Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease

References

• Abunadi, I, Albraikan, A. A., Alzahrani, J. S., & Eltahir, M. M., & Healthcare, and undefined 2022. (2022). An automated glowworm swarm optimization with an inception-based deep convolutional neural network for COVID-19 diagnosis and classification. Mdpi.Com. https://www.mdpi.com/1579912
   Google Scholar
• Abunadi, I., & Senan, E. M. (2021). Deep learning and machine learning techniques of diagnosis dermoscopy images for early detection of skin diseases. Electronics 2021, 10(24), 3158. Multidisciplinary Digital Publishing Institute: 3158. doi:10.3390/ELECTRONICS10243158
   Google Scholar
• Ahmed, I. A., Senan, E. M., Rassem, T. H., Ali, M. A. H., Ahmad Shatnawi, H. S., Alwazer, S. M., & Alshahrani, M. (2022). Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques. Electronics 2022, 11(4), 530. Multidisciplinary Digital Publishing Institute: 530. doi:10.3390/ELECTRONICS11040530.
   Google Scholar
• Ahmed, O. b., Mizotin, M., Benois-Pineau, J., Allard, M., Catheline, G., Amar, B., & Allard, M. (2015). Alzheimer’s disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Computerized Medical Imaging and Graphics, 44, 13–25. doi:10.1016/j.compmedimag.2015.04.007
   PubMed Web of Science ®Google Scholar
• Al-Khuzaie, F. E. K., Bayat, O., & Duru, A. D. (2021). Diagnosis of Alzheimer disease using 2D MRI slices by convolutional neural network. Applied Bionics and Biomechanics, 2021. Hindawi Limited. doi:10.1155/2021/6690539.
   PubMed Web of Science ®Google Scholar
• Al-Mekhlafi, Z. G., Senan, E. M., & Computers, and undefined 2022. (2022). Deep learning and machine learning for early detection of stroke and haemorrhage. Eprints.Bournemouth.Ac.Uk. http://eprints.bournemouth.ac.uk/36721/
   Google Scholar
• Altinkaya, E., Polat, K., Barakli, B., & Journal of the Institute of Electronics, and undefined 2020. (2019). Detection of Alzheimer’s disease and dementia states based on deep learning from MRI images: A comprehensive review. Iecscience.Org, 1, 39–53. doi:10.33969/JIEC.2019.11005.
   Google Scholar
• “Alzheimer’s Dataset (4 Class of Images) | Kaggle”. 2022. https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images
   Google Scholar
• Amoroso, N., Diacono, D., Fanizzi, A., La Rocca, M., & Journal of neuroscience, and undefined 2018. (2022). Deep learning reveals alzheimer’s disease onset in MCI subjects: Results from an international challenge. Elsevier. https://www.sciencedirect.com/science/article/pii/S0165027017304296
   Google Scholar
• Ballarini, T., van Lent, D. M., Brunner, J., Schröder, A., Wolfsgruber, S., Altenstein, S., Brosseron, F., et al. (2021). Mediterranean diet, Alzheimer disease biomarkers, and brain atrophy in old age. Neurology, 96(24), e2920–e2932. Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. doi:10.1212/WNL.0000000000012067
   PubMed Web of Science ®Google Scholar
• Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., Fischl, B., & Neuroimage, and undefined 2013. (2022). Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Elsevier. https://www.sciencedirect.com/science/article/pii/S1053811913005430
   Google Scholar
• Budak, Ü., Cömert, Z., Rashid, Z. N., Şengür, A., & Applied Soft Computing, and undefined 2019. (2022). Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Elsevier. https://www.sciencedirect.com/science/article/pii/S1568494619305460
   Google Scholar
• Cai, S., Han, D., Yin, X., Li, D., & Chang, C. C. (2022). A hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Taylor & Francis 34(1), 551–577. doi:10.1080/09540091.2021.2024509.
