The elements of statistical learning: Data mining, inference, and prediction

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works

Feature Selection: A Data Perspective

Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442 · see citing works

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works

Machine learning with big data: challenges and approaches.

L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023 · see citing works

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2020) · Geoscience Frontiers · doi:10.1016/j.gsf.2020.03.007 · Cited by 913 · see citing works

Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification

Yadav, S., & Shukla, S. (2016) · 2016 IEEE 6th International Conference on Advanced Computing (IACC) · doi:10.1109/iacc.2016.25 · Cited by 773 · see citing works

Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms

Xue, B., Zhang, M., & Browne, W. N. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2013.09.018 · Cited by 614 · see citing works

Efficient Human Pose Estimation from Single Depth Images

Shotton, J., Girshick, R., Fitzgibbon, A. W., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.241 · Cited by 579 · see citing works

The auxin signalling network translates dynamic input into robust patterning at the shoot apex

Vernoux, T., Brunoud, G., Farcot, E., Morin, V., Van den Daele, H., Legrand, J., Oliva, M., Das, P., Larrieu, A., Wells, D., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.39 · Cited by 576 · see citing works

Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees

Leathwick, J., Elith, J., Francis, M., Hastie, T. J., & Taylor, P. (2006) · Marine Ecology Progress Series · doi:10.3354/meps321267 · Cited by 570 · see citing works

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Wu, W., Li, Y.-J., Feng, A., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021) · Military Medical Research · doi:10.1186/s40779-021-00338-z · Cited by 529 · see citing works

Ensemble Methods in Data Mining

Seni, G., & Elder, J. F. (2010) · Synthesis lectures on data mining and knowledge discovery · doi:10.2200/s00240ed1v01y200912dmk002 · Cited by 646 · see citing works

Screening large-scale association study data: exploiting interactions using random forests

Lunetta, K. L., Hayward, B., Segal, J., & Eerdewegh, P. V. (2004) · BMC Genetics · doi:10.1186/1471-2156-5-32 · Cited by 464 · see citing works

ICE: a statistical approach to identifying endmembers in hyperspectral images

Berman, M., Kiiveri, H., Lagerstrom, R., Ernst, A. T., Dunne, R. A., & Huntington, J. F. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.835299 · Cited by 397 · see citing works

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Chen, K., Huang, C., & He, J. (2016) · High Voltage · doi:10.1049/hve.2016.0005 · Cited by 378 · see citing works

Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling

Guo, S., Popp, J., & Bocklitz, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00620-3 · Cited by 361 · see citing works

The persistence of distance? The impact of technology on MNE motivations for foreign investment

Nachum, L., & Zaheer, S. (2005) · Strategic Management Journal · doi:10.1002/smj.472 · Cited by 359 · see citing works

Efficient regression of general-activity human poses from depth images

Girshick, R., Shotton, J., Kohli, P., Criminisi, A., & Fitzgibbon, A. W. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126270 · Cited by 347 · see citing works

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

Wager, T. D., Atlas, L. Y., Leotti, L. A., & Rilling, J. K. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3420-10.2011 · Cited by 328 · see citing works

Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time

Zhu, Z., Woodcock, C. E., Holden, C. E., & Yang, Z. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.02.009 · Cited by 312 · see citing works

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening

Basith, S., Manavalan, B., Shin, T. H., & Lee, G. (2020) · Medicinal Research Reviews · doi:10.1002/med.21658 · Cited by 311 · see citing works

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer, C., & Gerlich, D. W. (2013) · Journal of Cell Science · doi:10.1242/jcs.123604 · Cited by 298 · see citing works

A method for calling gains and losses in array CGH data

Wang, P., Kim, Y., Pollack, J. R., Narasimhan, B., & Tibshirani, R. J. (2004) · Biostatistics · doi:10.1093/biostatistics/kxh017 · Cited by 307 · see citing works

Multiobjective Optimization in Bioinformatics and Computational Biology

Handl, J., Kell, D. B., & Knowles, J. (2007) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.070203 · Cited by 308 · see citing works

Data based identification and prediction of nonlinear and complex dynamical systems

Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 317 · see citing works

Examining the relationship between built environment and metro ridership at station-to-station level

Gan, Z., Yang, M., Feng, T., & Timmermans, H. J. P. (2020) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2020.102332 · Cited by 281 · see citing works

Comparison of variable selection methods for clinical predictive modeling

Sanchez-Pinto, L. N., Venable, L. R., Fahrenbach, J. P., & Churpek, M. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.05.006 · Cited by 281 · see citing works

