CNN Ensemble learning method for Transfer learning: A Review

Yudha Islami Sulistya(1); Elsi Titasari Br Bangun(2); Dyah Aruming Tyas(3*);

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author



This  study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152.


Ensemble Learning; Transfer Learning; Deep Learning; Pre-Trained Model; CNN


Full Text:


Article Metrics

Abstract view: 99 times
PDF view: 25 times

Digital Object Identifier




N. Subramanian, O. Elharrouss, S. Al-Maadeed, and M. Chowdhury, “A review of deep learning-based detection methods for COVID-19,” Computers in Biology and Medicine, vol. 143. Elsevier Ltd, Apr. 01, 2022. doi: 10.1016/j.compbiomed.2022.105233.

Y. Ding, L. Hua, and S. Li, “Research on computer vision enhancement in intelligent robot based on machine learning and deep learning,” Neural Comput Appl, vol. 34, no. 4, pp. 2623–2635, Feb. 2022, doi: 10.1007/s00521-021-05898-8.

M. N. Y. Ali, M. L. Rahman, J. Chaki, N. Dey, and K. C. Santosh, “Machine translation using deep learning for universal networking language based on their structure,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 8, pp. 2365–2376, Aug. 2021, doi: 10.1007/s13042-021-01317-5.

K. Sudars, “Face recognition Face2vec based on deep learning: Small database case,” Automatic Control and Computer Sciences, vol. 51, no. 1, pp. 50–54, Jan. 2017, doi: 10.3103/S0146411617010072.

S. Li, Z. Q. Liu, and A. B. Chan, “Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network,” Int J Comput Vis, vol. 113, no. 1, pp. 19–36, May 2015, doi: 10.1007/s11263-014-0767-8.

M. N. Asim, M. U. Ghani, M. A. Ibrahim, W. Mahmood, A. Dengel, and S. Ahmed, “Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification,” Neural Comput Appl, vol. 33, no. 11, pp. 5437–5469, Jun. 2021, doi: 10.1007/s00521-020-05321-8.

X. Bi, X. Zhao, H. Huang, D. Chen, and Y. Ma, “Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine,” Cognit Comput, vol. 12, no. 3, pp. 513–527, May 2020, doi: 10.1007/s12559-019-09688-2.

G. Chugh, S. Kumar, and N. Singh, “Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis,” Cognit Comput, vol. 13, no. 6, pp. 1451–1470, Nov. 2021, doi: 10.1007/s12559-020-09813-6.

S. Goyal and R. Singh, “Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques,” J Ambient Intell Humaniz Comput, 2021, doi: 10.1007/s12652-021-03464-7.

Z. Zhu, D. Li, Y. Hu, J. Li, D. Liu, and J. Li, “Indoor scene segmentation algorithm based on full convolutional neural network,” Neural Comput Appl, vol. 33, no. 14, pp. 8261–8273, Jul. 2021, doi: 10.1007/s00521-020-04961-0.

K. Kuppusamy and C. Eswaran, “Convolutional and Deep Neural Networks based techniques for extracting the age-relevant features of the speaker,” J Ambient Intell Humaniz Comput, 2021, doi: 10.1007/s12652-021-03238-1.

A. ul Haq et al., “MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system,” J Ambient Intell Humaniz Comput, 2022, doi: 10.1007/s12652-022-04373-z.

A. Mahmood, S. K. Singh, and A. K. Tiwari, “Pre-trained deep learning-based classification of jujube fruits according to their maturity level,” Neural Comput Appl, vol. 34, no. 16, pp. 13925–13935, Aug. 2022, doi: 10.1007/s00521-022-07213-5.

R. Pramanik and S. Bag, “Handwritten Bangla city name word recognition using CNN-based transfer learning and FCN,” Neural Comput Appl, vol. 33, no. 15, pp. 9329–9341, Aug. 2021, doi: 10.1007/s00521-021-05693-5.

S. Zebhi, S. M. T. AlModarresi, and V. Abootalebi, “Human activity recognition using pre-trained network with informative templates,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 12, pp. 3449–3461, Dec. 2021, doi: 10.1007/s13042-021-01383-9.

H. S. Nogay, T. C. Akinci, and M. Yilmaz, “Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network,” Neural Comput Appl, vol. 34, no. 2, pp. 1423–1432, Jan. 2022, doi: 10.1007/s00521-021-06652-w.