   Google Scholar
• Dai, Y., & Wang, T. (2021). Prediction of customer engagement behaviour response to marketing posts based on machine learning. Taylor & Francis 33(4), 891–910. doi:10.1080/09540091.2021.1912710.
   Google Scholar
• D’Angelo, G., Palmieri, F., Robustelli, A., & Castiglione, A. (2021). Effective classification of android malware families through dynamic features and neural networks. Taylor & Francis: 33(3), 786–801. doi:10.1080/09540091.2021.1889977.
   Google Scholar
• Duc, N. T., Ryu, S., Qureshi, M. N. I., Choi, M., Lee, K. H., & Lee, B. (2020). 3D-Deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state FMRI. Neuroinformatics, Springer: 18(1), 71–86. doi:10.1007/s12021-019-09419-w
   PubMed Web of Science ®Google Scholar
• Ebrahimi-Ghahnavieh, A, Luo, S., & 2019 IEEE International, and undefined 2019. (2022). Transfer learning for Alzheimer’s disease detection on MRI images. Ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/8784845/
   Google Scholar
• Elhoseny, M., Shankar, K., Uthayakumar, J., & Scientific reports, and undefined 2019. (2022). Intelligent diagnostic prediction and classification system for chronic kidney disease. Nature.Com. https://www.nature.com/articles/s41598-019-46074-2
   Google Scholar
• El-Sappagh, S., Abuhmed, T., Riazul Islam, S. M., & Kwak, K. S. (2020). Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data. Neurocomputing, 412(October) Elsevier, 197–215. doi:10.1016/j.neucom.2020.05.087
   Google Scholar
• Fan, Z., Xu, F., Qi, X., Li, C., & Yao, L. (2020). Classification of Alzheimer’s disease based on brain MRI and machine learning. Neural Computing and Applications, 32(7) Springer, 1927–1936. doi:10.1007/s00521-019-04495-0
   Web of Science ®Google Scholar
• Fuse, H., Oishi, K., Maikusa, N., Fukami, T., & 2018 Joint 10th, and undefined 2018. (2022). Detection of Alzheimer’s disease with shape analysis of MRI images. Ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/8716217/
   Google Scholar
• Hu, N., Zhang, D., Xie, K., Liang, W., & Hsieh, M. Y. (2021). Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting. Taylor & Francis 34(1), 429–448. doi:10.1080/09540091.2021.2006607.
   Google Scholar
• Huang, H., Zheng, S., Yang, Z., Wu, Y., Li, Y., Qiu, J., Cheng, Y., Lin, P., Lin, Y., Guan, J., Mikulis, D. J., Zhou, T., & Wu, R. (2022). Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer’s disease based on cerebral gray matter changes. Cerebral Cortex. doi:10.1093/CERCOR/BHAC099.
   Google Scholar
• International, Alzheimer’s Disease, and Christina Patterson. (2018). World Alzheimer report 2018: The state of the art of dementia research: New frontiers. https://www.alzint.org/resource/world-alzheimer-report-2018/
   Google Scholar
• Islam, J., Zhang, Y., & Proceedings of the IEEE conference on, and undefined 2018. (2022). Early diagnosis of Alzheimer’s disease: A neuroimaging study with deep learning architectures. Openaccess.Thecvf.Com. https://openaccess.thecvf.com/content_cvpr_2018_workshops/w36/html/Islam_Early_Diagnosis_of_CVPR_2018_paper.html
   Google Scholar
• Ithapu, V., Singh, V., Lindner, C., Austin, B. P., Hinrichs, C., Carlsson, C. M., Bendlin, B. B., & Johnson, S. C. (2014). Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies. Human Brain Mapping, 35(8), John Wiley & Sons, Ltd 4219–4235. doi:10.1002/hbm.22472
