SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569

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A Unified Approach to Interpreting Model Predictions

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

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

On Spectral Clustering: Analysis and an algorithm

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

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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

Explainable Machine Learning in Credit Risk Management

Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020) · Computational Economics · doi:10.1007/s10614-020-10042-0 · Cited by 635

Development and Validation of an Explainable Machine Learning Model for Predicting Invasive Fungal Infection in Acute‐On‐Chronic Liver Failure Within 28 Days

Xiong, F., Yan, J., Zhang, X., Zhou, Y., Ji, X., Jin, R., & Hou, Y. (2025) · Mycoses · doi:10.1111/myc.70090 · Cited by 1

A whole slide image-based risk score predicts prognosis and postmastectomy radiotherapy benefit in triple negative breast cancer patients

Chen, H., Zhang, L., Liu, P., Ji, L., Luo, C., Peng, B., Luo, T., Ye, F., & Zhong, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-12957-w · Cited by 1

Explainable Credit Intelligence: A Unified SHAP-Based Framework for Interpretable Risk Scoring across Corporate and Retail Lending Domains

Owolabi, O. S. (2025) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2025.134030 · Cited by 1

Explainable AI approach exploring the impact of carbon nanotubes on the compressive strength of concrete through various machine learning techniques

Wani, S. R., Suthar, M., & Al-Dala’ien, R. N. (2025) · Innovative Infrastructure Solutions · doi:10.1007/s41062-025-02363-6 · Cited by 1

Explainable adaptive ensemble learning with imbalance mitigation for manufacturing sector financial risk warning

Liu, W., Zou, Y., Liu, B., Tao, J., Lan, X., & Xia, M. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117577 · Cited by 1

Reviewing XAI for Credit Risk Assessment and Analysis: A Transparent Approach to Financial Decision-Making

Rana, V., & Gagneja, R. (2025) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito66076.2025.11241271 · Cited by 1

Advancing Credit Risk Assessment with Explainable AI and Embedded Learning

Kaur, G., Kaur, R., Panigrahi, G. R., & Shelke, N. M. (2025) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca66364.2025.11325714 · Cited by 1

Development and Validation of Predictive Models for Trifecta Achievement in Robot-Assisted Partial Nephrectomy for Renal Hilar Tumors: Preliminary Application of the Modified R.E.N.A.L. Score

Yuan, Y., Zhou, L., Yang, J., zheng, F., Zou, X., Liu, X., Chen, L., Hu, J., & Fu, B. (2026) · Journal of Endourology · doi:10.1177/08927790251408826 · Cited by 1

A 5-hydroxymethylcytosine-based noninvasive model for acute myocardial infarction in type 2 diabetes: implications in cardiovascular outcomes from a 5-year median follow-up study

YANG, K. Y., Qin, S., Xu, S., Cui, X., Zhang, Z., Cao, J., Xiao, M.-Y., Yang, Y., He, C., Zhou, X., et al. (2026) · Cardiovascular Diabetology · doi:10.1186/s12933-026-03087-2 · Cited by 2

Improved prediction of geopolymer concrete compressive strength using metaheuristic-enhanced extreme learning models with combined SHAP and LIME for sustainable construction

Alizamir, M., Gholampour, A., Kim, S., Heddam, S., & Ryu, G.-S. (2026) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2026.104043 · Cited by 1

A Multimodal fusion model for predicting nasopharyngeal necrosis after re-irradiation in recurrent nasopharyngeal carcinoma

Gao, X., Peng, Y., Miao, J., Lin, J., Fei, Z., Liu, X., Chen, M., Zhang, J., Qi, Z., Zhou, Q., et al. (2026) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2026.112513 · Cited by 1

XAI-IoT based real-time diabetes prediction using TabTransformer

Mukherjee, & De, D. (2026) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2026.100435 · Cited by 3

An adaptive Bayesian Physics-Informed neural network model for predicting the fatigue life of high-strength steel under non-uniform corrosion

Yu, X., Wang, Q., Khodadadi, N., Nanni, A., & Mosalam, K. M. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100112 · Cited by 1

Interpretable XGBoost Modeling Using SHAP and LIME: A Real-World Electric Vehicle Study

Demirsoy, İ. (2026) · Pamukkale University Journal of Engineering Sciences · doi:10.65206/pajes.1873321 · Cited by 1

Variations in Vegetation Cooling Efficiency Across 20 Major Cities Worldwide: The Interplay of Built-Up Fraction and Surface Properties

Hu, L., Neidhardt, H., Braun, A., & Hochschild, V. (2026) · Forests · doi:10.3390/f17060707 · Cited by 1