S. Hira, A. Bai, and S. Hira, “An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images,” Applied Intelligence , pp. 2864–2889, 2020, doi: 10.1007/s10489-020-02010-w/Published.

K. Usha Kingsly Devi and V. Gomathi, “Deep Convolutional Neural Networks with Transfer Learning for Visual Sentiment Analysis,” Neural Process Lett, Nov. 2022, doi: 10.1007/s11063-022-11082-3.

T. Aitazaz, A. Tubaishat, F. Al-Obeidat, B. Shah, T. Zia, and A. Tariq, “Transfer learning for histopathology images: an empirical study,” Neural Comput Appl, 2022, doi: 10.1007/s00521-022-07516-7.

P. Kumar and A. S. Hati, “Transfer learning-based deep CNN model for multiple faults detection in SCIM,” Neural Comput Appl, 2021, doi: 10.1007/s00521-021-06205-1.

M. Dua, Shakshi, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection,” Neural Comput Appl, vol. 33, no. 8, pp. 3155–3168, Apr. 2021, doi: 10.1007/s00521-020-05209-7.

J. Chen, A. Zeb, Y. A. Nanehkaran, and D. Zhang, “Stacking ensemble model of deep learning for plant disease recognition,” J Ambient Intell Humaniz Comput, 2022, doi: 10.1007/s12652-022-04334-6.

J. Arun Prakash, C. Asswin, V. Ravi, V. Sowmya, and K. Soman, “Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures,” Multimed Tools Appl, 2022, doi: 10.1007/s11042-022-13844-6.

S. Thirumaladevi, K. Veera Swamy, and M. Sailaja, “Improved transfer learning of CNN through fine-tuning and classifier ensemble for scene classification,” Soft comput, vol. 26, no. 12, pp. 5617–5636, Jun. 2022, doi: 10.1007/s00500-022-07145-1.

A. Ghorbanali, M. K. Sohrabi, and F. Yaghmaee, “Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks,” Inf Process Manag, vol. 59, no. 3, May 2022, doi: 10.1016/j.ipm.2022.102929.

R. Kundu, P. K. Singh, M. Ferrara, A. Ahmadian, and R. Sarkar, “ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images,” Multimed Tools Appl, vol. 81, no. 1, pp. 31–50, Jan. 2022, doi: 10.1007/s11042-021-11319-8.

S. Majumdar, P. Pramanik, and R. Sarkar, “Gamma function based ensemble of CNN models for breast cancer detection in histopathology images,” Expert Syst Appl, vol. 213, Mar. 2023, doi: 10.1016/j.eswa.2022.119022.

M. Ayaz, F. Shaukat, and G. Raja, “Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors,” Phys Eng Sci Med, vol. 44, no. 1, pp. 183–194, Mar. 2021, doi: 10.1007/s13246-020-00966-0.

A. A. Hekal, H. E. D. Moustafa, and A. Elnakib, “Ensemble deep learning system for early breast cancer detection,” Evol Intell, 2022, doi: 10.1007/s12065-022-00719-w.

E. Ayan, B. Karabulut, and H. M. Ünver, “Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images,” Arab J Sci Eng, vol. 47, no. 2, pp. 2123–2139, Feb. 2022, doi: 10.1007/s13369-021-06127-z.

M. Islam, M. T. Reza, M. Kaosar, and M. Z. Parvez, “Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images,” Neural Process Lett, 2022, doi: 10.1007/s11063-022-11014-1.

Q. Lv, Y. Quan, W. Feng, M. Sha, S. Dong, and M. Xing, “Radar Deception Jamming Recognition Based onWeighted Ensemble CNN With Transfer Learning,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, doi: 10.1109/TGRS.2021.3129645.

W. Xie, S. Wei, Z. Zheng, Y. Jiang, and D. Yang, “Recognition of Defective Carrots Based on Deep Learning and Transfer Learning,” Food Bioproc Tech, vol. 14, pp. 1361–1374, 2021, doi: 10.1007/s11947-021-02653-8/Published.

S. M. Oh, J. Park, J. Yang, Y. G. Oh, and K. W. Yi, “Smart classification method to detect irregular nozzle spray patterns inside carbon black reactor using ensemble transfer learning,” J Intell Manuf, 2022, doi: 10.1007/s10845-022-01951-y.

