scikit-optimize/scikit-optimize

Pesticide effect on earthworm lethality via interpretable machine learning

Kotli, M., Piir, G., & Maran, U. (2023) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2023.132577 · Cited by 15

Unity is strength: Improving the detection of adversarial examples with ensemble approaches

Craighero, F., Angaroni, F., Stella, F. A., Damiani, C., Antoniotti, M., & Graudenzi, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126576 · Cited by 15

Optimizing Photodegradation Rate Prediction of Organic Contaminants: Models with Fine-Tuned Hyperparameters and SHAP Feature Analysis for Informed Decision Making

Schossler, R. T., Ojo, S., & Yu, X. B. (2023) · ACS ES&T Water · doi:10.1021/acsestwater.3c00435 · Cited by 14

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

Luo, H., Cho, Y., Demmel, J. W., Li, X. S., & Liu, Y. (2024) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2024.2308216 · Cited by 14

On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Strong, G. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab983a · Cited by 18

Fusobacterium species are distinctly associated with patients with Lynch syndrome colorectal cancer

Salim, F., Mizutani, S., Shiba, S., Takamaru, H., Yamada, M., Nakajima, T., Yachida, T., Soga, T., Saitō, Y., Fukuda, S., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110181 · Cited by 14

Comparison of optimization algorithms for automated method development of gradient profiles

van Henten, G. B., Boelrijk, J., Kattenberg, C., Bos, T. S., Ensing, B., Forré, P. D., & Pirok, B. W. J. (2024) · Journal of Chromatography A · doi:10.1016/j.chroma.2024.465626 · Cited by 13

Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers

Cook, C. J., Miller, A. E., Barker, T. H., Di, Y., & Fogg, K. C. (2022) · Matrix Biology Plus · doi:10.1016/j.mbplus.2022.100117 · Cited by 11

Gradient and uncertainty enhanced sequential sampling for global fit

Lämmle, S., Bogoclu, C., Cremanns, K., & Roos, D. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116226 · Cited by 13

A Deep Learning Framework for End-to-End Control of Powered Prostheses

Nuesslein, C. P. O., & Young, A. J. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3374189 · Cited by 12

Corn grain and silage yield class prediction for zone delineation using high-resolution satellite imagery

Shajahan, S., Polson, B., Vaish, I., Marcaida, M., Longchamps, L., van Aardt, J., & Ketterings, Q. M. (2024) · Agricultural Systems · doi:10.1016/j.agsy.2024.104009 · Cited by 12

Monitoring of a Dynamic System Based on Autoencoders

Osmani, A., Hamidi, M., & Bouhouche, S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/254 · Cited by 13

Search-based Methods for Multi-Cloud Configuration

Lazuka, M., Parnell, T. P., Anghel, A., & Pozidis, H. (2022) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud55607.2022.00067 · Cited by 12

Graph Autoencoder-Based Embedded Learning in Dynamic Brain Networks for Autism Spectrum Disorder Identification

Noman, F. M., Yap, S.-Y., Phan, R. C. ‐., Ombao, H., & Ting, C. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9898034 · Cited by 12

High thermoelectric power factor in Ni–Fe alloy for active cooling applications

Li, S., Das, S. S., Wang, H., Pryga, K., Bati, S., Wiendlocha, B., Shiomi, J., Floro, J. A., Balachandran, P. V., & Zebarjadi, M. (2025) · Materials Horizons · doi:10.1039/d5mh00524h · Cited by 11

Reconstruction of Hydraulic Data by Machine Learning

Lapeyre, C., Cazard, N., Roy, P. T., Ricci, S., & Zaoui, F. (2020) · Springer water · doi:10.1007/978-981-15-5436-0_54 · Cited by 13

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications

Anjana, G., Nisha, K. L., & Arun Sankar, M. S. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154815 · Cited by 12

Subjective machines: Probabilistic risk assessment based on deep learning of soft information

Brito, M. P., Stevenson, M., & Bravo, C. (2022) · Risk Analysis · doi:10.1111/risa.13930 · Cited by 11

MethylNet: An Automated and Modular Deep Learning Approach for DNA Methylation AnalysisPreprint

