Explainable AI for DeepFake Detection by Nazneen Mansoor, Alexander I. Iliev

Subjects: “…explainable artificial intelligence…”
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Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators by Luis Mata, Marco Sousa, Pedro Vieira, Maria Paula Queluz, Antonio Rodrigues

Subjects: “…Explainable artificial intelligence…”
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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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An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios by Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán, Félix J. García Clemente

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Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment by Gazi Mohammad Imdadul Alam, Sharia Arfin Tanim, Sumit Kanti Sarker, Yutaka Watanobe, Rashedul Islam, M. F. Mridha, Kamruddin Nur

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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

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Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It by Yasir Hafeez, Khuhed Memon, Maged S. AL-Quraishi, Norashikin Yahya, Sami Elferik, Syed Saad Azhar Ali

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Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations by Thavavel Vaiyapuri

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From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic by Bekir Cetintav, Ahmet Yalcin

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Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models by Geemi P. Wellawatte, Philippe Schwaller

“…Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. …”
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Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…Techniques such as Explainable Artificial Intelligence (XAI), transfer learning, and privacy-preserving machine learning can be a way to unlock the limitations of adopting AI techniques in a wider range of rehabilitation settings. …”
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An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival by Sören Schleibaum, Jörg P. Müller, Monika Sester

“…To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. …”
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A Comprehensive Survey of Deep Learning Approaches in Image Processing by Maria Trigka, Elias Dritsas

“…Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

“…This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. …”
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Convolutional neural network prediction of the particle size distribution of soil from close-range images by Enrico Soranzo, Carlotta Guardiani, Wei Wu

“…Additionally, we applied explainable artificial intelligence techniques to enhance the transparency and interpretability of the model’s predictions. …”
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A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement by Mehrdad Ebrahimi, Mohammad Rastegar

“…This paper proposes employing ensemble learning methods and explainable artificial intelligence tools to build an accurate data-driven model. …”
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Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights by Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. …”
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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

“…While various research endeavors have explored obesity classification, many existing approaches lack reliability due to limited integration with explainable artificial intelligence (XAI) methodologies. In this study, we propose a robust machine learning framework that incorporates Explainable AI (XAI) principles to accurately estimate obesity levels and provide insights into the factors influencing the predictions. …”
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Interpreting seasonal droughts over the Yangtze River Basin utilizing anomalies of local-scale atmospheric circulation by Hao Yin, Zhiyong Wu, Hai He

“…Using the 3D Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, we identified 43 seasonal drought events in the YRB from 1971 to 2022. An Explainable Artificial Intelligence framework was then applied to assess the influence of these local-scale factors on the Standardized Precipitation Index (SPI3) at the grid scale. …”
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Towards unbiased skin cancer classification using deep feature fusion by Ali Atshan Abdulredah, Mohammed A. Fadhel, Laith Alzubaidi, Ye Duan, Monji Kherallah, Faiza Charfi

“…Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model’s decisions. …”
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