A Framework for Auditing Multilevel Models using Explainability Methods

Bhaumik, D., Dey, D., & Kayal, S. (2022) · European Conference on the Impact of Artificial Intelligence and Robotics · doi:10.34190/eciair.4.1.874 · Cited by 2

Implementation of Ensemble Algorithm with Data Pruning on Qualcomm Snapdragon 820c

Nandi, P., Anupama, K. R., Agarwal, H., Jain, A., Paliwal, S., & Jakhar, R. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307472 · Cited by 1

A Comparison of Global Explanations Given on Electronic Health Records

Duell, J., Seisenberger, M., & Fan, X. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-44981-9_34 · Cited by 1

Novel Multi-Layer Ensemble Algorithm for Fall Detection using wrist-worn devices

Nandi, P., Anupama, K. R., Jain, A., Paliwal, S., Tayal, O., & Agarwal, H. (2024) · International Conference on Information Networking · doi:10.1109/icoin59985.2024.10572064 · Cited by 1

Leveraging Electric Power Data for Enhanced Credit Assessment and Risk Control: A Framework for Data Monetization and Innovation

Zhao, Y., Guo, N., Wu, Y., Tian, Y., & Su, Y. (2024) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud62866.2024.00034 · Cited by 1

Multi-Modal Vision Transformer with Explainable Shapley Additive Explanations Value Embedding for Cymbidium goeringii Quality Grading

Wang, Z., He, X., Wang, Y., & Li, X. (2024) · Applied Sciences · doi:10.3390/app142210157 · Cited by 1

An Evaluation of Interpretable algorithms for Credit Scoring Model

Jiang, Y., Fu, Y., & Chen, B. (2024) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3724154.3724173 · Cited by 1

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi, A., & Moore, J. H. (2022) · Journal of Emerging Investigators · doi:10.59720/22-073 · Cited by 1

Meta-Heuristic Feature Optimization for Predictive Analysis on HRV Dataset and Semantic Knowledge Representation for Stress Management: A Case-Study Towards Ethical AIPreprint

Chatterjee, A., Ganesh, K., Reigler, M., & Halvorsen, P. (2023) · Research Square · doi:10.21203/rs.3.rs-3114142/v3 · Cited by 1

Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive ExplanationsPreprint

Huang, A. A., & Huang, S. Y. (2022) · Research Square · doi:10.21203/rs.3.rs-2075948/v2

Recommendation Modelling based on Contextual Weather Data in an Adaptive Coaching System: A Semantic and Explainable ApproachPreprint

Chatterjee, A., Ganesh, K., Pahari, N., & Riegler, M. A. (2023) · Research Square · doi:10.21203/rs.3.rs-2743778/v1

Meta-Heuristic Feature Optimization for Predictive Analysis on HRV Dataset and Semantic Knowledge Representation for Stress Management: A Case-Study Towards Ethical AIPreprint

Chatterjee, A., Ganesh, K., Reigler, M., & Halvorsen, P. (2023) · Research Square · doi:10.21203/rs.3.rs-3114142/v2

Developing a Machine Learning Model to Predict In-hospital Mortality in Sepsis-complicated Acute Pancreatitis: A Retrospective Study Utilizing MIMIC-III, MIMIC-IV and eICU Databases (Preprint)Preprint

Wang, Z., Wang, Z., Xu Xia, A., Zhou, Y., Li, F., Xue, Z., Cai, J., Chen, Z., & Li, Y. (2023) · doi:10.2196/preprints.53814

SignSense: AI Framework for Sign Language Recognition

Dilpak, P. V. M., Joshi, R. S., & Sonje, H. K. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-17257

Machine learning-based models for predicting overall and cancer specific survival in upper tract urothelial carcinoma: a retrospective cohort studyPreprint

Pan, Y., Ma, H., Jiao, B., Ding, Z., Zhang, Y., & Zhang, G. (2024) · Research Square · doi:10.21203/rs.3.rs-4259332/v1

The Three Gorges Dam Drives the Dynamic Evolution of Wetland Ecology in the Middle and Lower Reaches of the Yangtze RiverPreprint

Gong, Y., Zhang, S., Lin, D., Cai, Y., Lv, S.-B., Zheng, M., Hu, B., Lei, X., Xu, N., Wang, J., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4806498

Explainable Artificial Intelligence for Medical Science

Duell, J. (2024) · Cronfa (Swansea University) · doi:10.23889/suthesis.66607

The Influence of the Three Gorges Dam on the Evolution of Amphibious Snail in the Yangtze River BasinPreprint