Levy, J. J., Titus, A., Petersen, C. L., Chen, Y., Salas, L. A., & Christensen, B. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/692665 · Cited by 14

A machine learning approach to population pharmacokinetic modelling automation

Richardson, S., Irurzun‐Arana, I., Nowojewski, A., Zhou, D., Leander, J., Tang, W., Dearden, R., & Gibbs, M. A. (2025) · Communications Medicine · doi:10.1038/s43856-025-01054-8 · Cited by 10

Reaction-conditioned generative model for catalyst design and optimization with CatDRX

Kengkanna, A., Kikuchi, Y., Niwa, T., & Ohue, M. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01732-7 · Cited by 10

Cancer Type Classification in Liquid Biopsies Based on Sparse Mutational Profiles Enabled through Data Augmentation and Integration

Danyi, A., Jager, M., & de Ridder, J. (2021) · Life · doi:10.3390/life12010001 · Cited by 10

Sensitivity analysis on critical combinations of input parameters in DEM granular flow analysis

Xiao, J., Tozato, K., Nomura, R., Otake, Y., Terada, K., & Moriguchi, S. (2024) · Acta Geotechnica · doi:10.1007/s11440-024-02499-2 · Cited by 10

A methodology for adaptive AI-based causal control: Toward an autonomous factory in solder paste printing

Herchenbach, M., Weinzierl, S., Zilker, S., Schwulera, E., & Matzner, M. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104256 · Cited by 10

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

Gmeiner, A., Ivanova, M., Njage, P. M. K., Hansen, L. T., Chindelevitch, L., & Leekitcharoenphon, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-94321-6 · Cited by 10

An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics

Kappatou, C. D., Odgers, J., García‐Muñoz, S., & Misener, R. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c04583 · Cited by 10

Artificial Neural Network enabled P2D Model Deployment for End-of-Line Battery Cell Characterization

Turetskyy, A., Laue, V., Lamprecht, R., Thiede, S., Krewer, U., & Herrmann, C. A. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972181 · Cited by 10

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate modelsPreprint

van Hoof, J., & Vanschoren, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02289 · Cited by 10

The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique

Brook, J., Protat, A., Potvin, C. K., Soderholm, J., & McGowan, H. A. (2023) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-23-0004.1 · Cited by 9

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

Johnson, C. W., & Johnson, P. A. (2024) · Geophysical Research Letters · doi:10.1029/2024gl108288 · Cited by 9

Bacterial cell surface characterization by phage display coupled to high-throughput sequencing

Grun, C. N., Jain, R., Schniederberend, M., Shoemaker, C. B., Nelson, B., & Kazmierczak, B. I. (2024) · Nature Communications · doi:10.1038/s41467-024-51912-7 · Cited by 9

Predicting purification process fit of monoclonal antibodies using machine learning

Maier, A. J., Cha, M., Burgess, S., Wang, A., Cuellar, C., Kim, S. Y., Rajan, N. S., Neyyan, J., SENGUPTA, R., O’Connor, K., et al. (2025) · mAbs · doi:10.1080/19420862.2024.2439988 · Cited by 11

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Grbčić, L., Park, M., Müller, J., Zorba, V., & de Jong, W. A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109971 · Cited by 9

Robust filling of extra-long gaps in eddy covariance CO2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting

Liu, Y., Lucas, B., Bergl, D. D., & Richardson, A. D. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110438 · Cited by 9

Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms

Choi, J. C., Liu, Z., Lacasse, S., & Skurtveit, E. (2021) · Geosciences · doi:10.3390/geosciences11040181 · Cited by 9

Modulation transfer functions for audiovisual speech

Pedersen, N. F., Dau, T., Hansen, L. K., & Hjortkjær, J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010273 · Cited by 9

Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality

Bona, R., Fantini, D., Presti, G., Tiraboschi, M., Alonso-Martinez, J. I. E., & Avanzini, F. (2022) · Audio Mostly Conference (AM) · doi:10.1145/3561212.3561236 · Cited by 9

Estimating Error and Bias in Offline Evaluation Results

Tian, M., & Ekstrand, M. D. (2020) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3343413.3378004 · Cited by 9

