Bayesian networks for interpretable machine learning and optimization

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

A fast and elitist multiobjective genetic algorithm: NSGA-II

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053

Adaptation in Natural and Artificial Systems

Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340

Estimating the Dimension of a Model

Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033

Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty.

Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 1×

Judgment under Uncertainty: Heuristics and Biases

Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650

"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

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,082

Untitled work

(1999) · Cited by 12,854

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561

Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches

Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Alfalahi, H., Dias, S. B., Khandoker, A. H., Chaudhuri, К. R., & Hadjileontiadis, L. J. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00494-0 · Cited by 58

Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

Flammini, F., Alcaraz, C., Bellini, E., Marrone, S., López, J., & Bondavalli, A. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3227113 · Cited by 43

Neuro-Symbolic Explainable Artificial Intelligence Twin for Zero-Touch IoE in Wireless Network

Munir, M. S., Kim, K. T., Adhikary, A., Saad, W., Shetty, S. S., Park, S.-B., & Hong, C. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303713 · Cited by 47

A systematic review of explainable artificial intelligence in urban building energy modeling: methods, applications, and future directions

Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42

Improved Z-number based Bayesian network modelling to predict cyber-attack risk for maritime autonomous surface ship (MASS)

Aydın, M., Sezer, Ş. İ., Akyüz, E., & Gardoni, P. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113416 · Cited by 38

Twenty-two years since revealing cross-site scripting attacks: A systematic mapping and a comprehensive survey

Hannousse, A., Yahiouche, S., & Nait-Hamoud, M. C. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100634 · Cited by 33

A Vision of Intelligent Train Control

Flammini, F., Donato, L. D., Fantechi, A., & Vittorini, V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05814-1_14 · Cited by 27

Machine-learning-guided design of MOF-based electrocatalysts for sustainable ammonia production

Han, B., de Carvalho, M., Zhang, J., Mao, H., & Yan, Q. (2026) · Chemical Communications · doi:10.1039/d5cc07118f · Cited by 23

Development of a Bayesian networks-based early warning system for wave-induced flooding

Garzón, J. L., Ferreira, Ó., Zózimo, A. C., Fortes, C. J. E. M., Ferreira, A. M., Pinheiro, L., & Reis, M. T. (2023) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2023.103931 · Cited by 21

Feature importance measure of a multilayer perceptron based on the presingle-connection layer

Zhang, W., Shen, X., Zhang, H., Yin, Z., Sun, J., Zhang, X., & Zou, L. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01959-7 · Cited by 19

Development of a Bayesian network-based early warning system for storm-driven coastal erosion

Garzón, J. L., Ferreira, Ó., Plomaritis, T. A., Zózimo, A. C., Fortes, C. J. E. M., & Pinheiro, L. (2024) · Coastal Engineering · doi:10.1016/j.coastaleng.2024.104460 · Cited by 18

A multi-model approach to construction site safety: Fault trees, Bayesian networks, and ontology reasoning

Shi, D., Gan, S., Zurada, J. M., Guan, J., Wang, F., & Weichbroth, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127817 · Cited by 16

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning

Ang, K. M., Lim, W. H., Tiang, S. S., Sharma, A., Eid, M. M., Tawfeek, S. M., Khafaga, D. S., Alharbi, A. H., & Abdelhamid, A. A. (2023) · Biomimetics · doi:10.3390/biomimetics8070525 · Cited by 18

ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach

Parrales–Bravo, F., Caicedo–Quiroz, R., Rodríguez-Larraburu, E., & Barzola–Monteses, J. (2024) · Informatics · doi:10.3390/informatics11020031 · Cited by 16

Linking urban structure types and Bayesian network modelling for an integrated flood risk assessment in data-scarce mega-cities

Zwirglmaier, V., & Garschagen, M. (2024) · Urban Climate · doi:10.1016/j.uclim.2024.102034 · Cited by 16

An Explainable Student Performance Prediction Method Based on Dual-Level Progressive Classification Belief Rule Base

Mai, J., Wei, F., He, W., Huang, H., & Zhu, H. (2024) · Electronics · doi:10.3390/electronics13224358 · Cited by 22