   PubMed Web of Science ®Google Scholar
• Janelidze, S., Mattsson, N., Palmqvist, S., Smith, R., & Nature medicine, and undefined 2020. (2022). Plasma P-Tau181 in Alzheimer’s disease: Relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s Dementia. Nature.Com. https://www.nature.com/articles/s41591-020-0755-1
   Google Scholar
• Johnson, K. A., Fox, N. C., Sperling, R. A., & Klunk, W. E. (2012). Brain imaging in Alzheimer disease. Cold Spring Harbor Perspectives in Medicine, 2(4), A006213. Cold Spring Harbor Laboratory Press. doi:10.1101/cshperspect.a006213
   PubMed Web of Science ®Google Scholar
• Karim, R., Shahrior, A., & Rahman, M. M. (2021). Machine learning-based Tri-stage classification of Alzheimer’s progressive neurodegenerative disease using PCA and MRMR administered textural, orientational, and spatial features. International Journal of Imaging Systems and Technology, 31(4), John Wiley & Sons, Ltd: 2060–2074. doi:10.1002/ima.22622
   Web of Science ®Google Scholar
• Lazli, L., Boukadoum, M., & Mohamed, O. A. (2020). A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on Alzheimer disease diagnosis and the contribution of the multimodal fusion. Applied Sciences 2020, 10(5), 1894. Multidisciplinary Digital Publishing Institute: 1894. doi:10.3390/APP10051894.
   Google Scholar
• Lella, E., Lombardi, A., Amoroso, N., Diacono, D., Maggipinto, T., Monaco, A., Bellotti, R., and Tangaro, S. (2022). Machine learning and DWI brain communicability networks for Alzheimer’s disease detection. Mdpi.Com doi:10.3390/app10030934.
   Google Scholar
• Liu, G., Ma, J., Hu, T., & Gao, X. (2022). A feature selection method with feature ranking using genetic programming. Taylor & Francis 34(1), 1146–1168. doi:10.1080/09540091.2022.2049702.
   Google Scholar
• Maqsood, M., Nazir, F., Khan, U., Aadil, F., Jamal, H., Mehmood, I., & Song, O. Y. (2019). Transfer learning assisted classification and detection of Alzheimer’s disease stages using 3D MRI scans. Sensors 2019, 19(11), 2645. Multidisciplinary Digital Publishing Institute: 2645. doi:10.3390/S19112645.
   Google Scholar
• Meghdadi, A. H., Karic, M. S., McConnell, M., Rupp, G., Richard, C., Hamilton, J., Salat, D., & Berka, C. (2021). Resting state EEG biomarkers of cognitive decline associated with Alzheimer’s disease and mild cognitive impairment. PLOS ONE, Public Library of Science 16(2), e0244180. doi:10.1371/journal.pone.0244180
   PubMed Web of Science ®Google Scholar
• Mohammed, B. A., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Al-Mekhlafi, Z. G., Almurayziq, T. S., & Ghaleb, F. A. (2021a). Multi-Method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods. Electronics 2021, 10(22), 2860. Multidisciplinary Digital Publishing Institute: 2860. doi:10.3390/ELECTRONICS10222860.
   Google Scholar
• Mohammed, B. A., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Al-Mekhlafi, Z. G., Almurayziq, T. S., & Ghaleb, F. A. (2021b). Multi-Method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods. Electronics 2021, 10(22), 2860. Multidisciplinary Digital Publishing Institute: 2860. doi:10.3390/ELECTRONICS10222860.
