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Optimal coordination of directional overcurrent relays in active distribution networks with high penetration of distributed energy resources using a data-driven strategy

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Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis

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Classification Methods for Market Making in Auction Markets

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Aqueous dissolution of Li-Na borosilicates: Insights from machine learning and experiments

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Automatic Classification of OSA related Snoring Signals from Nocturnal Audio RecordingsPreprint

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Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns

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CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

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Predicting reticuloruminal pH and subacute ruminal acidosis of individual cows using machine learning and Fourier-transform infrared spectroscopy milk analysis

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Classification of neurological and mental health disorders based on multimodal approaches: A comprehensive review

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Saraiva, M. N., & Mendonça, C. R. (2025) · Journal of Applied Physics · doi:10.1063/5.0289970 · Cited by 1

Are acquirer stock price reactions to M&A announcements in any way predictable? A machine-learning analysis

Neto, J. Q. F., Bozos, K., Dutordoir, M., & Nikolopoulos, K. I. (2025) · Journal of the Operational Research Society · doi:10.1080/01605682.2025.2562956 · Cited by 1

When Values Matter More than Behavior: Behavioral Integrity in Air Travel and Climate Policy Support

Im, H. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6050273 · Cited by 1

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Fardana, N. I., Isnanto, R. R., & Nurhayati, O. D. (2025) · International Conference on Informatics and Computational Sciences (ICICoS) · doi:10.1109/icicos68590.2025.11329876 · Cited by 1

Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study

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AI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation: A Case Study of Titanium Microdrilling

Fattahi, S., Azarhoushang, B., Paknejad, M., & Kitzig-Frank, H. (2026) · Machines · doi:10.3390/machines14020196 · Cited by 1

Machine learning prediction and calibration of cellulose-based solid-phase extraction performance for pharmaceuticals across aqueous matrices

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Subregional thalamic atrophy in Progressive Supranuclear Palsy: A machine learning study

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A new predicting model of an entrepreneurial behaviour: interpretable machine learning on GEM data for multi-year and multi country analysis

Duplock, R., Perano, M., Casali, G. L., & Regno, C. D. (2026) · International Entrepreneurship and Management Journal · doi:10.1007/s11365-026-01217-6 · Cited by 1

Machine learning-assisted prediction and optimization of microwave-assisted esterification of levulinic acid for ethyl levulinate production

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Learning networks hyper-parameter using multi-objective optimization of statistical performance metrics

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Using pretrained models in ensemble learning for date fruits multiclass classification

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Automated phenotyping of mild cognitive impairment and Alzheimer’s disease and related dementias using electronic health records

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LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small MoleculesPreprint

Shaikh, F., Tai, H. K., Desai, N., & Siu, S. W. I. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12923474.v1 · Cited by 1

LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecular CompoundsPreprint

Shaikh, F., Tai, H. K., Desai, N., & Siu, S. W. I. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12923474 · Cited by 1

Extending Multi-Input Linear Correction to Energy Representation Theory: Accurate Solvation Free Energy Prediction Independent of Volume Information

Maruyama, Y., Matubayasi, N., & Yoshida, N. (2026) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c02734 · Cited by 1

3D-QSAR, ADMET and Molecular Docking Study of a Series of 2-Substituted 1H-Benzimidazole-4-Carboxamide Derivatives Inhibitors Against Enteroviruses

(2021) · Biointerface Research in Applied Chemistry · doi:10.33263/briac126.81288143

Big Data und Künstliche Intelligenz: Chancen und Anforderungen für einen erfolgreichen und nachhaltigen Einsatz im Gesundheitswesen

Varghese, J. (2022) · Springer eBooks · doi:10.1007/978-3-658-36226-3_32

Predictive Analytic Model of Ectopic Pregnancy Diagnosis: Statistics vs. Machine Learning MethodsPreprint

Rueangket, P., Rittiluechai, K., & Prayote, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4144531

Forecasting M&A Shareholder Wealth Effects to Prevent Value-Destroying Deals: Can It Be Done?Preprint