Robust Wasserstein profile inference and applications to machine learning

Blanchet, J., Kang, Y., & Murthy, K. (2019) · Journal of Applied Probability · doi:10.1017/jpr.2019.49 · Cited by 254 · see citing works

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G. K., & Ryan, P. (2016) · Atmospheric Environment · doi:10.1016/j.atmosenv.2016.11.066 · Cited by 264 · see citing works

An ensemble learning framework for anomaly detection in building energy consumption

Araya, D. B., Grolinger, K., ElYamany, H. F., Capretz, M. A. M., & Bitsuamlak, G. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.02.058 · Cited by 256 · see citing works

Earthquake magnitude prediction in Hindukush region using machine learning techniques

Asim, K. M., Alvarez, F. M., Basit, A., & Iqbal, T. (2016) · Natural Hazards · doi:10.1007/s11069-016-2579-3 · Cited by 257 · see citing works

Parkinson’s Disease Diagnosis Using Machine Learning and Voice

Wroge, T. J., Özkanca, Y., Demiroğlu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2018.8615607 · Cited by 260 · see citing works

A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection

Arora, S., Singh, H., Sharma, M., Sharma, S., & Anand, P. (2019) · IEEE Access · doi:10.1109/access.2019.2897325 · Cited by 241 · see citing works

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Krafft, C., Schmitt, M. P., Schie, I. W., Cialla‐May, D., Matthäus, C., Bocklitz, T. W., & Popp, J. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201607604 · Cited by 232 · see citing works

Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach

Moro, S., Rita, P., & Vala, B. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.02.010 · Cited by 226 · see citing works

Machine Learning in Nanoscience: Big Data at Small Scales

Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D., & Celano, U. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04090 · Cited by 229 · see citing works

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

Chen, W., Jin, R., & Sudjianto, A. (2004) · Journal of Mechanical Design · doi:10.1115/1.1904642 · Cited by 230 · see citing works

Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

Zhu, X., Suk, H., Lee, S., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2466616 · Cited by 218 · see citing works

Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures

Desu, R. K., Hansoge, N. K., Balu, A., Gupta, A. K., & Singh, S. K. (2015) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2015.04.001 · Cited by 218 · see citing works

Predicting Hurricane Power Outages to Support Storm Response Planning

Guikema, S. D., Nateghi, R., Quiring, S. M., Staid, A., Reilly, A., & Gao, M. (2014) · IEEE Access · doi:10.1109/access.2014.2365716 · Cited by 216 · see citing works

When a gold standard isn’t so golden: Lack of prediction of subjective sleep quality from sleep polysomnography

Kaplan, K. A., Hirshman, J., Hernandez, B., Stefanick, M. L., Hoffman, A. R., Redline, S. S., Ancoli‐Israel, S., Stone, K. L., Friedman, L., & Zeitzer, J. M. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.11.010 · Cited by 208 · see citing works

Classifiers consensus system approach for credit scoring

Alaraj, M., & Abbod, M. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.04.013 · Cited by 202 · see citing works

Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models

Zhang, J., Li, D., & Wang, Y. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120665 · Cited by 204 · see citing works

Optimizing ensemble weights and hyperparameters of machine learning models for regression problems

Shahhosseini, M., Hu, G., & Pham, H. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100251 · Cited by 222 · see citing works

First-person hyper-lapse videos

Kopf, J., Cohen, M. F., & Szeliski, R. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601195 · Cited by 198 · see citing works

Measuring the Development of Social Attention Using Free‐Viewing

Frank, M. C., Vul, E., & Saxe, R. R. (2011) · Infancy · doi:10.1111/j.1532-7078.2011.00086.x · Cited by 192 · see citing works

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Bikmukhametov, T., & Jäschke, J. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106834 · Cited by 198 · see citing works

Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Al-Tashi, Q., Abdulkadir, S. J., Rais, H. M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3007291 · Cited by 194 · see citing works

Traditional Governance, Citizen Engagement, and Local Public Goods: Evidence from Mexico

Díaz-Cayeros, A., Magaloni, B., & Ruiz-Euler, A. (2013) · World Development · doi:10.1016/j.worlddev.2013.01.008 · Cited by 185 · see citing works

Sentiment Analysis of Health Care Tweets: Review of the Methods Used

Gohil, S., Vuik, S., & Darzi, A. W. (2018) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.5789 · Cited by 185 · see citing works

Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication

Xia, L., Gu, P., Li, B., Tang, T., Yin, X., Huangfu, W., Yu, S., Cao, Y. K., Wang, Y., & Yang, H. (2016) · Journal of Computer Science and Technology · doi:10.1007/s11390-016-1608-8 · Cited by 184 · see citing works

