improved sensitivity by at least 20% while sustaining strong overall accuracy, demonstrating its potential as an interpretable, scalable tool for pre-hospital LVO …
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Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance
… metabolism, and neuroimmune interactions. SHAP-based interpretation and pathway analysis highlight key resistance-associated programs, including immunosuppressive cytokine signaling, metabolic signaling, and neuroactive pathways such as calcium and cAMP signaling. Unsupervised clustering …
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A Data-Driven Approach for Predicting and Understanding Braking Conditions of Aircraft Landings
… for linked classifiers which maintains the shape of the runway condition code distribution. A forecast-focused version of the model only requires weather information from METAR reports, a description of the runway and aircraft type as input. The method is validated on a collection of 30 …
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Πρόβλεψη πρόωρου τερματισμού κλινικών δοκιμών με μοντέλα μηχανικής μάθησης vs κλασσικής παλινδρόμησης, βάση ανάλυσης των χαρακτηριστικών σχεδιασμού τους
… σημαντικότερες εκ των μεταβλητών εκτιμήθηκαν με SHAP plots και δευτερευόντως με logistic regression βάση των μεταβλητών που υπέδειξαν τα SHAP plots.. Διαπιστώθηκε ότι ο μειωμένος αριθμός συμμετεχόντων εμφανίζεται ως το κυρίαρχο χαρακτηριστικό που προβλέπει πρόωρη λήξη, ενώ ο αριθμός των δομών το …
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Leveraging Process Mining and Deep Learning to Improve Health and Safety Outcome Predictive Models
… with Paralytic Ileus, using ablation and SHAP analyses to identify critical clinical predictors. The third contribution focuses on predicting neurologic outcomes in out-of-hospital cardiac arrest (OHCA) patients, showing that early clinical data can effectively forecast outcomes and that …
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