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Sakkos, J. K., Santos‐Merino, M., Kokarakis, E. J., Li, B., Fuentes‐Cabrera, M., Zuliani, P., & Ducat, D. C. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011045 · Cited by 7

Selection of powerful radio galaxies with machine learning

Carvajal, R., Matute, I., Afonso, J., Norris, R. P., Luken, K., Sanchéz-Sáez, P., Cunha, P. A. C., Humphrey, A., Messias, H., Amarantidis, S., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245770 · Cited by 8

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Morand, L., Norouzi, E., Weber, M., Butz, A., & Helm, D. (2024) · Experimental Mechanics · doi:10.1007/s11340-024-01057-x · Cited by 8

Optimizing equity in intermittent water supply systems: A volume-driven demand and flow control approach

Hendrickson, G., & Perelman, L. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105615 · Cited by 8

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Torbati, S., Daneshmehr, A. R., Pouraliakbar, H., Asgharian, M., Tafti, S. H. A., Tim, D. S., & Heidari, A. (2024) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-024-01856-0 · Cited by 8

AI-aided process intensification of structures

Boy, O. C., Nogueira, I. B. R., Caprio, U. D., & Leblebici, M. E. (2025) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2025.110406 · Cited by 8

Operational Machine Learning Post‐Processing of Short‐Range Temperature, Humidity, Wind Speed and Gust Forecasts

Hieta, L., & Partio, M. (2025) · Meteorological Applications · doi:10.1002/met.70074 · Cited by 8

Predicting natural variation in the yeast phenotypic landscape with machine learning

Khaiwal, S., Chiara, M. D., Barré, B. P., Barrio‐Hernandez, I., Stenberg, S., Beltrão, P., Warringer, J., & Liti, G. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00136-y · Cited by 9

Quantum Computation of Conical Intersections on a Programmable Superconducting Quantum Processor

Zhao, S., Tang, D., Xiao, X., Wang, R., Sun, Q., Chen, Z., Cai, X., Li, Z., Yu, H., & Fang, W. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01314 · Cited by 11

Explaining inference queries with bayesian optimization

Lockhart, B., Peng, J., Wu, W., Wang, J., & Wu, E. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476249.3476304 · Cited by 8

To dee or not to dee: costs and benefits of altering the triangularity of a steady-state DEMO-like reactor

Schwartz, J. A., Nelson, A. O., & Kolemen, E. (2022) · Nuclear Fusion · doi:10.1088/1741-4326/ac62f6 · Cited by 8

The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate-Measures Using Machine Learning

Hillege, R. H. L., Lo, J. C., Janssen, C. P., & Romeijn, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50788-6_24 · Cited by 8

An open, analysis-ready, and quality controlled resource for pediatric brain white-matter researchPreprint

Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S. C., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M. P., Avelar‐Pereira, B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.24.481303 · Cited by 8

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

Bertelsen, S., Carlsen, S., Furbo, S., Nielsen, M. B., Obdrup, A., & Taaning, R. H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02240 · Cited by 8

Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and Experimental Validation

Ocampo, B. E., Altundas, B., Bock, M. J., Feiz, S., & Denmark, S. E. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00900 · Cited by 7

Molecular Representation Matters: Comparative Evaluation of Fingerprints, RDKit Descriptors, and Hashing Effects

Zhou, S., Li, J., Liu, Z., & Wang, D. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c03797 · Cited by 8

Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss

Effland, T., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00429 · Cited by 8

One-shot optimization for vehicle dynamics control systems

Thomaser, A., Kononova, A. V., Vogt, M.-E., & Bäck, T. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3533979 · Cited by 9

DriveWays: a method for identifying possibly overlapping driver pathways in cancer

Baali, I., Erten, C., & Kazan, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-78852-8 · Cited by 7

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

Callahan, J., Monogue, K., Villarreal, R., & Catanach, T. (2025) · Geophysical Journal International · doi:10.1093/gji/ggae458 · Cited by 6

Transition States Energies from Machine Learning: An Application to Reverse Water–Gas Shift on Single-Atom Alloys

Cheula, R., & Andersen, M. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c02818 · Cited by 7

Parameter identification of minifrac numerical tests using a gradient boosting‐based proxy model and genetic algorithm

