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Cited by in F6Publishing
For: Syed AH, Khan T, Hassan A, Alromema NA, Binsawad M, Alsayed AO. An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD). IEEE Access 2020;8:222126-43. [DOI: 10.1109/access.2020.3043715] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 1.7] [Reference Citation Analysis]
Number Citing Articles
1 Anbarasu B, Thaseen IS. Anomaly Detection Using Feature Selection and Ensemble of Machine Learning Models. Computational Methods and Data Engineering 2023. [DOI: 10.1007/978-981-19-3015-7_16] [Reference Citation Analysis]
2 Thushara A. An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs. Comput Biol Med 2022;151:106247. [PMID: 36375415 DOI: 10.1016/j.compbiomed.2022.106247] [Reference Citation Analysis]
3 Tufail AB, Ullah I, Rehman AU, Khan RA, Khan MA, Ma Y, Hussain Khokhar N, Sadiq MT, Khan R, Shafiq M, Eldin ET, Ghamry NA. On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease. Sustainability 2022;14:14695. [DOI: 10.3390/su142214695] [Reference Citation Analysis]
4 Rajayyan S, Mustafa SMM. Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data. AIP 2022. [DOI: 10.18267/j.aip.198] [Reference Citation Analysis]
5 El-sappagh S, Ali F, Abuhmed T, Singh J, Alonso JM. Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022;512:203-24. [DOI: 10.1016/j.neucom.2022.09.009] [Reference Citation Analysis]
6 Cruz J, Mamani W, Romero C, Pineda F. Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System. SN COMPUT SCI 2021;2:202. [DOI: 10.1007/s42979-021-00584-x] [Reference Citation Analysis]
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