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin, S., Zhao, J., Li, J., Liu, X., Zhang, Y., Wang, S., Mei, Q., Chen, Z., & Gao, Y. (2022) · Entropy · doi:10.3390/e24081125 · Cited by 16

Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks

Altinok, O., & Güveniş, A. (2023) · Physica Medica · doi:10.1016/j.ejmp.2023.102671 · Cited by 15

Fast Robustness Enhancement for Dynamic IIoT Topology With Adaptive Bayesian Learning

Chen, N., Zhang, S., Zhou, X., Cao, S., & Qiu, T. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3571431 · Cited by 14

Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation

Foroumandi, E., & Moradkhani, H. (2025) · Water Resources Research · doi:10.1029/2025wr040078 · Cited by 22

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning

Wang, Z., Lan, Y., Xu, Z., Gu, Y., & Li, J. (2022) · Chinese Medical Sciences Journal · doi:10.24920/004102 · Cited by 14

Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph

Xia, X., & Qi, W. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01739-2 · Cited by 14

Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures

Ali, M., Chen, L., Latif, Q. B. A. I., Alsekait, D. M., Khan, A., Arif, K., Luqman, M. Z., Elminaam, D. S. A., Hamza, A., & Khan, M. (2024) · Composites Part C Open Access · doi:10.1016/j.jcomc.2024.100529 · Cited by 14

Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review

Hong, T., Xie, S., Liu, X., Wu, J., & Chen, G. (2025) · Value in Health · doi:10.1016/j.jval.2024.12.010 · Cited by 14

Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Fuster-Parra, P., Yáñez, A. M., López‐González, Á. A., Aguiló, A., & Bennasar‐Veny, M. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1035025 · Cited by 14

A tutorial on data mining for Bayesian networks, with a specific focus on IoT for agriculture

Krause, P. J., & Bokinala, V. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100738 · Cited by 13

Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini

Dlamini, W. M. D., Simelane, S. P., & Nhlabatsi, N. M. (2021) · Spatial Information Research · doi:10.1007/s41324-021-00421-6 · Cited by 13

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Laccourreye, P., Bielza, C., & Larrañaga, P. (2022) · Mathematics · doi:10.3390/math10121994 · Cited by 12

Towards safer steel operations with a multi model framework for accident prediction and risk assessment simulation

Pandey, S., Singh, A. K., Parhi, S., & Jha, S. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-96028-0 · Cited by 11

Weather Conditions Using Bayesian Networks: A Scene-Based Approach with CARLA Simulator

Al-Hindawi, R., & Alhadidi, T. I. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3562-4_32 · Cited by 10

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios

Zimmerman, R. M., Hernandez, E. J., Yandell, M. D., Tristani‐Firouzi, M., Silver, R. M., Grobman, W. A., Haas, D. M., Saade, G. A., Steller, J. G., & Blue, N. R. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-07095-6 · Cited by 12

Synthetic Data Improve Survival Status Prediction Models in Early-Onset Colorectal Cancer

Kim, H., Jang, W. S., Sim, W. S., Kim, H. S., Choi, J. E., Baek, E. S., Park, Y. R., & Shin, S. J. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00201 · Cited by 11

Predicting student burnout in blended environments: a complementary PLS-SEM and machine learning approach

Tu, Y., Huang, C., Wang, Q., Zhou, Y., Han, Z., & Huang, Q. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2415446 · Cited by 10

Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

Shui, K., Hou, K., Hou, W.-W., Sun, J., & Sun, H. (2023) · Journal of Mountain Science · doi:10.1007/s11629-023-8158-7 · Cited by 9

Artificial Intelligence Models: A literature review addressing Industry 4.0 approach

de Castro, H. C. G. A., Câmara, E., Ávila, P., Cruz-Cunha, M., & Ferreira, L. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.06.430 · Cited by 8

Evaluation and optimization of adaptive cruise control in autonomous vehicles using the car learning to act simulator: A performance evaluation under various weather conditions

Al-Hindawi, R., & Alhadidi, T. I. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100707 · Cited by 8