Abreu, R., Mejia, C., Roehl, D. D. M., & Pereira, L. C. (2023) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3654 · Cited by 6

Covariant Jacobi-Legendre expansion for total energy calculations within the projector augmented wave formalism

Focassio, B., Domina, M., Patil, U., Fazzio, A., & Sanvito, S. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.110.184106 · Cited by 6

Transit communication via Twitter during the COVID-19 pandemic

Zhang, W., Barchers, C., & Smith‐Colin, J. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083221135609 · Cited by 6

Artificial Intelligence, Bioinformatics, and Pathology

Levy, J., Lu, Y., Montivero, M. K. S., Ramwala, O. A., McFadden, J. R., Miles, C., Diamond, A. G., Reddy, R., Reddy, R., Hudson, T. R., et al. (2022) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2023.01.001 · Cited by 7

Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit

Vente, T., Ekstrand, M. D., & Beel, J. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3610656 · Cited by 6

Mapping Glacier Basal Sliding Applying Machine Learning

Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet‐Leduc, B., Hulbert, C., et al. (2023) · Journal of Geophysical Research Earth Surface · doi:10.1029/2023jf007280 · Cited by 6

Evaluation of combinatorial algorithms for optimizing highly nonlinear structural problems

Rettl, M., Pletz, M., & Schuecker, C. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.111958 · Cited by 6

A Software Framework for Comparing Training Approaches for Spiking Neuromorphic Systems

Schuman, C. D., Plank, J. S., Parsa, M., Kulkarni, S., Skuda, N. D., & Mitchell, J. P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533881 · Cited by 6

Evaluating the impact of noise on the performance of the Variational Quantum EigensolverPreprint

Oliv, M., Matic-Flierl, A., Messerer, T., & Lorenz, J. M. (2023) · Research Square · doi:10.21203/rs.3.rs-2640456/v1 · Cited by 18

Enhancing the output of time series forecasting algorithms for cloud resource provisioning

Agulló, F., Gutierrez-Torre, A., Torres, J., & Berral, J. L. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107833 · Cited by 6

Multivariable modelling based on statistical and machine learning techniques for monthly precipitation forecasting in the eastern Amazon

Tedeschi, R. G., Neto, A. V. N., Costa, C. P. W., de Freitas, J. C. G., Rocha, R. D. L., Alves, R. C. D. O., Carvalho, E. C., & de Oliveira, E. C. L. (2025) · Frontiers in Earth Science · doi:10.3389/feart.2025.1576377 · Cited by 5

Geothermal Heat Flow Mapping of Germany Integrating Multi‐Geophysical and Geological Constraints With Uncertainty Quantification

Sobh, M., Gabriel, G., Christiansen, R. O., Al‐Aghbary, M., Tanner, D. C., & Gerhards, C. (2025) · Journal of Geophysical Research Solid Earth · doi:10.1029/2025jb031541 · Cited by 5

Modelling cosmic-ray transport: magnetised versus unmagnetised motion in astrophysical magnetic turbulence

Lübke, J., Reichherzer, P., Aerdker, S., Effenberger, F., Wilbert, M., Fichtner, H., & Grauer, R. (2025) · Journal of Plasma Physics · doi:10.1017/s0022377825100883 · Cited by 5

Complex genetic effects linked to plasma protein abundance in the UK Biobank

Sigurdsson, A. I., Gräf, J. F., Yang, Z., Ravn, K., Meisner, J., Thielemann, R., Webel, H. E., Smit, R. A. J., Niu, L., Mann, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67235-0 · Cited by 11

Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study

Ettling, T. J., Saba-Sadiya, S., & Roig, G. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385662 · Cited by 5

An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

Esmaeilzadeh, H., Ghodrati, S., Kahng, A. B., Kim, J. K., Kinzer, S., Kundu, S., Mahapatra, R., Manasi, S. D., Sapatnekar, S. S., Wang, Z., et al. (2024) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3664652 · Cited by 5

Applying Information Theory to Design Optimal Filters for Photometric Redshifts

Kalmbach, J. B., VanderPlas, J. T., & Connolly, A. J. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab684f · Cited by 6