Gong, Y., Zhang, S., Lin, D., Cai, Y., Lv, S.-B., Zheng, M., Hu, B., Lei, X., Xu, N., Wang, J., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4907264

Building and validating a predictive model for stroke risk in Chinese community-dwelling patients with chronic obstructive pulmonary disease using machine learning methodsPreprint

Chen, Y., Yu, Y., Yang, D., & Chen, X. (2024) · medRxiv · doi:10.1101/2024.09.12.24313533

Bibliometric Review of Artificial Intelligence in Credit Risk

Jain, V., Choudhury, T., Mehrotra, R., Anand, T., Prakash, N., & Tiwari, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3991-2_14

Towards More Explainable and Traceable AI: Gray-boxed Design in a Case of Microservice Allocation

García, J. J., Lacalle, I., Szmeja, P., Wasielewska-Michniewska, K., Ganzha, M., Salvador, C. E. P., Bădică, C., Fidanova, S. S., & Paprzycki, M. (2024) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista62901.2024.10683838

Cancer survival prediction using artificial intelligence: current status and future prospects

Shaikh, H., & Ali, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24028-7.00016-7

Predicting FTTH Take-Up Ratio in West Java Using SHAP and LIME Methods

Atmaja, W. H. K., Prabowo, H., Soeparno, H., Andryani, N. A. C., & Liam, N. (2024) · International Conference on Creative Communication and Innovative Technology (ICCIT) · doi:10.1109/iccit62134.2024.10701134

Building and validation based on machine learning methods: Predictive model for falls risk among community patients with chronic obstructive pulmonary disease in ChinaPreprint

Chen, Y., Yu, Y., Yang, D., & Chen, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4927688/v1

EST: Integrating Explainable AI and Semi-Supervised Ensemble Learning for Social Media Stress Detection

Tasnia, N., Hosen, M. H., Uddin, A., Foysal, M. F., Symon, N. K., Asgar, S., & Chowdhury, L. (2024) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas60761.2024.10796570

Development and validation of an interpretable risk prediction model for depressive symptoms among community-based patients with chronic obstructive pulmonary disease using machine learning techniques

Chen, Y., Yu, Y., Yang, D., Chen, X., & Chen, J. (2026) · BMC Pulmonary Medicine · doi:10.21203/rs.3.rs-5651501/v1

Machine Learning and Neural Network Methods Applied to Drug Discovery

de Sousa, D. S., da Silva, A. P., de Angelo, R. M., Chiari, L. P. A., Honório, K. M., & da Silva, A. B. F. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_4

Artificial intelligence based systems for combating communicable diseases

Saurav, & Kumar, T. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02735-2

Predicting Potential Non-Performing Loans Collectibility in MSMEs using Ensemble Stacking with SHAP-Boosting Algorithm

Wibowo, A. F. P., & Sanjaya, S. A. (2024) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti64779.2024.10963398

Expert-Like Automatic Tuning of Dialysis Parameters for Dialysis Hypotension Prediction and Prevention

Tsai, A., Chiu, T.-Y., Lin, H.-M., & Wang, J.-F. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-8764-7_6

Pixel-wise Relevance Propagation for Detailed Insights in Brain Tumour Classification

Narayankar, P. M., & Baligar, V. P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.555

Trends and Recommendations in Machine Learning Applications for Credit Risk Prediction

KOÇOĞLU, E., & Ersöz, T. (2025) · Springer tracts in additive manufacturing · doi:10.1007/978-3-031-84873-5_61

Integrating Explainable AI Techniques in Time Series Price Forecasting for Enhanced Interpretability and Decision-MakingPreprint

Cheng, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5373836

Using Explainable and Interpretable Artificial Intelligence Methods to Identify and Understand Factors Affecting ResiliencePreprint

Kamal, M. S., Sultana, N., Ip, R. H. L., Bewong, M., Islam, M. Z., & Rahman, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5390486

An interpretability framework for multimodal English learning behaviors using temporal graph networks and adaptive SHAP

Xu, Q., & Gan, Y. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251366556

Predicting Hla-B27 Positivity in Ankylosing Spondylitis Using Explainable Artificial Intelligence: A Comprehensive AnalysisPreprint

Taş, N., Şeferoğlu, B., Naldan, M. E., Naldan, H., Kırboğa, K. K., Işık, M., & Yağanoğlu, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5390703

Leveraging Local Interpretable Model-Agnostic Explanations (LIME) for Sentiment Analysis of News Articles

Tewari, P., Verma, H., Jaiswal, R., & Baghel, A. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5781-0_22