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

Liu, X., Zhao, J., Lin, S., Li, J., Wang, S., Zhang, Y., Gao, Y., & Chai, J. (2022) · Atmosphere · doi:10.3390/atmos13060959 · Cited by 8

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii, I. V., Mramorov, N. D., Usoltsev, S. D., & Kovalchuk, S. V. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081325 · Cited by 8

Machine learning, healthcare resource allocation, and patient consent

Webb, J. (2024) · The New Bioethics · doi:10.1080/20502877.2024.2416858 · Cited by 7

Operation Mode Recognition With Multiple Sensing Data for Electro-Mechanical Actuator Based on Deep-Shallow Fusion Network

Zhang, Y., Du, M., Luo, C., & Miao, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3369149 · Cited by 7

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7512289 · Cited by 7

Bayesian Network analysis of software logs for data‐driven software maintenance

del Rey, S., Martínez‐Fernández, S., & Salmerón, A. (2023) · IET Software · doi:10.1049/sfw2.12121 · Cited by 7

A novel feature selection method via mining Markov blanket

Khan, W. A., Kong, L., Noman, S. M., & Brekhna, B. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03863-z · Cited by 7

A New Hybrid Weighted Regional Drought Index to Improve Regional Drought Assessment

Mukhtar, A., Batool, A., Ali, Z., Qamar, S., Riaz, S., & Sammen, S. S. (2024) · Water Resources Management · doi:10.1007/s11269-024-03920-x · Cited by 6

Optimal pivot path of the simplex method for linear programming based on reinforcement learning

Li, A., Guo, T., Han, C., Li, B., & Li, H. (2024) · Science China Mathematics · doi:10.1007/s11425-022-2259-1 · Cited by 9

Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis

Kumar, R., Marla, K., Ravi, P., Sporn, K., Srinivas, R., Vaja, S., Ngo, A., & Tavakkoli, A. (2025) · Diagnostics · doi:10.3390/diagnostics15182295 · Cited by 5

Enhancing Innovation Management and Venture Capital Evaluation via Advanced Deep Learning Techniques

Quan, C., & Lu, B. (2023) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.335081 · Cited by 5

InterREC: An Interpretable Method for Referring Expression Comprehension

Wang, W., Pagnucco, M., Xu, C., & Song, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3251111 · Cited by 5

Achieving descriptive accuracy in explanations via argumentation: The case of probabilistic classifiers

Albini, E., Rago, A., Baroni, P., & Toni, F. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1099407 · Cited by 4

The research progress on effective connectivity in adolescent depression based on resting-state fMRI

Deng, X., Cui, J., Zhao, J., Bai, J., Li, J., & Li, K. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1498049 · Cited by 4

Fine-Tuning Fasttext Using Bayesian Optimization for Movie Review Sentiment Analysis

Halim, R., Girsang, A. S., & Riftiarrasyid, M. F. (2023) · International Conference on Technology, Engineering, and Computing Applications (ICTECA) · doi:10.1109/icteca60133.2023.10490756 · Cited by 4

Predicting Transport Mode in the Rabat Region: A Machine Learning Approach

Qbouche, K., & Rhoulami, K. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290210 · Cited by 4

The Application of Artificial Intelligence Models in Commercial Banks − Opportunities and Threats

Chwila, A. (2023) · Acta Universitatis Lodziensis Folia oeconomica · doi:10.18778/0208-6018.362.04 · Cited by 4

Bioinspired actor-critic algorithm for reinforcement learning interpretation with Levy–Brown hybrid exploration strategy

Xiao, W., & Li, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127291 · Cited by 5

Explainable automated wild-orchid identification combining deep neural networks and Bayesian networks

Apriyanti, D. H., Spreeuwers, L., & Lucas, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111961 · Cited by 4

IP-CAM: Class activation mapping based on importance weights and principal-component weights for better and simpler visual explanations

Zhang, W., Zhang, H., Zhang, X., Shen, X., & Zou, L. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104523 · Cited by 3

Early prediction of battery swelling via delta resistance features and optimized machine learning models to avoid thermal runaway

Kumar, D. K., & Sharma, M. K. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100389 · Cited by 3

