BiLSTM deep neural network model for imbalanced medical data of IoT systems
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Siami-Namini, 2019, The performance of lstm and bilstm in forecasting time series, 3285
Jeong, 2020, Brain-controlled robotic arm system based on multi-directional cnn-bilstm network using eeg signals, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 1226, 10.1109/TNSRE.2020.2981659
Aslan, 2021, Cnn-based transfer learning–bilstm network: A novel approach for covid-19 infection detection, Appl. Soft Comput., 98, 10.1016/j.asoc.2020.106912
Li, 2019, Interpretability analysis of heartbeat classification based on heartbeat activity’s global sequence features and bilstm-attention neural network, IEEE Access, 7, 109870, 10.1109/ACCESS.2019.2933473
Peng, 2021, An integrated framework of bi-directional long-short term memory (bilstm) based on sine cosine algorithm for hourly solar radiation forecasting, Energy, 221, 10.1016/j.energy.2021.119887
Lu, 2020, A cnn-bilstm-am method for stock price prediction, Neural Comput. Appl., 1
Liu, 2020, Document-level multi-topic sentiment classification of email data with bilstm and data augmentation, Knowl.-Based Syst., 197, 10.1016/j.knosys.2020.105918
Zhao, 2020, A double-channel hybrid deep neural network based on cnn and bilstm for remaining useful life prediction, Sensors, 20, 7109, 10.3390/s20247109
Jagvaral, 2020, Path-based reasoning approach for knowledge graph completion using cnn-bilstm with attention mechanism, Expert Syst. Appl., 142, 10.1016/j.eswa.2019.112960
Raj, 2021, An eemd-bilstm algorithm integrated with boruta random forest optimiser for significant wave height forecasting along coastal areas of queensland, australia, Remote Sens., 13, 1456, 10.3390/rs13081456
Liu, 2021, Automatic modulation recognition based on a dcn-bilstm network, Sensors, 21, 1577, 10.3390/s21051577
He, 2008, Adasyn: Adaptive synthetic sampling approach for imbalanced learning, 1322
Satapathy, 2021, Adasyn and abc-optimized rbf convergence network for classification of electroencephalograph signal, Pers. Ubiquitous Comput., 1
e Silva, 2020, An optimised ensemble for antibody-mediated rejection status prediction in kidney transplant patients, 1
Özdemir, 2021, Classification of imbalanced hyperspectral images using smote-based deep learning methods, Expert Syst. Appl., 10.1016/j.eswa.2021.114986
Jonathan, 2020, Observation imbalanced data text to predict users selling products on female daily with smote, tomek, and smote-tomek, 81
Enrique, 2020, Design issues in time series dataset balancing algorithms, Neural Comput. Appl., 32, 1287, 10.1007/s00521-019-04011-4
FitzGerald, 2017, Implicit bias in healthcare professionals: a systematic review, BMC Med. Ethics, 18, 1, 10.1186/s12910-017-0179-8
Cunningham, 2002, Attitudes about sexual disclosure and perceptions of stigma and shame, Sex. Transm. Infect., 78, 334, 10.1136/sti.78.5.334
Fernandes, 2017, Transfer learning with partial observability applied to cervical cancer screening, 243
Abdoh, 2018, Cervical cancer diagnosis using random forest classifier with smote and feature reduction techniques, IEEE Access, 6, 59475, 10.1109/ACCESS.2018.2874063
Nithya, 2019, Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction, SN Appl. Sci., 1, 1, 10.1007/s42452-019-0645-7
Razali, 2020, Risk factors of cervical cancer using classification in data mining, J. Phys.: Conf. Ser., 1529
Wu, 2017, Data-driven diagnosis of cervical cancer with support vector machine-based approaches, IEEE Access, 5, 25189, 10.1109/ACCESS.2017.2763984
Ceylan, 2017, Comparison of multi-label classification methods for prediagnosis of cervical cancer, Graph. Models, 21, 22
Taha, 2017, Classification of cervical-cancer using pap-smear images: a convolutional neural network approach, 261
Khamparia, 2020, Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning, J. Supercomput., 1
M. Wu, C. Yan, H. Liu, Q. Liu, Y. Yin, Automatic classification of cervical cancer from cytological images by using convolutional neural network, Biosci. Rep. 38 (6).