Predicting 28-Day Mortality in Critically Ill Patients Receiving Continuous Renal Replacement Therapy: A Novel Interpretable Machine Learning Approach

Zhang, T., Nan, Z.-H., Fan, X., Pang, J.-X., Zhao, C., Yan, X., Hu, Z., & Guo, S. (2025) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s533031

Financial Explainable AI for Credit Card Scoring: Fairness Challenges and Case Study

Teodorescu, M. H., & Sun, Y. (2025) · Springer eBooks · doi:10.1007/978-3-031-97007-8_3

A Review of Explainable AI Techniques for Enhancing Interpretability in Healthcare

Hemalatha, K. P. J., Saketh, G., Ravikannan, J. P., & Jayanth, G. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6715-4_22

Sign language recognition: A comparative study of traditional deep learning approaches

Kulkarni, K., & Wakchaure, M. A. (2025) · AIP conference proceedings · doi:10.1063/5.0291720

Is Bitcoin a Conditional Safe Haven During Market Stress? An Explainable Machine Learning Perspective Using Global Equity DivergencePreprint

Manjunath, B. R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5600296

Is Bitcoin a Conditional Safe Haven During Market Stress? An Explainable Machine Learning Perspective Using Global Equity DivergencePreprint

Manjunath, B. R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5692169

Forecasting sales and identifying purchase-driving attributes of perishable products with explainable artificial intelligence based on the example of yogurt

Brückner, K., Eiglsperger, J., Emberger‐Klein, A., Grimm, D. G., & Menrad, K. (2025) · British Food Journal · doi:10.1108/bfj-03-2025-0291

Handling Imbalanced Credit Data with Interpretable ML Techniques

Thakur, G. K., Kumari, N., Naik, S. S. B., Yellaboina, M. P., Girish, K. K., & Bhowmik, B. R. (2025) · IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) · doi:10.1109/discover66922.2025.11259019

Unraveling the Determinants of Urban Energy Intensity in China: An XGBoost-SHAP Analysis of 286 CitiesPreprint

Fang, S., Li, R., Li, M., Zhang, R., Feng, N., Wang, Y., Chen, W., & Lu, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5891309

Loan Acceptance Prediction and Analysis Using Explainable AI Techniques

Kanaji, A., Mahale, S., Gattani, A., Rai, R., Tawde, P., & Sawant, V. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-9971-1_12

LightGBM Scorecard based on SHAP values

Choi, Y., & Cha, E. (2026) · Computational Economics · doi:10.1007/s10614-025-11194-7

A Multimodal fusion model for predicting nasopharyngeal necrosis after reirradiation in recurrent nasopharyngeal carcinomaPreprint

Gao, X., Peng, Y., Miao, J., Lin, J., Fei, Z., Liu, X., Chen, M., Zhang, J., Qi, Z., Zhou, Q., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8516588/v1

Cascade systems as an implementation of a gray-box architecture: A case study in traceability for microservice scaling

Garcia, J. A., Lacalle, I., Szmeja, P., Wasielewska-Michniewska, K., Hołda, P., Ganzha, M., Salvador, C. E. P., Bădică, C., Fidanova, S. S., & Paprzycki, M. (2026) · Computer Science and Information Systems · doi:10.2298/csis250425014g

Bridging Heterogeneous Experimental Data and Soil Mechanics: An Interpretable Machine Learning Framework for Displacement-Dependent Earth Pressure

Zeng, T., Zhang, Z., & Zeng, Y. (2026) · Buildings · doi:10.3390/buildings16030601

STREAM: State Trajectory Representation & Evolution-Aware MonitoringPreprint

Namvar, A., Ram, S., Labaki, W. W., Galbán, S., Lugogo, N. L., & Galbán, C. J. (2026) · medRxiv · doi:10.64898/2026.02.03.26345478

Challenges of Developing an AI-Powered Dashboard in a Higher Education Institution in South Africa

Mashile, E. O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9195-3.ch007

Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients

Zhu, B., Jiang, H., Zhang, C., Wang, Q., Yang, L., Yang, H., Li, R., Li, J., Pang, X., Zheng, Y., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1704671

Interpretable Deep Reinforcement Learning for Dynamic Truck Dispatching in Open-Pit Mining

Marticorena, L. F. R., & Angelo, D. I. A. Z. (2025) · International Conference of Management and Industrial Engineering · doi:10.56177/12icmie2025.202

A novel method of fusing Bert-LSTM and XAI for social media disinformation identification