K. Zhao, H. Jiang, X. Li, and R. Wang, “Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 5, pp. 1483–1499, May 2021, doi: 10.1007/s13042-020-01249-6.

R. S. Alkhawaldeh, M. Alawida, N. F. F. Alshdaifat, W. Z. Alma’aitah, and A. Almasri, “Ensemble deep transfer learning model for Arabic (Indian) handwritten digit recognition,” Neural Comput Appl, vol. 34, no. 1, pp. 705–719, Jan. 2022, doi: 10.1007/s00521-021-06423-7.

P. Aggarwal, N. K. Mishra, B. Fatimah, P. Singh, A. Gupta, and S. D. Joshi, “COVID-19 image classification using deep learning: Advances, challenges and opportunities,” Comput Biol Med, vol. 144, May 2022, doi: 10.1016/j.compbiomed.2022.105350.

S. Mittal, “Ensemble of transfer learnt classifiers for recognition of cardiovascular tissues from histological images,” Phys Eng Sci Med, vol. 44, no. 3, pp. 655–665, Sep. 2021, doi: 10.1007/s13246-021-01013-2.

S. Kumar, S. Mishra, and S. K. Singh, “Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays,” J Health Manag, vol. 23, no. 4, pp. 730–746, Dec. 2021, doi: 10.1177/09720634211050425.

A. N. Jahromi, H. Karimipour, and A. Dehghantanha, “An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things,” Comput Commun, Nov. 2022, doi: 10.1016/j.comcom.2022.11.009.

P. A. H. Vardhini, S. Asritha, and Y. S. Devi, “Efficient Disease Detection of Paddy Crop using CNN,” in Proceedings of the International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2020, Oct. 2020, pp. 116–119. doi: 10.1109/ICSTCEE49637.2020.9276775.

S. M. Mohammadi, S. Enshaeifar, A. Hilton, D. J. Dijk, and K. Wells, “Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 290–299, 2021, doi: 10.1109/TNSRE.2020.3048121.

L. Visuña, D. Yang, J. Garcia-Blas, and J. Carretero, “Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning,” BMC Med Imaging, vol. 22, no. 1, Dec. 2022, doi: 10.1186/s12880-022-00904-4.

S. A. B. P and C. S. R. Annavarapu, “Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification,” Applied Intelligence, vol. 51, no. 5, pp. 3104–3120, May 2021, doi: 10.1007/s10489-021-02199-4.

M. Shorfuzzaman, “An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection,” in Multimedia Systems, Aug. 2022, vol. 28, no. 4, pp. 1309–1323. doi: 10.1007/s00530-021-00787-5.

S. Alijani, J. Tanha, and L. Mohammadkhanli, “An ensemble of deep learning algorithms for popularity prediction of flickr images,” Multimed Tools Appl, vol. 81, no. 3, pp. 3253–3274, Jan. 2022, doi: 10.1007/s11042-021-11517-4.

S. Vallabhajosyula, V. Sistla, and V. K. K. Kolli, “Transfer learning-based deep ensemble neural network for plant leaf disease detection,” Journal of Plant Diseases and Protection, vol. 129, no. 3, pp. 545–558, Jun. 2022, doi: 10.1007/s41348-021-00465-8.

N. Gianchandani, A. Jaiswal, D. Singh, V. Kumar, and M. Kaur, “Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images,” J Ambient Intell Humaniz Comput, 2020, doi: 10.1007/s12652-020-02669-6.

A. Das, “Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis,” Multimed Tools Appl, vol. 81, no. 4, pp. 5407–5441, Feb. 2022, doi: 10.1007/s11042-021-11787-y.

M. Bhuiyan and M. S. Islam, “A new ensemble learning approach to detect malaria from microscopic red blood cell images,” Sensors International, p. 100209, Nov. 2022, doi: 10.1016/j.sintl.2022.100209.

A. S. Qureshi and T. Roos, “Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets,” Neural Process Lett, 2022, doi: 10.1007/s11063-022-11049-4.

H. Guo, Y. Liu, D. Yang, and J. Zhao, “Offline handwritten Tai Le character recognition using ensemble deep learning,” Visual Computer, vol. 38, no. 11, pp. 3897–3910, Nov. 2022, doi: 10.1007/s00371-021-02230-2.

