Random decision forests

Neural network ensembles

Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941

A survey on ensemble learning

Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-8208-z · Cited by 1,928

Deep Learning for Anomaly Detection: A Survey

Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240

A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities

Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036

Identification of autism spectrum disorder using deep learning and the ABIDE dataset

Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.08.017 · Cited by 1,016

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922

Convolutional neural networks in medical image understanding: a survey

Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917

Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches

Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100059 · Cited by 841

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707

Towards Large-Scale Small Object Detection: Survey and Benchmarks

Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., & Han, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3290594 · Cited by 683

A Survey on Ensemble Learning for Data Stream Classification

Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1

McCulloch, J. A., Davar, D., Rodrigues, R. R., Badger, J. H., Fang, J. R., Cole, A. M., Balaji, A. K., Vetizou, M., Prescott, S. M., Fernandes, M. R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01698-2 · Cited by 472

Methodologies for Cross-Domain Data Fusion: An Overview

Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix083 · Cited by 389

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24412 · Cited by 339

Artificial Intelligence Techniques for Stability Analysis and Control in Smart Grids: Methodologies, Applications, Challenges and Future Directions

Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327

Understanding and capturing people's privacy policies in a mobile social networking application

Sadeh, N., Hong, J., Cranor, L., Fette, I., Kelley, P., Prabaker, M., & Rao, J. (2009) · Personal and Ubiquitous Computing · doi:10.1007/s00779-008-0214-3 · Cited by 318

Machine learning for renewable energy materials

Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Kardani, N., Zhou, A., Nazem, M., & Shen, S.-L. (2021) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2020.05.011 · Cited by 285

Affordance detection of tool parts from geometric features

Myers, A., Teo, C. L., Fermuller, C., & Aloimonos, Y. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139369 · Cited by 273

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S., & Zeller, G. (2021) · Genome Biology · doi:10.1186/s13059-021-02306-1 · Cited by 269

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

Pardakhti, M., Moharreri, E., Wanik, D., Suib, S. L., & Srivastava, R. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00056 · Cited by 254

Artificial intelligence for aging and longevity research: Recent advances and perspectives

Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.003 · Cited by 246

Decision tree classifiers for automated medical diagnosis

Azar, A. T., & El-Metwally, S. M. (2013) · Neural Computing and Applications · doi:10.1007/s00521-012-1196-7 · Cited by 242

Accelerating antibiotic discovery through artificial intelligence

Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234

Explainable decision forest: Transforming a decision forest into an interpretable tree

Sagi, O., & Rokach, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.013 · Cited by 189

Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling

Guleria, P., & Sood, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11221-2 · Cited by 178

Automated trading with performance weighted random forests and seasonality

Booth, A., Gerding, E., & McGroarty, F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.009 · Cited by 154

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

Malafeev, A., Laptev, D., Bauer, S., Omlin, X., Wierzbicka, A., Wichniak, A., Jernajczyk, W., Riener, R., Buhmann, J., & Achermann, P. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00781 · Cited by 151

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Fluke, C. J., & Jacobs, C. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1349 · Cited by 149

Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms

Kim, J., Park, M., Kim, H., Cho, S., & Kang, P. (2019) · Applied Sciences · doi:10.3390/app9194018 · Cited by 134

Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization

Herbol, H. C., Hu, W., Frazier, P., Clancy, P., & Poloczek, M. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0106-7 · Cited by 121

Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction

Zhong, H., Wang, Y., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5479 · Cited by 113

Missing data imputation through the use of the random forest algorithm

Pantanowitz, A., & Marwala, T. (2009) · Advances in Intelligent and Soft Computing · doi:10.1007/978-3-642-03156-4_6 · Cited by 106

Disease Prediction via Graph Neural Networks

Sun, Z., Yin, H., Chen, H., Chen, T., Cui, L., & Yang, F. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3004143 · Cited by 98

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96

Molecular Insights from Conformational Ensembles via Machine Learning

Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2019.12.016 · Cited by 93

Deep Differentiable Random Forests for Age Estimation

Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., & Yuille, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2937294 · Cited by 91

