On explaining recommendations with Large Language Models: a review by Alan Said

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Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

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XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations by Mohammad Azad, Md Faraz Kabir Khan, Sameh Abd El-Ghany

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Computer-aided cholelithiasis diagnosis using explainable convolutional neural network by Dheeraj Kumar, Mayuri A. Mehta, Ketan Kotecha, Ambarish Kulkarni

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Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning by Qiyuan Liu, Jiawei Zhang, Jingwei Ge, Cheng Chang, Zhiheng Li, Shen Li, Li Li

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An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records by Shiqi Wei, Tianfang Xu

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Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models by Chi Zhang, Meng Yuan, Xiaoning Ma, Ping Wei, Yuanqi Su, Li Li, Yuehu Liu

Subjects: “…Explainable AI evaluation…”
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Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models by Ju-Hwan Lee, Dang Thanh Vu, Nam-Kyung Lee, Il-Hong Shin, Jin-Young Kim

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From physical activity patterns to cognitive status: development and validation of novel digital biomarkers for cognitive assessment in older adults by Ling-Jie Fan, Feng-Yi Wang, Jun-Han Zhao, Jun-Jie Zhang, Yang-An Li, Jia Tang, Tao Lin, Quan Wei

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A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease by K. M. Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf

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Shear Wave Velocity Prediction with Hyperparameter Optimization by Gebrail Bekdaş, Yaren Aydın, Umit Işıkdağ, Sinan Melih Nigdeli, Dara Hajebi, Tae-Hyung Kim, Zong Woo Geem

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Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering by Jing Wang, Wei Liu, Hairun Xie, Miao Zhang

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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy by Kimji N. Pellano, Inga Strumke, Daniel Groos, Lars Adde, Espen F. Alexander Ihlen

Subjects: “…Explainable AI…”
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PD_EBM: An Integrated Boosting Approach Based on Selective Features for Unveiling Parkinson's Disease Diagnosis With Global and Local Explanations by Fahmida Khanom, Mohammad Shorif Uddin, Rafid Mostafiz

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CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space by Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann, Michael Pühringer, Thomas Wolf, Maximilian Lübbesmeyer, Julian Heinrich, Julius Hillenbrand, Giulio Volpin, Marc Streit

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3MT Competition (EUSIPCO2024): A peek into the black box: Insights into the functionality of complex-valued neural networks for multichannel speech enhancement by Annika Briegleb

Subjects: “…Explainable AI…”
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Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers by Ehtesham Hashmi, Sule Yildirim Yayilgan, Muhammad Mudassar Yamin, Mohib Ullah

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Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation by Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Mikolaj, Caroline Taksøe-Vester, Anders Nymark Christensen, Morten B. S. Svendsen, Mette Hvilshøj Fabricius, Lisbeth Andreasen, Mads Nielsen, Martin Grønnebæk Tolsgaard

Subjects: “…Artificial intelligence, Fetal growth scans, Explainable AI, Human-AI collaboration…”
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AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecogniz... by Raquel M. Zimmerman, Edgar J. Hernandez, Mark Yandell, Martin Tristani-Firouzi, Robert M. Silver, William Grobman, David Haas, George Saade, Jonathan Steller, Nathan R. Blue

Subjects: “…Explainable artificial intelligence…”
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