   Google Scholar
• Morabito, F. C., Campolo, M., Mammone, N., Versaci, M., Franceschetti, S., Tagliavini, F., Sofia, V., Fatuzzo, D., Gambardella, A., Labate, A., Mumoli, L., Tripodi, G. G., Gasparini, S., Cianci, V., Sueri, C., Ferlazzo, E., & Aguglia, U. (2017). Deep learning representation from electroencephalography of early-stage Creutzfeldt-Jakob disease and features for differentiation from rapidly progressive dementia. International Journal of Neural Systems, 27(2), 1650039. World Scientific Publishing Co. Pte Ltd. doi:10.1142/S0129065716500398
   PubMed Web of Science ®Google Scholar
• Munadhil, Z., Gharghan, S. K., Mutlag, A. H., Al-Naji, A., & Chahl, J. (2020). Neural network-based Alzheimer’s patient localization for wireless sensor network in an indoor environment. IEEE Access, 8, Institute of Electrical and Electronics Engineers Inc.: 150527–150538. doi:10.1109/ACCESS.2020.3016832
   Web of Science ®Google Scholar
• Nadel, J., Mcnally, J. S., Digiorgio, A., Grandhi, R., & Gatto, G. (2021). Emerging utility of applied magnetic resonance imaging in the management of traumatic brain injury. Medical Sciences 2021, 9(1), 10. Multidisciplinary Digital Publishing Institute: 10. doi:10.3390/MEDSCI9010010.
   Google Scholar
• Pinaya, W. H. L., Scarpazza, C., Garcia-Dias, R., Vieira, S., & Scientific reports, and undefined 2021. (2022). Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study. Nature.Com. https://www.nature.com/articles/s41598-021-95098-0
   Google Scholar
• Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N., & Rubino, I. (2021). Diagnosis of early Alzheimer’s disease: Clinical practice in 2021. Journal of Prevention of Alzheimer’s Disease, 8(3), Serdi-Editions 371–386. doi:10.14283/JPAD.2021.23/TABLES/5
   PubMed Web of Science ®Google Scholar
• Qin, M. (2022). Application of efficient recognition algorithm based on deep neural network in English teaching scene. Taylor & Francis, 34(1), 1913–1928. doi:10.1080/09540091.2022.2088699.
   Google Scholar
• Raghavaiah, P., & Varadarajan, S. (2022). A CAD system design for Alzheimer’s disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomedical Signal Processing and Control, 75(May), Elsevier: 103571. doi:10.1016/j.bspc.2022.103571
   Google Scholar
• Reuter, M., Schmansky, N. J., Rosas, H. D., Fischl, B., & Neuroimage, and undefined 2012. (2022). Within-subject template estimation for unbiased longitudinal image analysis. Elsevier. https://www.sciencedirect.com/science/article/pii/S1053811912002765
   Google Scholar
• Salehi, A. W., Baglat, P., Gupta, G., & Int. J. Eng. Adv. Technol, and undefined 2020. (2022). Alzheimer’s disease diagnosis using deep learning techniques. Researchgate.Net. https://www.researchgate.net/profile/Waleed-Salehi/publication/341152313_Alzheimer’s_Disease_Diagnosis_using_Deep_Learning_Techniques/links/5f44e2e3299bf13404f1c051/Alzheimers-Disease-Diagnosis-using-Deep-Learning-Techniques.pdf
   Google Scholar
• Senan, E. M., Abunadi, I., Jadhav, M. E., & Fati, S. M. (2021). Score and correlation coefficient-based feature selection for predicting heart failure diagnosis by using machine learning algorithms. Computational and Mathematical Methods in Medicine, 2021(December), Hindawi. doi:10.1155/2021/8500314.
   Google Scholar
• Senan, E. M., & Jadhav, M. E. (2021). Communications in computer and information science. Communications in Computer and Information Science, 1381, CCIS. Springer Science and Business Media Deutschland GmbH: 14–25. doi:10.1007/978-981-16-0493-5_2
   Google Scholar
• Senan, E. M., & Jadhav, M. E. (2022). Advances in intelligent systems and computing. 125–134. doi:10.1007/978-981-16-4538-9_13.