Artificial intelligence in renewable energy technologies: Advancing optimization, integration, and carbon neutrality

Sohail, A. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100575 · Cited by 4

Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China

Zhou, S., Li, J., Zhang, J., Xu, Z., & Lu, X. (2024) · Frontiers in Earth Science · doi:10.3389/feart.2024.1444882 · Cited by 3

Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane

Pan, J., Qiao, X., Zhang, C., Li, B., Li, L., Li, G., & Qin, S. (2025) · Electronics · doi:10.3390/electronics14071329 · Cited by 3

Motivating explanations in Bayesian networks using MAP-independence

Kwisthout, J. (2022) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2022.11.012 · Cited by 3

Descriptive Accuracy in Explanations: The Case of Probabilistic Classifiers

Albini, E., Rago, A., Baroni, P., & Toni, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18843-5_19 · Cited by 3

Assessing Credibility in Bayesian Networks Structure Learning

Barth, V. B. D. O., Serrão, F. V., & Maciel, C. D. (2024) · Entropy · doi:10.3390/e26100829 · Cited by 2

Toward trustworthy AI with integrative explainable AI frameworks

Finzel, B. (2025) · it - Information Technology · doi:10.1515/itit-2025-0007 · Cited by 4

AIA: A Customized Multi-Core RISC-V SoC for Discrete Sampling Workloads in 16 nm

Zhao, S., Shah, N., Meert, W., & Verhelst, M. (2025) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2025.3561880 · Cited by 2

Determinants of Multidimensional Poverty for Rural Households in Vietnam With Bayesian Belief Network Model

Le, D. H., Hoang, V., Ly, L. T. K., Tan, D. D., & Huong, M. T. L. (2025) · Review of Development Economics · doi:10.1111/rode.13272 · Cited by 2

Multiple Bayesian models approach to assessing drivers of cultural heritage spatial distribution illustrated Lushan County in China

Liu, Y., Du, X., Bai, Y., Chen, Q., & Liu, D. (2025) · npj Heritage Science · doi:10.1038/s40494-025-01941-2 · Cited by 2

Detecting credit card fraud from transaction data streams with incremental time-aware liquid recurrent network

Cao, R., Mao, M., Wang, X., & Fang, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114729 · Cited by 4

Rolling Bearing Fault Diagnosis Based on Multi-Source Domain Joint Structure Preservation Transfer with Autoencoder

Jiang, Q., Shi, T., Hou, X., Miao, B., Zhang, Z., Jin, Y., Wang, Z., & Zhou, H. (2025) · Sensors · doi:10.3390/s26010222 · Cited by 2

A Novel Explainable Rumor Detection Model with Fusing Objective Information

Wang, J., Pi, D., Ping, M., & Chen, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46664-9_34 · Cited by 2

A simulated annealing‐based Bayesian network structure optimization framework for late morbidity prediction with a large prospective dataset

Stenhouse, K., McGeachy, P., Spampinato, S., Tanderup, K., Kirchheiner, K., Martell, K., Quirk, S., Viswanathan, A. N., & Roumeliotis, M. B. (2025) · Medical Physics · doi:10.1002/mp.17881 · Cited by 2

Sensor data modeling with Bayesian networks

SILVA, C. V. D., Rodrigues, A. S., Jorge, A., & Dutra, I. (2022) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict55358.2022.9887461 · Cited by 2

Availability Optimization of a Dragline Subsystem using Bayesian Network

Jana, D., Kumar, D., Gupta, S., & Gupta, K. K. (2023) · Journal of The Institution of Engineers (India) Series D · doi:10.1007/s40033-023-00457-y · Cited by 2

Can Data and Machine Learning Change the Future of Basic Income Models? A Bayesian Belief Networks Approach

Khalili, H. (2024) · Data · doi:10.3390/data9020018 · Cited by 2

Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data

Neelakantan, S., Hansson, A., Norell, J., Schött, J., Längkvist, M., & Loutfi, A. (2024) · Linköping electronic conference proceedings · doi:10.3384/ecp208021 · Cited by 1