K. R. Bhatele and S. S. Bhadauria, “Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning,” Multimed Tools Appl, 2022, doi: 10.1007/s11042-022-13439-1.

N. Kumar, M. Gupta, D. Gupta, and S. Tiwari, “Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images,” J Ambient Intell Humaniz Comput, 2021, doi: 10.1007/s12652-021-03306-6.

Z. Wang, J. Dong, and J. Zhang, “Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images,” J Shanghai Jiaotong Univ Sci, vol. 27, no. 1, pp. 70–80, Feb. 2022, doi: 10.1007/s12204-021-2392-3.

A. Paul, A. Basu, M. Mahmud, M. S. Kaiser, and R. Sarkar, “Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays,” Neural Comput Appl, 2022, doi: 10.1007/s00521-021-06737-6.

K. el Asnaoui, “Design ensemble deep learning model for pneumonia disease classification,” Int J Multimed Inf Retr, vol. 10, no. 1, pp. 55–68, Mar. 2021, doi: 10.1007/s13735-021-00204-7.

A. Dhande and R. Malik, “Design of a highly efficient crop damage detection ensemble learning model using deep convolutional networks,” J Ambient Intell Humaniz Comput, 2022, doi: 10.1007/s12652-022-04352-4.

L. D. Nguyen, R. Gao, D. Lin, and Z. Lin, “Biomedical image classification based on a feature concatenation and ensemble of deep CNNs,” J Ambient Intell Humaniz Comput, 2019, doi: 10.1007/s12652-019-01276-4.

E. Jangam, A. A. D. Barreto, and C. S. R. Annavarapu, “Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking,” Applied Intelligence, vol. 52, no. 2, pp. 2243–2259, Jan. 2022, doi: 10.1007/s10489-021-02393-4.

S. Sachar and A. Kumar, “Deep ensemble learning for automatic medicinal leaf identification,” International Journal of Information Technology (Singapore), vol. 14, no. 6, pp. 3089–3097, Oct. 2022, doi: 10.1007/s41870-022-01055-z.

P. gifani, A. Shalbaf, and M. Vafaeezadeh, “Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans,” Int J Comput Assist Radiol Surg, vol. 16, no. 1, pp. 115–123, Jan. 2021, doi: 10.1007/s11548-020-02286-w.

F. Altaf, S. M. S. Islam, and N. K. Janjua, “A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays,” Neural Comput Appl, vol. 33, no. 20, pp. 14037–14048, Oct. 2021, doi: 10.1007/s00521-021-06044-0.

F. Younas, M. Usman, and W. Q. Yan, “An ensemble framework of deep neural networks for colorectal polyp classification,” Multimed Tools Appl, Nov. 2022, doi: 10.1007/s11042-022-14177-0.

A. Paul, R. Pramanik, S. Malakar, and R. Sarkar, “An ensemble of deep transfer learning models for handwritten music symbol recognition,” Neural Comput Appl, vol. 34, no. 13, pp. 10409–10427, Jul. 2022, doi: 10.1007/s00521-021-06629-9.

F. Gil, S. Osowski, and M. Slowinska, “Melanoma recognition using deep learning and ensemble of classifiers,” in 2022 23rd International Conference on Computational Problems of Electrical Engineering (CPEE), Sep. 2022, pp. 1–4. doi: 10.1109/CPEE56060.2022.9919681.

J. Waleed, S. Albawi, H. Q. Flayyih, and A. Alkhayyat, “An Effective and Accurate CNN Model for Detecting Tomato Leaves Diseases,” in 4th International Iraqi Conference on Engineering Technology and Their Applications, IICETA 2021, 2021, pp. 33–37. doi: 10.1109/IICETA51758.2021.9717816.

H. Gunduz and S. Yilmaz Gunduz, “Plant Disease Classification using Ensemble Deep Learning,” in 2022 30th Signal Processing and Communications Applications Conference, SIU 2022, 2022. doi: 10.1109/SIU55565.2022.9864776.

O. A. Malik, M. Faisal, and B. R. Hussein, “Ensemble Deep Learning Models for Fine-grained Plant Species Identification,” in 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, 2021. doi: 10.1109/CSDE53843.2021.9718387.

B. Chellapandi, M. Vijayalakshmi, and S. Chopra, “Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease,” in Proceedings - IEEE 2021 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2021, Feb. 2021, pp. 383–387. doi: 10.1109/ICCCIS51004.2021.9397098.