Decision trees do not generalize to new variations

Bengio, Y., Delalleau, O., & Simard, C. (2010) · Computational Intelligence · doi:10.1111/j.1467-8640.2010.00366.x · Cited by 89

Machine Learning in Human Olfactory Research

Lötsch, J., Kringel, D., & Hummel, T. (2019) · Chemical Senses · doi:10.1093/chemse/bjy067 · Cited by 71

Cognitive Analytics

Gudivada, V. N., Irfan, M. T., Fathi, E., & Rao, D. L. (2016) · Handbook of Statistics · doi:10.1016/bs.host.2016.07.010 · Cited by 70

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Seal, D. B., Das, V., Goswami, S., & De, R. K. (2020) · Genomics · doi:10.1016/j.ygeno.2020.03.021 · Cited by 69

Heavy metal pollution in Mongolian-Manchurian grassland soil and effect of long-range dust transport by wind

Qin, M., Jin, Y., Peng, T., Zhao, B., & Hou, D. (2023) · Environment International · doi:10.1016/j.envint.2023.108019 · Cited by 63

Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance

Kimmel, J. C., Chang, A. Y., Brack, A. S., & Marshall, W. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005927 · Cited by 54

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples

Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51

RadioPathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy

Tortora, M., Cordelli, E., Sicilia, R., Nibid, L., Ippolito, E., Perrone, G., Ramella, S., & Soda, P. (2023) · IEEE Access · doi:10.1109/access.2023.3275126 · Cited by 49

Urinary metabolic phenotyping for Alzheimer’s disease

Kurbatova, N., Garg, M., Whiley, L., Chekmeneva, E., Jiménez, B., Gómez-Romero, M., Pearce, J., Kimhofer, T., D’Hondt, E., Soininen, H., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78031-9 · Cited by 44

Large iterative multitier ensemble classifiers for security of big data

Abawajy, J. H., Kelarev, A., & Chowdhury, M. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2316510 · Cited by 44

Current State and Prospects of Artificial Intelligence in Allergy

van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Custovic, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 43

Whole-Body Movement during Videogame Play Distinguishes Youth with Autism from Youth with Typical Development

Ardalan, A., Assadi, A. H., Surgent, O. J., & Travers, B. G. (2019) · Scientific Reports · doi:10.1038/s41598-019-56362-6 · Cited by 40

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action

Zhou, H., Cao, H., Matyunina, L., Shelby, M., Cassels, L., McDonald, J. F., & Skolnick, J. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b01248 · Cited by 39

Automatic classification of literature in systematic reviews on food safety using machine learning

van den Bulk, L. M., Bouzembrak, Y., Gavai, A., Liu, N., van den Heuvel, L. J., & Marvin, H. J. P. (2022) · Current Research in Food Science · doi:10.1016/j.crfs.2021.12.010 · Cited by 38

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Gardiner, L.-J., Carrieri, A. P., Bingham, K., Macluskie, G., Bunton, D., McNeil, M., & Pyzer-Knapp, E. O. (2022) · PLOS ONE · doi:10.1371/journal.pone.0263248 · Cited by 36

DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways

Cai, Z., Poulos, R. C., Aref, A., Robinson, P. J., Reddel, R. R., & Zhong, Q. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-24-0285 · Cited by 36

Benchmarking compound activity prediction for real-world drug discovery applications

(2024) · doi:10.1038/s42004-024-01204-4 · Cited by 34

Automated identification of postural control for children with autism spectrum disorder using a machine learning approach

Li, Y., Mache, M. A., & Todd, T. A. (2020) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2020.110073 · Cited by 29

Towards the use of artificial intelligence deep learning networks for detection of archaeological sites

Karamitrou, A., Sturt, F., Bogiatzis, P., & Beresford-Jones, D. (2022) · Surface Topography: Metrology and Properties · doi:10.1088/2051-672x/ac9492 · Cited by 29

Accuracy Comparison of CNN, LSTM, and Transformer for Activity Recognition Using IMU and Visual Markers