Neto, J. Q. F., Bozos, K., Dutordoir, M., & Nikolopoulos, K. I. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4199363

Aprendizado de máquina aplicado em dados de biossensores para diagnóstico de câncer e COVID-19

Braz, D. C. (2022) · doi:10.11606/t.76.2022.tde-16112022-161304

Human-aware application of data science techniques

Coma-Puig, B. (2022) · doi:10.5821/dissertation-2117-365522

Implementation of K-means Clustering Algorithm to Analyze the Familial Sentiments Towards COVID-19 Vaccination For Elementary School Students in Kalawat District

Kairupan, I. Y., Wikarsa, L., & Kembuan, A. (2023) · Journal of Information Technology and Its Utilization · doi:10.56873/jitu.6.2.5280

Modelagem de dinâmicas sociais interagentes em redes complexas

Pineda, A. M. (2023) · doi:10.11606/t.55.2023.tde-09012024-122031

Künstliche Intelligenz in der Medizin

Varghese, J. (2024) · Springer eBooks · doi:10.1007/978-3-658-43236-2_50

Effect of Spatial Scale, Color Infrared and Sample Size on Learning Poverty from Aerial ImagesPreprint

Burger, J. M. S., Boonstra, H. J., & van den Brakel, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4779343

Additional file 1 of Alterations in gut microbiota and host transcriptome of patients with coronary artery disease

Chen, L., Mou, X., Li, J., Li, M., Ye, C., Gao, X., Liu, X., Ma, Y., Xu, Y., & Zhong, Y. (2023) · Figshare · doi:10.6084/m9.figshare.24499411

An ACS-Stacking Prediction Model Based on Interpretable Machine LearningPreprint

Wang, G., Liu, J., & Wang, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4188796/v1

A Pipeline for Monitoring and Maintaining a Text Classification Tool in Production

Ohata, E. F., Mattos, C. L. C., & Rêgo, P. A. L. (2024) · Anais do Seminário Integrado de Software e Hardware (SEMISH) · doi:10.5753/semish.2024.2438

Effect of spatial scale, color infrared and sample size on learning poverty from aerial images

Burger, J., Boonstra, H. J., & van den Brakel, J. (2024) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2024.101304 · Cited by 1

Hybrid Feature Selection Approach Combining Filters and Feature Importance on the PPMI Dataset

Calomino, C., Quattrone, A., & Bianco, M. G. (2024) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3678935.3678938

An Interpretable Sparse Graph Contrastive Learning Approach for Identifying Breast Cancer Risk VariantsPreprint

Gudhe, N. R., Hartikainen, J. M., Tengström, M., Pylkäs, K., Winqvist, R., Kosma, V., Behravan, H., & Mannermaa, A. (2025) · medRxiv · doi:10.1101/2025.01.13.25320451

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Subramanian, V. (2025) · doi:10.1002/9781394155408.ch16

Can patient-reported data improve predictions about who will be a high-need, high-cost patient in British Columbia?

Trenaman, L., Guh, D. P., Bryan, S., McGrail, K. M., Karim, M. E., Sawatzky, R., Yu, M., Parker, M., Wheeler, K., & Harrison, M. J. (2025) · Quality of Life Research · doi:10.1007/s11136-025-04008-8 · Cited by 1

Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models

Khanmohammadi, R., Ghassemi, M. M., Verdecchia, K., Ghanem, A. I., Luo, B., Chetty, I. J., Bagher‐Ebadian, H., Siddiqui, F., Elshaikh, M. A., Movsas, B., et al. (2025) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubaf010 · Cited by 7

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Liu, Y., Jing, Z., Lin, Z., Wang, Y., Chen, Q., Liu, M., & Chen, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-7298061/v1

Exploring Bone Health Determinants in Youth Athletes Using Supervised and Unsupervised Machine Learning

Retzepis, N.-O., Avloniti, A. A., Kokkotis, C., Stampoulis, T., Balampanos, D., Draganidis, D., Gkachtsou, A., Grammenou, M., Karaiskou, A. M., Kelaraki, D., et al. (2025) · Dietetics · doi:10.3390/dietetics4040044

