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An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

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Ensemble neural network model for detecting thyroid eye disease using external photographs

Karlin, J. N., Gai, L., LaPierre, N., Danesh, K., Farajzadeh, J., Palileo, B. M., Taraszka, K., Zheng, J., Wang, W., Eskin, E., et al. (2022) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-321833 · Cited by 45 · see citing works

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Darbandsari, A., Farahani, H., Asadi, M., Wiens, M. O., Cochrane, D. R., Mirabadi, A. K., Jamieson, A., Farnell, D., Ahmadvand, P., Douglas, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49017-2 · Cited by 45 · see citing works

Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons

Struye, J., & Latré, S. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.09.098 · Cited by 45 · see citing works

An Improved Random Forest Model for the Prediction of Dam Displacement

Su, Y. P., Weng, K., Lin, C., & Zheng, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3049578 · Cited by 48 · see citing works

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Chaudhuri, K. D., & Alkan, B. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03251-7 · Cited by 45 · see citing works

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

Melchiorre, C., Abella, E. A. C., van Westen, C. J., & Matteucci, M. (2010) · Computers & Geosciences · doi:10.1016/j.cageo.2010.10.004 · Cited by 44 · see citing works

Application of Ridge Polynomial Neural Networks to Financial Time Series Prediction

Ghazali, R., Hussain, A. J., & El‐Deredy, W. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716194 · Cited by 44 · see citing works

Diagnosing acute promyelocytic leukemia by using convolutional neural network

Ouyang, N., Wang, W., Ma, L., Wang, Y., Chen, Q., Yang, S., Xie, J., Su, S., Cheng, Y., Cheng, Q., et al. (2020) · Clinica Chimica Acta · doi:10.1016/j.cca.2020.10.039 · Cited by 44 · see citing works

Remarks on the Use of Multilayer Perceptrons for the Analysis of Chemical Sensor Array Data

Pardo, M., & Sberveglieri, G. (2004) · IEEE Sensors Journal · doi:10.1109/jsen.2004.827207 · Cited by 44 · see citing works

A New Early Stopping Algorithm for Improving Neural Network Generalization

Wu, X., & Liu, J. (2009) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2009.11 · Cited by 43 · see citing works

A neural network approach to understanding implied volatility movements

Cao, J., Chen, J. H. K., & Hull, J. C. (2020) · Quantitative Finance · doi:10.1080/14697688.2020.1750679 · Cited by 43 · see citing works

Detectability of Bridge-Structural Damage Based on Fiber-Optic Sensing through Deep-Convolutional Neural Networks

Li, S., & Sun, L. (2020) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001531 · Cited by 43 · see citing works

Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME

Hung, S.-C., Wu, H.-C., & Tseng, M. (2020) · Applied Sciences · doi:10.3390/app10186151 · Cited by 43 · see citing works

Lessons in neural network training: overfitting may be harder than expected

Lawrence, S., Giles, C. L., & Tsoi, A. C. (1997) · Cited by 234 · see citing works

Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping

Caruana, R., Lawrence, S., & Giles, C. L. (2000) · Cited by 906 · see citing works

A study of the generalization capability versus training in backpropagation neural networks

Dalianis, P. J., Tzafestaş, S. G., & Anthopoulos, G. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1993.390760 · Cited by 10 · see citing works

An analysis of underfitting in MLP networks

Narayan, S., & Tagliarini, G. A. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555986 · Cited by 20 · see citing works

Comparison of Backpropagation and Conjugate Gradient Algorithms for Efficient Training of Multilayer Perceptron Networks

Miao, G. (2002) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)