Trujillo-Guerrero, M. F., Román-Niemes, S., Jaén-Vargas, M., Cadiz, A., Fonseca, R., & Serrano-Olmedo, J. J. (2023) · IEEE Access · doi:10.1109/access.2023.3318563 · Cited by 28

Extendable and explainable deep learning for pan-cancer radiogenomics research

Liu, Q., & Hu, P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102111 · Cited by 27

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

Sun, K., Roy, A., & Tobin, J. M. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154792 · Cited by 28

Arrhythmia Detection - A Machine Learning based Comparative Analysis with MIT-BIH ECG Data

Singh, V., Tewary, S., Sardana, V., & Sardana, H. K. (2019) · 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct45611.2019.9033665 · Cited by 24

Fusion-based machine learning approach for classification of algae varieties exposed to different light sources in the growth stage

Gerdan Koc, D., Koc, C., & Ekinci, K. (2023) · Algal Research · doi:10.1016/j.algal.2023.103087 · Cited by 23

Predictors of NFT Prices: An Automated Machine Learning Approach

Alon, I., Bretas, V. P. G., & Katrih, V. (2023) · Journal of Global Information Management · doi:10.4018/jgim.317097 · Cited by 20

Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data

Dorraki, M., Liao, Z., Abbott, D., Psaltis, P. J., Baker, E., Bidargaddi, N., Wardill, H. R., van den Hengel, A., Narula, J., & Verjans, J. W. (2024) · JACC: Advances · doi:10.1016/j.jacadv.2024.101180 · Cited by 19

A Literature Review and Research Agenda on Explainable Artificial Intelligence (XAI)

Kalyanathaya, K. P., & K., K. P. (2022) · International Journal of Applied Engineering and Management Letters · doi:10.47992/ijaeml.2581.7000.0119 · Cited by 10

Learning the syntax of plant assemblages

Leblanc, C., Bonnet, P., Servajean, M., Thuiller, W., Chytrý, M., Aćić, S., Argagnon, O., Biurrun, I., Bonari, G., Bruelheide, H., et al. (2025) · Nature Plants · doi:10.1038/s41477-025-02105-7 · Cited by 10

Expression‐based machine learning models for predicting plant tissue identity

Palande, S., Arsenault, J., Basurto‐Lozada, P., Bleich, A., Brown, B. N. I., Buysse, S. F., Connors, N. A., Das Adhikari, S., Dobson, K. C., Guerra‐Castillo, F. X., et al. (2025) · Applications in Plant Sciences · doi:10.1002/aps3.11621 · Cited by 7

Using Chao’s Estimator as a Stopping Criterion for Technology-Assisted Review

Bron, M., van der Heijden, P. G. M., Feelders, A., & Siebes, A. (2025) · ACM Transactions on Information Systems · doi:10.1145/3724116 · Cited by 7

A Consensus Time for Performing Quality Control of 225Ac-Labeled Radiopharmaceuticals

Kelly, J. M., Amor-Coarasa, A., Sweeney, E., Wilson, J. J., Causey, P. W., & Babich, J. (2021) · doi:10.21203/rs.3.rs-39342/v2 · Cited by 11

Molecular insights from conformational ensembles via machine learning

(2019) · doi:10.1101/695254 · Cited by 6

WIMOAD: Weighted Integration of Multi-Omics Data with Meta Learning for Alzheimer’s Disease Diagnosis

Xiao, H., Wang, J., & Wan, S. (2024) · doi:10.1101/2024.09.25.614862 · Cited by 6

Deep Learning-Driven User Legitimacy Prediction Using Keystroke and Mouse Behavioural Dynamics

AbouRida, O., Nashaat, M., & Gamal El-din Saad, N. (2024) · 2024 International Conference on Computer and Applications (ICCA) · doi:10.1109/icca62237.2024.10928042 · Cited by 4

From memristors to compositional machine learning: exploring neuromemristive algorithmic abstractions with the knowm API

Nugent, A., & Molter, T. (2017) · Proceedings of the Neuromorphic Computing Symposium · doi:10.1145/3183584.3183615

Learning the syntax of plant assemblages

(2025) · doi:10.21203/rs.3.rs-6304381/v1