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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification Subject The topic of the resource ASPP; Convolutional neural network; EffUnet; Glaucoma detection; Tensor tubal PCA Description An account of the resource Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Venkatachalam K.; Bacanin N.; Kabir E.; Prabu P. Source A related resource from which the described resource is derived Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol-13079 LNCS, pp. 181-192. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-030-90885-0_17" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-90885-0_17</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120047999&amp;doi=10.1007%2F978-3-030-90885-0_17&amp;partnerID=40&amp;md5=cd66cccc257cb1c01c4f0548c3d1b6a2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120047999&amp;doi=10.1007%2f978-3-030-90885-0_17&amp;partnerID=40&amp;md5=cd66cccc257cb1c01c4f0548c3d1b6a2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 3029743; ISBN: 978-303090884-3 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Venkatachalam K., Department of Applied Cybernetics, Faculty of Science, University of Hradec Krov Hradec Krov 50003, Czech Republic; Bacanin N., Department of Computer Science, Singidunum University, Belgrade, 160622, Serbia; Kabir E., School of Sciences, University of Southern Queensland, Toowoomba, Darling Heights, 4350, Australia; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560074, India