C. Narvekar and M. Rao, “Flower classification using CNN and Transfer Learning in CNN-Agriculture Perspective,” in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, Dec. 2020, pp. 660–664. doi: 10.1109/ICISS49785.2020.9316030.

Y. Wu, X. Qin, Y. Pan, and C. Yuan, “Convolution Neural Network based Transfer Learning for Classification of Flowers,” in 2018 IEEE 3rd International Conference on Signal and Image Processing, 2018, pp. 562–566. doi: 10.1109/SIPROCESS.2018.8600536.

X. He and Y. Chen, “Transferring CNN Ensemble for Hyperspectral Image Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 5, pp. 876–880, May 2021, doi: 10.1109/LGRS.2020.2988494.

Y. Miao and W. Luo, “Improve Generalization Ability of CNN by Data Augmentation and SE Block in Landmark Classification,” in 2022 IEEE 14th International Conference on Computer Research and Development, ICCRD 2022, 2022, pp. 250–255. doi: 10.1109/ICCRD54409.2022.9730256.

M. Goyal, A. Oakley, P. Bansal, D. Dancey, and M. H. Yap, “Skin Lesion Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods,” IEEE Access, vol. 8, pp. 4171–4181, 2020, doi: 10.1109/ACCESS.2019.2960504.

S. Yang and Y. Huang, “Damage identification method of prestressed concrete beam bridge based on convolutional neural network,” Neural Comput Appl, vol. 33, no. 2, pp. 535–545, Jan. 2021, doi: 10.1007/s00521-020-05052-w.

Y. Zhang, S. Wang, H. Zhao, Z. Guo, and D. Sun, “CT image classification based on convolutional neural network,” Neural Comput Appl, vol. 33, no. 14, pp. 8191–8200, Jul. 2021, doi: 10.1007/s00521-020-04933-4.

B. Xu, “Improved convolutional neural network in remote sensing image classification,” Neural Comput Appl, vol. 33, no. 14, pp. 8169–8180, Jul. 2021, doi: 10.1007/s00521-020-04931-6.

J. Jiang et al., “MultiBSP: multi-branch and multi-scale perception object tracking framework based on siamese CNN,” Neural Comput Appl, vol. 34, no. 21, pp. 18787–18803, Nov. 2022, doi: 10.1007/s00521-022-07420-0.

R. Wang, Z. Li, J. Cao, T. Chen, and L. Wang, “Convolutional Recurrent Neural Networks for Text Classification,” in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–6. [Online]. Available:

V. R. S. Dora and V. N. Lakshmi, “Optimal feature selection with CNN-feature learning for DDoS attack detection using meta-heuristic-based LSTM,” Int J Intell Robot Appl, vol. 6, no. 2, pp. 323–349, Jun. 2022, doi: 10.1007/s41315-022-00224-4.

V. Srivastava and B. Biswas, “Manifold Preserving CNN for Pixel-Based Object Labelling in Images for High Dimensional Feature spaces,” Neural Process Lett, vol. 53, no. 1, pp. 607–635, Feb. 2021, doi: 10.1007/s11063-020-10415-4.

X. Li, H. Jiang, M. Xie, T. Wang, R. Wang, and Z. Wu, “A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains,” Advanced Engineering Informatics, vol. 51, Jan. 2022, doi: 10.1016/j.aei.2021.101480.

S. K and P. S. Thilagam, “Multi-layer perceptron based fake news classification using knowledge base triples,” Applied Intelligence, 2022, doi: 10.1007/s10489-022-03627-9.

O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4. Wiley-Blackwell, Jul. 01, 2018. doi: 10.1002/widm.1249.

R. G. Hussain, M. A. Ghazanfar, M. A. Azam, U. Naeem, and S. Ur Rehman, “A performance comparison of machine learning classification approaches for robust activity of daily living recognition,” Artif Intell Rev, vol. 52, no. 1, pp. 357–379, Jun. 2019, doi: 10.1007/s10462-018-9623-5.


  • There are currently no refbacks.

Copyright (c) 2023 Dyah Aruming Tyas, Yudha Islami Sulistya, Yudha Islami Sulistya, Elsi Titasari Br Bangun, Elsi Titasari Br Bangun

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.