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Shabani, S., Samadianfard, S., Sattari, M. T., et al. (2020) · Atmosphere · doi:10.3390/atmos11010066 · Cited by 183 · see citing works

Clinical concept extraction: A methodology review

Fu, S., Chen, D. J., He, H., Liu, S., Moon, S., Peterson, K. J., Shen, F., Wang, L., Wang, Y., Wen, A., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103526 · Cited by 182 · see citing works

Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation

Wang, M., Zhang, D., Huang, J., Yap, P., Shen, D. G., & Liu, M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2933160 · Cited by 179 · see citing works

A Least-Squares Framework for Component Analysis

la Torre, F. D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.184 · Cited by 176 · see citing works

Artificial intelligence and machine learning

Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022) · Electronic Markets · doi:10.1007/s12525-022-00598-0 · Cited by 210 · see citing works

Survey of Machine Learning based intrusion detection methods for Internet of Medical Things

Si-Ahmed, A., Al-Garadi, M. A., & Boustia, N. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110227 · Cited by 188 · see citing works

Modeling human performance in statistical word segmentation.

Frank, M. C., Goldwater, S., Mansinghka, V. K., Griffiths, T., & Tenenbaum, J. (2010) · Cognition · doi:10.1016/j.cognition.2010.07.005 · Cited by 183 · see citing works

Coupled Spectral Regression for matching heterogeneous faces

Lei, Z., & Li, S. Z. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206860 · Cited by 174 · see citing works

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174 · see citing works

Risk Prediction Models for Post‐Operative Mortality in Patients With Cirrhosis

Mahmud, N., Fricker, Z. P., Hubbard, R. A., Ioannou, G. N., Lewis, J. D., Taddei, T. H., Rothstein, K. D., Serper, M., Goldberg, D. S., & Kaplan, D. E. (2020) · Hepatology · doi:10.1002/hep.31558 · Cited by 177 · see citing works

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173 · see citing works

SVM and PCA based fault classification approaches for complicated industrial process

Jing, C., & Hou, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.082 · Cited by 166 · see citing works

Multitask Linear Discriminant Analysis for View Invariant Action Recognition

Yan, Y., Ricci, E., Subramanian, R., Liu, G., & Sebe, N. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2365699 · Cited by 165 · see citing works

Machine learning and computer vision approaches for phenotypic profiling

Grys, B. T., Lo, D. S., Sahin, N., Kraus, O. Z., Morris, Q., Boone, C. M., & Andrews, B. (2016) · The Journal of Cell Biology · doi:10.1083/jcb.201610026 · Cited by 163 · see citing works

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

Lorencin, I., Anđelić, N., Španjol, J., & Car, Z. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101746 · Cited by 164 · see citing works

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

Pilania, G., Iverson, C. N., Lookman, T., & Marrone, B. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00807 · Cited by 165 · see citing works

Sex-driven modifiers of Alzheimer risk

Rahman, A., Schelbaum, E., Hoffman, K., Diaz, I., Hristov, H., Andrews, R. D., Jett, S., Jackson, H. C., Lee, A., Sarva, H., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000009781 · Cited by 165 · see citing works

Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS

Fan, S., Zhang, J., Blanco‐Davis, E., et al. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107544 · Cited by 162 · see citing works

Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making

Hamon, R., Junklewitz, H., Sanchez, I., Malgieri, G., & Hert, P. D. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2021.3129960 · Cited by 165 · see citing works

Avoiding Pitfalls in Neural Network Research

Zhang, G. (2006) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2006.876059 · Cited by 157 · see citing works

View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data

Liu, M., Zhang, J., Yap, P., & Shen, D. G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.002 · Cited by 159 · see citing works

One class random forests

Désir, C., Bernard, S., Petitjean, C., & Heutte, L. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.022 · Cited by 153 · see citing works

Random Forests with ensemble of feature spaces

Zhang, L., & Suganthan, P. N. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.04.001 · Cited by 156 · see citing works

Universal machine learning for topology optimization

Chi, H., Zhang, Y., Tang, T. L. E., Mirabella, L., Dalloro, L., Song, L., & Paulino, G. H. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.112739 · Cited by 161 · see citing works

Modelling heating and cooling energy demand for building stock using a hybrid approach

Li, X., & Yao, R. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110740 · Cited by 154 · see citing works

Dynamic Pricing and Information Disclosure for Fresh Produce: An Artificial Intelligence Approach

Yang, C., Feng, Y., & Whinston, A. B. (2021) · Production and Operations Management · doi:10.1111/poms.13525 · Cited by 153 · see citing works