Xia, H., Islam, N., Zhu, D. L., Zhang, Z., Behl, A., & Roohanifar, M. (2026) · Annals of Operations Research · doi:10.1007/s10479-026-07063-7 · Cited by 1

Corporate Boundary, Hedging Decisions, and Financial Market Shocks: An AI-Driven Risk Balancing Path for Corporate Sustainable DevelopmentPreprint

Zhang, H., Xia, L., & Hong, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6309313

Exploring the Nexus Between Sustainability Index and Central European Stock Markets Competitiveness: Evidence Through Time–Frequency Analysis and SHAP

Janková, Z., Jana, D. K., Páleš, M., Fleacă, E., & Dostál, P. (2026) · Journal of Forecasting · doi:10.1002/for.70141

Precise disease heterogeneity and progression quantification in MSA and Parkinson’s disease using machine learning

Gebre, R. K., Raghavan, S., Tora, M. E. J. D., Fought, A. J., Reid, R. I., Low, P. A., Singer, W., & Vemuri, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-45949-5

A SAFE Model for AI Risk Management

Giudici, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-13422-6_2

Hybrid CNN-transformer architecture for personal credit risk prediction with comparative insights into model explainability

Lee, J., Lee, Y., Hong, J., Chung, Y., & Kim, E. K. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08486-6

Analyzing and explaining data-driven Artificial Intelligence Models by argumentation

Trenquier, H. (2023) · theses.fr (ABES) · doi:10.70675/963acbcaz72d2z48f2za396z0cf62886238c

Apprentissage multiannotateur et explications communes en logique du premier ordre

Aoual, M. K. (2024) · theses.fr (ABES) · doi:10.70675/4056956dzb9abz47ecz8f08zfe994fc46d42

Tree-Based XAI for an Efficient and Transparent Model in Predicting Bank Term Deposit Subscriptions

Sahlaoui, H., Laaroussi, A., Assbai, H., Sallah, A., & Hessane, A. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-97825-8_119

Identifying and predicting key industry–education integration projects via explainable artificial intelligence (XAI) based machine learning models

Feng, Y., & Park, J. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3868

Explainable AI Techniques for Quality Improvement in Automotive Manufacturing Processes

Dacal-Nieto, Á., Vicente-Estévez, D., Raña, B., Paradela, A., Bugarín, A., Areal, J. J., & Alonso-Ramos, V. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-18802-1_7

Explainable AI (XAI) Driven Neural–Machine Learning Ensemble for Business Customer Churn Prediction

Mahin, M. R., Prince, A. A., Samad, A. R., Ahmed, M. R., Arman, S. H., & Hasan, M. M. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491640

Enhance Expert‐Semantic Feature Extraction and Combine Feature Importance and Attention Scores for Just‐in‐Time Defect Prediction and Localization

Zhao, Y., Yuan, X., Gong, L., & Huang, Z. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70118

Audit-Ready Fraud Detection: Aligning Gradient Boosting and SHAP with GDPR and SOX Compliance

Pai, D., Pappu, K., & Gangaiah, Y. K. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541999

Data-Driven Optimization of Size-Aware T6 Heat Treatment Parameters for A356 Aluminum Alloy

Tiwari, T., Gan, T., & Patel, J. B. (2026) · Metals · doi:10.3390/met16060615

Development and validation of an interpretable machine learning model to predict high-risk patients with acute small bowel obstruction

Zhao, Y., Wang, H., Zhao, E., Zhao, G., Zhang, J., Chen, S., Li, Y., Zhao, Y. L., Zhuo, W., Lyu, G., et al. (2026) · World Journal of Emergency Medicine · doi:10.5847/wjem.j.1920-8642.2026.063

Customer Segmentation and Creditworthiness Profiling via Multi-view Clustering and Predictive Risk Scoring

Shannaq, B., Sadriwala, K. F., Alshar’e, M., Osman, N. E., Bani‐Ismail, B., & Ali, O. (2026) · Journal of Information & Knowledge Management · doi:10.1142/s0219649226500450

Application of Explainable AI and Uncertainty Quantification in Credit Risk Assessment

Rambauli, M., Ravele, T., & Sigauke, C. (2026) · Risks · doi:10.3390/risks14070152 · Cited by 3

Machine Learning‐Based Prediction of Brain Metastasis at Initial Diagnosis in Small‐Cell Lung Cancer: Model Development and SHAP Interpretation Study

He, Y., Qin-ling, J., Zhai, Y., Zhao, X., & Huang, Z. (2026) · Cancer Reports · doi:10.1002/cnr2.70625

Recognizing distressed finances of China a-share listed companies: insights from interpretable machine learning approach

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