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Untitled work

(1967) · Cited by 23,719

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Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392

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Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001) · Cited by 8,159

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217

Clustering-based undersampling in class-imbalanced data

Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.008 · Cited by 832

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Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

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Recent Applications of Explainable AI (XAI): A Systematic Literature Review

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Alabi, R. O., Elmusrati, M. S., Leivo, I. V. V., Almangush, A., & Mäkitie, A. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-35795-0 · Cited by 164

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Reliable water quality prediction and parametric analysis using explainable AI models

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Explainable AI for Credit Assessment in Banks

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Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

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Machine learning-based predictions of fatigue life for multi-principal element alloys

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SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

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An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

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Nwafor, C. N., & Nwafor, O. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104084 · Cited by 38

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Gramegna, A., & Giudici, P. (2022) · FinTech · doi:10.3390/fintech1010006 · Cited by 30

Systemic risk prediction using machine learning: Does network connectedness help prediction?

Wang, G., Chen, Y., Zhu, Y., & Xie, C. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103147 · Cited by 32

Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice

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A comprehensive systematic review of intrusion detection systems: emerging techniques, challenges, and future research directions

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Explainable Machine Learning Prediction for the Academic Performance of Deaf Scholars

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Deep learning based approaches to enhance energy efficiency in autonomous driving systems

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Nayak, S., Amin, A., Reghunath, S. R., Thunga, G., Acharya, U. D., Shivashankara, K. N., Attur, R. P., & Acharya, L. D. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105546 · Cited by 22

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Sharma, D., Boruah, D., Bakir, A. A., Ameen, A., & Paul, S. (2024) · Coatings · doi:10.3390/coatings14040404 · Cited by 19

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Averkin, A. N., Volkov, E. N., & Yarushev, S. (2024) · Journal of Computer and Systems Sciences International · doi:10.1134/s1064230724700138 · Cited by 19

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Sušnjak, T., & Griffin, E. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108653 · Cited by 17

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Nhat, N. M., & Ngo, D. (2025) · Cogent Economics & Finance · doi:10.1080/23322039.2025.2465971 · Cited by 17

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Malik, N., & Bzdok, D. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.014 · Cited by 19

Evaluation of Cost-Sensitive Learning Models in Forecasting Business Failure of Capital Market Firms

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Impulsivity Classification Using EEG Power and Explainable Machine Learning

Hüpen, P., Kumar, H., Shymanskaya, A., Swaminathan, R., & Habel, U. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065723500065 · Cited by 16

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Sušnjak, T., & Maddigan, P. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00387-5 · Cited by 16

Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels

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Development and characterization of novel wood-based composite materials for solar-powered atmospheric water harvesting: A machine intelligence supported approach

Zhang, X., Xu, Y., Han, S., Weng, X., Che, W., Gu, K., Guan, Y., Yu, Y., Hao, D., & Hou, J. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145061 · Cited by 15

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Jeong, T., Park, U., & Kang, S. W. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1033379 · Cited by 15

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Explainable Machine Learning for Phishing Site Detection: A High‐Efficiency Approach Using Boosting Models and SHAP

Uddin, K. M. M., Biswas, N., Rikta, S. T., Nur-A-Alam, M., & Mostafiz, R. (2025) · The Journal of Engineering · doi:10.1049/tje2.70110 · Cited by 14

Prediction of juvenile crime in Bangladesh due to drug addiction using machine learning and explainable AI techniques

Nesa, M., Shaha, T. R., & Yoon, Y. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00175-7 · Cited by 14

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Qiu, B., Shen, Z., Wu, S., Qin, X., Yang, D., & Wang, Q. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1235121 · Cited by 14

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Mazzini, G., & Bagni, F. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1277544 · Cited by 14

“Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack

Burger, C., Chen, L., & Le, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.792 · Cited by 15

Enhanced streamflow prediction using SWAT’s influential parameters: a comparative analysis of PCA-MLR and XGBoost models

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Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference

Iatrou, M., Tziouvalekas, M., Tsitouras, A., Evangelou, E., Noulas, C., Vlachostergios, D. Ν., Aschonitis, V. G., Arampatzis, G., Metaxa, I., Karydas, C. G., et al. (2024) · Agriculture · doi:10.3390/agriculture14040549 · Cited by 13

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Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu, T. (2022) · Proceedings of Engineering and Technology Innovation · doi:10.46604/peti.2023.10101 · Cited by 16

Knowing (Not) to Know: Explainable Artificial Intelligence and Human Metacognition

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Ding, X., & Kwon, T. J. (2024) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2023-0410 · Cited by 12

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Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery

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Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

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