On Distributed Fuzzy Decision Trees for Big Data

Segatori, A., Marcelloni, F., & Pedrycz, W. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2646746 · Cited by 145 · see citing works

Inferring low-dimensional microstructure representations using convolutional neural networks

Lubbers, N., Lookman, T., & Barros, K. M. (2017) · Physical review. E · doi:10.1103/physreve.96.052111 · Cited by 143 · see citing works

Symbiosis of chemometrics and metabolomics: past, present, and future

van der Greef, J., & Smilde, A. K. (2005) · Journal of Chemometrics · doi:10.1002/cem.941 · Cited by 137 · see citing works

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

Engemann, D. A., Kozynets, O., Sabbagh, D., Lemaitre, G., Varoquaux, G. P., Liem, F., & Gramfort, A. (2020) · eLife · doi:10.7554/elife.54055 · Cited by 132 · see citing works

Stock Return Serial Dependence and Out-of-Sample Portfolio Performance

DeMiguel, V., Nogales, F. J., & Uppal, R. (2014) · Review of Financial Studies · doi:10.1093/rfs/hhu002 · Cited by 148 · see citing works

Boosted sooty tern optimization algorithm for global optimization and feature selection

Houssein, E. H., Oliva, D. A., Çelik, E., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119015 · Cited by 136 · see citing works

A Novel Template Reduction Approach for the $K$-Nearest Neighbor Method

Fayed, H. A., & Atiya, A. F. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2018547 · Cited by 132 · see citing works

AI-based prediction of independent construction safety outcomes from universal attributes

Baker, H., Hallowell, M. R., & Tixier, A. J. ‐. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103146 · Cited by 129 · see citing works

Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico

Rodríguez‐Veiga, P., Saatchi, S. S., Tansey, K. J., & Balzter, H. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.06.004 · Cited by 132 · see citing works

Learning in Big Data: Introduction to Machine Learning

Bouchefry, K. E., & de Souza, R. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819154-5.00023-0 · Cited by 128 · see citing works

Inflammation is increased with anxiety- and depression-like signs in a rat model of spinal cord injury

Maldonado‐Bouchard, S., Peters, K., Woller, S. A., Madahian, B., Faghihi, U., Patel, S. P., Bake, S., & Hook, M. A. (2015) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2015.08.009 · Cited by 127 · see citing works

Data science applications for predictive maintenance and materials science in context to Industry 4.0

Sajid, S., Haleem, A., Bahl, S., Javaid, M., Goyal, T., & Mittal, M. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.01.357 · Cited by 127 · see citing works

Learning to Detect Patterns of Crime

Wang, T., Rudin, C. D., Wägner, D., & Sevieri, R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_33 · Cited by 126 · see citing works

Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework

Hu, Q., Paisitkriangkrai, S., Shen, C., van den Hengel, A., & Porikli, F. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2496795 · Cited by 123 · see citing works

Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design

Bartoli, N., Lefebvre, T., Dubreuil, S., Olivanti, R., Priem, R., Bons, N. P., Martins, J. R. R. A., & Morlier, J. (2019) · Aerospace Science and Technology · doi:10.1016/j.ast.2019.03.041 · Cited by 120 · see citing works

Tensor methods and recommender systems

Frolov, E., & Oseledets, I. V. (2017) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1201 · Cited by 122 · see citing works

Forecasting crude oil prices: A scaled PCA approach

He, M., Zhang, Y., Wen, D., & Wang, Y. (2021) · Energy Economics · doi:10.1016/j.eneco.2021.105189 · Cited by 116 · see citing works

Ensemble of feature selection algorithms: a multi-criteria decision-making approach

Hashemi, A., Dowlatshahi, M. B., & Nezamabadi–pour, H. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01347-z · Cited by 122 · see citing works

Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art

Ling, X., Wu, L., Zhang, J., Qu, Z., Deng, W., Chen, X., Qian, Y., Wu, C., Ji, S., Luo, T., et al. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103134 · Cited by 133 · see citing works

A Bayesian network-based model for risk modeling and scenario deduction of collision accidents of inland intelligent ships

Zhang, J., Jin, M., Wan, C., Dong, Z. S., & Wu, X. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109816 · Cited by 119 · see citing works

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Gupta, S., Tran, T., Luo, W., Phung, D., Kennedy, R. L., Broad, A., Campbell, D. C., Kipp, D., Singh, M. S., Khasraw, M., et al. (2014) · BMJ Open · doi:10.1136/bmjopen-2013-004007 · Cited by 118 · see citing works

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat

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Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework

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