ACIDA-IA: Addressing Class Imbalance Does not Always Improve Accuracy: a Study of Bayesian Networks for Predicting Preeclampsia

Parrales–Bravo, F., Caicedo–Quiroz, R., & Barzola–Monteses, J. (2024) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic61668.2024.10731129 · Cited by 1

Wireless Propagation Modeling Through Bayesian Networks

Fuster-Parra, P., & Galmés, S. (2024) · IEEE Access · doi:10.1109/access.2024.3510419 · Cited by 1

Using Stochastic Methods to Setup High Precision Experiments

Veljković, K., Kochovski, P., & Stankovski, V. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74627-7_30 · Cited by 1

Multi-Value Alignment for Ml/Ai Development Choices

Jethwani, H., & Lewis, A. C. F. (2025) · American Philosophical Quarterly · doi:10.5406/21521123.62.2.03 · Cited by 1

An uncertainty decomposition-based automated out-of-distribution detection framework- for trustworthy machinery fault diagnosis

Zeng, T., Jiang, H., Wang, X., Bai, Y., & Yi, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adfcf8 · Cited by 1

Rapid Fourier-transform infrared spectroscopic profiling of generation and graphitization of biocarbon from large-scale bioresource by integrating optimal machine learning

Kang, J., Fang, Y., Hong, J., Yu, J., Wang, Y., (49333), Q. Z., Yang, Y., Wang, Y., Fu, C., Peng, L., et al. (2026) · Green Carbon · doi:10.1016/j.greenca.2026.05.002 · Cited by 1

Evaluation and Implementation of Landscape Aesthetics Based on Cloud and Edge Computing

Qian, S. (2023) · Journal of Combinatorial Mathematics and Combinatorial Computing · doi:10.61091/jcmcc118-13 · Cited by 1

Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

Fuster-Parra, P., Huguet-Torres, A., Castro‐Sánchez, E., Bennasar‐Veny, M., & Yáñez, A. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307041 · Cited by 1

Applying Bayesian Networks to Predict and Understand the Student Academic Performance

Parrales–Bravo, F., Caicedo–Quiroz, R., Barzola–Monteses, J., & Cevallos-Torres, L. (2024) · International Conference on Advanced Computing & Communication Technologies (ICACCTech) · doi:10.1109/icacctech65084.2024.00123 · Cited by 1

Female Models in AI and the Fight Against COVID-19

Guerrero, C., & Mazuelas, S. (2022) · F1000Research · doi:10.12688/f1000research.123599.1 · Cited by 1

Risk patterns of consecutive adverse events in airway management: a Bayesian network analysis

Huber, M., Greif, R. T., Pedersen, T. H., Theiler, L. G., & Kleine‐Brueggeney, M. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2022.11.007 · Cited by 1

A Recursive Local Search Method of Separators for Bayesian Network Decomposition Structure Learning AlgorithmPreprint

Li, H., Jia, X., & Guo, H. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4046019

A recursive local search method of separators for Bayesian network decomposition structure learning algorithm

Jia, X., Li, H., & Guo, H. (2022) · Soft Computing · doi:10.1007/s00500-022-07647-y

Analyses of Photovoltaic Systems Performance with Fuzzy Inference Systems and Bayesian Networks

Cremasco, N. P., Delgado, M., Urbanetz, J., Melo, L. L. M., Leludak, J. A., & Fonseca, K. V. O. (2023) · Seminar on Power Electronics and Control (SEPOC) · doi:10.1109/sepoc58810.2023.10322604

Smart Process Failure Analysis based on Bayesian Networks and Knowledge Graphs

Müller, A., Simon, L., Petrović, O., Herfs, W., & Brecher, C. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463268

Quantifying uncertainty in Bayesian Networks structural learning

Barth, V. B. D. O., Caetano, H. O., Maciel, C. D., & Aiello, M. (2024) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais58494.2024.10570017

DCFA-iTimeNet: Dynamic cross-fusion attention network for interpretable time series prediction

Yuan, J., Wu, F., Zhao, L., Pan, D., & Yu, X. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05973-2

Probabilistic Error Guarantees for Abductive Inference

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