Stratifying dementia risk factors: A prediction model and hypothesis‐driven analysis

Arnold, D., Barros, R. C., Ferrari‐Souza, J. P., Bastiani, M. A. D., Zimmer, E. R., & Borelli, W. V. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70870 · Cited by 2

Accurate prediction of gain- and loss-of-function variants in GABA A receptorsPreprint

Boßelmann, C. M., Ortiz, S., Dahl, R. S., Liao, V. W. Y., El‐Kamand, S., Lin, S. X. N., Kan, A. S. H., Brünger, T., Lal, D., Lerche, H., et al. (2025) · medRxiv · doi:10.64898/2025.12.03.25341551

Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health

Hussein, M., Li, L., Falkner, K. L., Buck, M. J., Diaz, P. I., & Hatzikirou, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-59740-z

Additional file 2 of Alterations in gut microbiota and host transcriptome of patients with coronary artery disease

Chen, L., Mou, X., Li, J., Li, M., Ye, C., Gao, X., Liu, X., Ma, Y., Xu, Y., & Zhong, Y. (2024) · Figshare · doi:10.6084/m9.figshare.24499440

Additional file 3 of Alterations in gut microbiota and host transcriptome of patients with coronary artery disease

Chen, L., Mou, X., Li, J., Li, M., Ye, C., Gao, X., Liu, X., Ma, Y., Xu, Y., & Zhong, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26635263

Deep learning-based blockchain framework for fraud detection using multilevel supervision in hierarchical generative hashing

Hamdan, I. K. A., Wulamu, A., Zhang, D., & Tiwari, A. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02916-2

Validation of three - horizon soil maps based on different soil texture datasets for Denmark

Motevalli, A., Iversen, B. V., Pesch, C., & Børgesen, C. D. (2026) · Geoderma Regional · doi:10.1016/j.geodrs.2026.e01049

Multi-objective machine learning for tailored design of lanthanum-based phosphate adsorbents: Balancing capacity, affinity, and kineticsPreprint

Shan, S., Li, X., Wang, C., Xu, Y., Sun, J., Liu, S., & Li, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6204370

An attention-driven framework for drug repurposing against human metapneumovirus: Integrating predictive modeling with docking validation

Roudsari, A. M., Hooshmand, M., Majidifar, S., Danaeifar, M., & Rabiee, H. R. (2026) · Antiviral Research · doi:10.1016/j.antiviral.2026.106366

Neuromorphic photonic systems for information processing

Mwamsojo, N. (2023) · theses.fr (ABES) · doi:10.70675/16013f38zb45dz4205za1d3zc1e15e7f5ff0

Analysis of Chip Electronic Components’ Typical Yield in Taping Process Based on Virtual Metrology

Zhang, S.-Q., Chen, L., Fu, J., Yang, C., & Chen, G.-L. (2026) · Italian National Conference on Sensors · doi:10.3390/s26082292

Forecasting agricultural commodity prices from satellite data satellite data using machine learning

Teste, F. (2025) · doi:10.70675/c559c3afzd62cz423azaeebz7e1171876efc

Artificial intelligence in petroleum and natural gas prospect evaluation and prediction

Wang, Q.-C., Chen, D.-X., Li, S., Chen, Y., Chen, X. H., Wang, M.-S., Yang, Z.-Q., & Wang, F.-W. (2026) · Petroleum Science · doi:10.1016/j.petsci.2026.04.020

Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike?

