Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource HTLML: Hybrid AI Based Model for Detection of Alzheimers Disease Subject The topic of the resource Alzheimers disease; convolutional neural network; deep learning; DenseNet121; DenseNet201; gaussian NB; SVM; XGBoost Description An account of the resource Alzheimers disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brains ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Nae base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. 2022 by the authors. Creator An entity primarily responsible for making the resource Sharma S.; Gupta S.; Gupta D.; Altameem A.; Saudagar A.K.J.; Poonia R.C.; Nayak S.R. Source A related resource from which the described resource is derived Diagnostics, Vol-12, No. 8 Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/diagnostics12081833" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/diagnostics12081833</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137409992&amp;doi=10.3390%2Fdiagnostics12081833&amp;partnerID=40&amp;md5=680d23a1518edb0ddfde64f60f0bff6d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137409992&amp;doi=10.3390%2fdiagnostics12081833&amp;partnerID=40&amp;md5=680d23a1518edb0ddfde64f60f0bff6d</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20754418 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma S., Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Gupta S., Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Gupta D., Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Nayak S.R., Amity School of Engineering and Technology, Amity University, Uttar Pradesh, Noida, 201301, India