   Google Scholar
• Senan, E. M., Jadhav, M. E., Kadam, A., & 2021 6th International, and undefined 2021. (2022). Classification of PH2 images for early detection of skin diseases. Ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/9417893/
   Google Scholar
• Shaikh, T. A., Ali, R., & Magnetic resonance imaging, and undefined 2019. (2022). Automated atrophy assessment for Alzheimer’s disease diagnosis from brain MRI images. Elsevier. https://www.sciencedirect.com/science/article/pii/S0730725X1930102X
   Google Scholar
• Soldan, A., Gazes, Y., & Stern, Y. (2017). Alzheimer’s disease. The Curated Reference Collection in Neuroscience and Biobehavioral Psychology, January. Elsevier, 108–115. doi:10.1016/B978-0-12-809324-5.06319-7.
   Google Scholar
• Song, E., & Li, H. (2021). A hybrid differential evolution for multi-objective optimisation problems. Taylor & Francis, 34(1), 224–253. doi:10.1080/09540091.2021.1984396.
   Google Scholar
• Song, M., Jung, H., Lee, S., Kim, D., & Ahn, M. (2021). Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm. Brain Sciences, 11(4), 453. Multidisciplinary Digital Publishing Institute: 453. doi:10.3390/brainsci11040453
   PubMed Web of Science ®Google Scholar
• Sun, X., Wang, Q., Zhang, X., Xu, C., & Zhang, W. (2021). Deep blur detection network with boundary-aware multi-scale features. Taylor & Francis, 34(1), 766–784. doi:10.1080/09540091.2021.1933906.
   Google Scholar
• Swain, B. K., Sahani, M., & Sharma, R. (2020). Lecture notes in electrical engineering. Lecture Notes in Electrical Engineering, 630, Springer, Singapore: 531–542. doi:10.1007/978-981-15-2305-2_43
   Google Scholar
• Tabatabaei-Jafari, H., Shaw, M. E., & Cherbuin, N. (2015). Cerebral atrophy in mild cognitive impairment: A systematic review with meta-analysis. Alzheimer’s & Dementia (Amsterdam, Netherlands), 1(4), 487–504. Alzheimers Dement (Amst). doi:10.1016/J.DADM.2015.11.002.
   PubMedGoogle Scholar
• Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Medical Imaging and, and undefined 2019. (2022). Convolutional neural networks for multi-class brain disease detection using MRI images. Elsevier. https://www.sciencedirect.com/science/article/pii/S0895611119300886
   Google Scholar
• Too, J., Sadiq, A. S., & Mirjalili, S. M. (2021). A conditional opposition-based particle swarm optimisation for feature selection. Taylor & Francis, 34(1), 339–361. doi:10.1080/09540091.2021.2002266.
   Google Scholar
• Ulep, M. G., Saraon, S. K., & McLea, S. (2018). Alzheimer disease. The Journal for Nurse Practitioners, 14(3), Elsevier: 129–135. doi:10.1016/j.nurpra.2017.10.014
   Google Scholar
• Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific Reports 2021, Nature Publishing Group 11(1), 1–13. doi:10.1038/s41598-020-74399-w.
   PubMed Web of Science ®Google Scholar
• Viña, J., & Lloret, A. (2010). Why women have more Alzheimer’s disease than Men: Gender and mitochondrial toxicity of amyloid-beta peptide. Journal of Alzheimer’s Disease : Jad, 20(SUPPL.2), J Alzheimers Dis. doi:10.3233/JAD-2010-100501.
   PubMedGoogle Scholar
• Wada, A., Tsuruta, K., Irie, R., Kamagata, K., & in Medical Sciences, and undefined 2019. (2022). Differentiating Alzheimer’s disease from dementia with lewy bodies using a deep learning technique based on structural brain connectivity. Ncbi.Nlm.Nih.Gov. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc6630050/
   Google Scholar
• Wang, S. H., & Chen, Y. (2020). Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique. Multimedia Tools and Applications, 79(21), Springer 15117–15133. doi:10.1007/S11042-018-6661-6
   Web of Science ®Google Scholar
• Wang, Z., Han, D., Li, M., Liu, H., & Cui, M. (2022). The abnormal traffic detection scheme based on PCA and SSH. Taylor & Francis 34(1), 1201–1220. doi:10.1080/09540091.2022.2051434.