Ullah, U., Laudanna, S., Sorbo, A. D., & Visaggio, C. A. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/8117975

Profit-Optimal Machine Learning for Purchase Intent Prediction: Calibration, Temporal Validation, and Economic Constraints

Hossain, R., Ali, L. E., Ripon, K. S. N., Bulbul, M. F., & Tonmoy, M. M. H. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491498

The Small-Sample Illusion: How Data Quantity, Not Quality, Drives Machine Learning Performance on Tabular DataPreprint

Nguyen, H. T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6798854

Family Networks and Childcare Choices: A Predictive Machine Learning Approach

Soler, N., Emery, T., & Kanas, A. (2026) · Sociological Science · doi:10.15195/v13.a23

Human-Related Acoustic Event Classification Under Real Background Interference: The Role of Controlled Acoustic Dataset Generation

Castaño, D., Fáñez, M., Sedano, J., Candás, J. L. C., Ávila, J. R., & de la Cal, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29292-6_16

Combining Online HPLC‐ABTS Screening With Machine Learning to Predict the Bioactivity of Antioxidant Components in Pandani Tectorii Radix et Rhizoma

Chen, S., Ma, Y., Li, D. Q., Yang, L., Wang, J., Hu, Q., & Ma, H. (2026) · Biomedical Chromatography · doi:10.1002/bmc.70526

Optimized convolutional neural network - kernel ridge regression-error correction method: an advanced model for predicting soil saturated hydraulic conductivity

Kahkhamoghadam, P., Chari, M. M., & Ehteram, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-55796-z

Machine learning and hybrid modelling of particle breakage in a jet mill

Jackson, C., Yu, M., Stitt, E. H., Liu, L., & Gallen, R. W. (2026) · Digital Chemical Engineering · doi:10.1016/j.dche.2026.100322

Local Surrogate Relationships Between Soil Texture Fractions and Near-Surface Hydro-Structural Properties for Hydrological Parameterization in High-Andean Catchments

Mera-Parra, C., Ochoa‐Cueva, P., Sinoga, J. D. R., & Sarango, P. J. D. (2026) · Soil Systems · doi:10.3390/soilsystems10070068

AI-Based Modeling of Dielectric Properties for Hydrocarbon Contaminated Soil

Al–Mattarneh, H., Ismail, R. M. A., & Mousa, M. S. (2026) · International Conference on Artificial Intelligence for Sustainable Engineering and Innovation (AISEI) · doi:10.1109/aisei68628.2026.11572777

Hybrid AI-driven decision framework for climate-resilient neighborhood retrofits in tropical savanna climate

Harkouss, F., & Fardoun, F. (2026) · Journal of Housing and the Built Environment · doi:10.1007/s10901-026-10320-7

Predictive ignorance: Using machine learning to predict what we do not understand

Naser, M. Z. (2026) · TAO. · doi:10.1016/j.tao.2026.100033

Factors associated with delayed diagnosis of fibrotic interstitial lung disease: a retrospective cohort study

Kim, H. J., Shah, S., SHETTY, S., Pimple, P. M., Rai, A., Petterson, C., Lloyd, C., Vaughn, I. A., Lamerato, L., Hicks, W., et al. (2026) · BMC Pulmonary Medicine · doi:10.1186/s12890-026-04420-x

Detecting cotton Verticillium wilt within and across multiple fields with Sentinel‐2 imagery and machine learning

Joshi, A., Bishop, T. F. A., Weaver, T., Deo, R., & Filippi, P. (2026) · Agronomy Journal · doi:10.1002/agj2.70485

Drivers, latent signatures and parsimonious screening of organophosphate triester mixtures in silver pomfret from Vietnamese coastal waters (2021−2023)

Tran‐Lam, T., Nguyen, T. T., Pham, P. T., Pham, M. T., & Dao, Y. H. (2026) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2026.120127

Modeling Pedestrian Red Signal Violations at Signalized Intersections Using Machine Learning

Tria, T. N., Abdullah, M., Rahman, S. M., & Al-Ahmadi, H. M. (2026) · International Conference on Frontiers of Engineering and Emerging Technologies (FET) · doi:10.1109/fet68771.2026.11601355

Integrating Mutation-Derived and Expression Features from Single-Cell RNA Sequencing: Pitfalls of Standard Cross-Validation in Small-Cohort Settings

Kunikeyev, A. D., Salybekov, A. A., Yerimbetova, A., & Omarov, B. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146429

Deep-Learning-Based Prediction of TMS-Induced Motor Evoked Responses from Flattened Individualized Cortical Electric Field Maps

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