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 Deep Learning Based Age Estimation Model Subject The topic of the resource Accuracy; convolutional neural network (CNN); Data Augmentation; Machine learning algorithm; Mean absolute error; Preprocessing; RELU Description An account of the resource To improve accuracy and resilience in demographic categorization, this research presents a novel use of Convolutional Neural Networks (CNNs) for age prediction. Deep learning is utilized to achieve this goal. Precise estimation of age has become essential in a variety of areas, including human-computer interaction, marketing, and healthcare. The ability of CNNs to handle the intricacies of facial features for accurate demographic forecasts is examined in this study. The research covers every step of the age prediction process, including dataset collection, prepossessing, model architecture, and assessment measures. The CNN is trained to automatically extract hierarchical characteristics from facial photos, which enables the model to recognize complex patterns related to age. The architecture's flexibility to different lighting conditions, facial expressions, and postures. In this research, we deal with deep learning-based perceived age estimation in still-face pictures. Our Convolution Neural Network models (CNNs) have been trained prior on Image Net for picture classification, as they use the VGG architecture. In addition, we analyze the effects of tailoring over Web photos having known age, considering a lack of apparent age-annotated annotated images. In addition, this work adds to the increasing library of studies on the use of deep learning methods for demographic data evaluation by showing the effectiveness of CNNs to predict age. The results show how, in practical situations, CNNs could significantly enhance the precision and dependability of age prediction systems. 2024 IEEE. Creator An entity primarily responsible for making the resource Reddy G.J.; Bendili S.; Vinodha D.; Jenefa J.; Sambandam R.K.; Vetriveeran D. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545269" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545269</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204482593&amp;doi=10.1109%2FTQCEBT59414.2024.10545269&amp;partnerID=40&amp;md5=13551662f0d49d361ac40ccbfeca5911" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204482593&amp;doi=10.1109%2fTQCEBT59414.2024.10545269&amp;partnerID=40&amp;md5=13551662f0d49d361ac40ccbfeca5911</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Reddy G.J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Bendili S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vinodha D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Jenefa J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Sambandam R.K., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vetriveeran D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India