Time Series Impact Through Topic Modeling

Cendrero, J., Gonzalo, J. A., Galletero, M., & Zapata, I. (2022) · IEEE Access · doi:10.1109/access.2022.3202960 · Cited by 5

Optimization of NQR excitation sequences using black-box techniques

Monea, C. (2020) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2020.106858 · Cited by 5

Learning context-dependent choice functions

Pfannschmidt, K., Gupta, P., Haddenhorst, B., & Hüllermeier, E. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2021.10.002 · Cited by 5

Prioritizing inspection of sewer pipes based on self-cleansing criteria

Vanegas, S., Montes, C., & Saldarriaga, J. G. (2022) · Urban Water Journal · doi:10.1080/1573062x.2022.2035408 · Cited by 5

Predicting the solidification time of low pressure die castings using geometric feature-based machine learning metamodels

Rosnitschek, T., Erber, M., Alber-Laukant, B., Hartmann, C., Volk, W., Rieg, F., & Tremmel, S. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.06.189 · Cited by 5

PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy

Stites, H., & Manor, U. (2025) · BMC Methods · doi:10.1186/s44330-024-00020-5 · Cited by 5

Characterization of core neutrals using passive measurements of the D α spectrum near the X -point in the DIII-D tokamak

Pratt, Q., Haskey, S. R., Odstrčil, T., Wilkie, G., Chrystal, C., & Angulo, A. M. (2026) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae363e · Cited by 4

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

Yuan, Y., Mao, X., Pan, X., Zhang, R., & Su, W. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01211 · Cited by 4

Toward Uncertainty‐Aware Hemolysis Modeling: A Universal Approach to Address Experimental Variance

Blum, C., Steinseifer, U., & Neidlin, M. (2025) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.70040 · Cited by 4

Evo-Panel: Dynamic Visualization Tool for Optimization Process

Kuo, S., Jiang, Y.-C., Hua, C.-Y., Chou, Y., & Kuo, S. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3299298 · Cited by 4

An empirical study of learning‐to‐rank for spare parts consumption in the repair process

Duarte, E., Moraes, D. D. H., & Padula, L. L. (2023) · Expert Systems · doi:10.1111/exsy.13441 · Cited by 4

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

Robinson, D. W., Avestruz, C., & Gnedin, N. Y. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3880 · Cited by 6

Upsampling Monte Carlo reactor simulation tallies in depleted LWR assemblies fueled with LEU and HALEU using a convolutional neural network

Berry, J., Romano, P. K., & Osborne, A. (2024) · AIP Advances · doi:10.1063/5.0169833 · Cited by 4

Alzheimer’s Disease Diagnosis via Deep Factorization Machine Models

Ronge, R., Nho, K. T., Wachinger, C., & Pölsterl, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_64 · Cited by 4

Utilizing AI to unveil the nonlinear interplay of convection, drift, and diffusion on galactic cosmic ray modulation in the inner heliosphere

Inceoglu, F., Pacini, A. A., & Loto'aniu, P. T. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-25277-0 · Cited by 4

Virtual reality (VR) as a testing bench for consumer optical solutions: a machine learning approach (GBR) to visual comfort under simulated progressive addition lenses (PALs) distortions

García, M. G., Sauer, Y., Watson, T., & Wahl, S. (2024) · Virtual Reality · doi:10.1007/s10055-023-00894-w · Cited by 4

matteo-brv @ DaDoEval: An SVM-based Approach for Automatic Document Dating

Brivio, M. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.7593 · Cited by 4

Machine Biometrics - Towards Identifying Machines in a Smart City Environment

Sidiropoulos, G. K., & Papakostas, G. A. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454230 · Cited by 4

AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH casePreprint

Fanelli, C., & Williams, M. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 4

Machine Learning for the LHCb SimulationPreprint

Anderlini, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07925 · Cited by 4

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learningPreprint

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Suppressing Oxygen-Loss-Driven Lattice Expansion Cascades in Disordered Rock-Salt Li-Ion Cathodes via Electrolyte Engineering

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Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10804382 · Cited by 2

scikit-package version 0.2.1

contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768434

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contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768404

C-OPT: composite optimization in Python

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A Data-Centric Optimization Workflow for the Python Language

Ziogas, A. N. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000586638