   Google Scholar
• Wenjin, H., Panpan, X., Guoyuan, H., Huiyuan, T., Huafei, S., & Chaoyang, Y. (2022). Few shot object detection for headdresses and seats in thangka Yidam based on ResNet and deformable convolution. Taylor & Francis, 34(1), 732–748. doi:10.1080/09540091.2022.2041554.
   Google Scholar
• Wu, J., Zeng, P., Liu, P., & Lv, G. (2022). Automatic classification method of liver ultrasound standard plane images using Pre-trained convolutional neural network. Connection Science, 34(1), Taylor and Francis Ltd. 975–989. doi:10.1080/09540091.2021.2015748
   Web of Science ®Google Scholar
• Xu, T., Liu, P., Li, P., Wang, X., Xue, H., Guo, J., Dong, B., & Sun, P. (2022). Racnet: Risk assessment Net of cervical lesions in colposcopic images. Taylor & Francis 34(1), 2139–2157. doi:10.1080/09540091.2022.2085665.
   Google Scholar
• Yagis, E., Citi, L., Diciotti, S., Marzi, C., Atnafu, S. W., & De Herrera, A. G. S. (2022). 3D convolutional neural networks for diagnosis of Alzheimer’s disease via structural MRI. Ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/9183323/
   Google Scholar
• Yang, B., Cai, A., & Lin, W. (2022). Analysis of early fault vibration detection and analysis of offshore wind power transmission based on deep neural network. Connection Science, Taylor & Francis 34(1), 1005–1017. doi:10.1080/09540091.2021.2025340
   Web of Science ®Google Scholar
• Yang, D., Du, Y., Yao, H., & Bao, L. (2022). Image semantic segmentation with hierarchical feature fusion based on deep neural network. Taylor & Francis: 34(1), 1772–1784. doi:10.1080/09540091.2022.2082384.
   Google Scholar
• Yildirim, M., Cinar, A. C., & Ingénierie des Systèmes d Inf., and undefined 2020. (2022). Classification of Alzheimer’s Disease MRI Images with CNN Based Hybrid Method. Researchgate.Net. doi:10.18280/isi.250402.
   Google Scholar
• Ying, L., Nan, Z. Q., Ping, W. F., Kiang, C. T., Pang, L. K., Chang, Z. H., Lu, C., Jun, L. G., & Nam, L. (2021). Adaptive weights learning in CNN feature fusion for crime scene investigation image classification. 33(3), Taylor & Francis, 719–734. doi:10.1080/09540091.2021.1875987.
   Google Scholar
• Zaretsky, D. V., & Zaretskaia, M. V. (2021). Mini-Review: Amyloid degradation toxicity hypothesis of Alzheimer’s disease. Neuroscience Letters, 756(June), Elsevier 135959. doi:10.1016/j.neulet.2021.135959
   PubMedGoogle Scholar
• Zhang, J., & Tai, Y. (2021). Secure medical digital twin via human-centric interaction and cyber vulnerability resilience. Taylor & Francis, 34(1), 895–910. doi:10.1080/09540091.2021.2013443.
   Google Scholar
• Zhang, X. X., Tian, Y., Wang, Z. T., Ma, Y. H., Tan, L., & Yu, J. T. (2021). The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. Journal of Prevention of Alzheimer’s Disease, 8(3), Serdi-Editions 313–321. doi:10.14283/JPAD.2021.15/FIGURES/2.
   PubMed Web of Science ®Google Scholar
• Zhao, C., Li, X., Shao, Z., Yang, H., & Wang, F. (2022). Multi-Featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction. 34(1), Taylor & Francis 1252–1272. doi:10.1080/09540091.2022.2061915.
   Google Scholar
• Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2021). Applications of federated learning in smart cities: Recent advances, taxonomy, and open challenges. 34(1), Taylor & Francis 1–28. doi:10.1080/09540091.2021.1936455.
   Google Scholar