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 An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals Subject The topic of the resource Autoencoder; Classification; Convolutional Neural Network; Depression; Electroencephalogram Description An account of the resource Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Aswathy S.U.; Vincent B.; Jacob P.M.; Aniyan N.; Daniel D.; Thomas J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1179-1188. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-27409-1_108" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-27409-1_108</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163295237&amp;doi=10.1007%2F978-3-031-27409-1_108&amp;partnerID=40&amp;md5=cb46eaf136448ba367c406bff0a336a7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163295237&amp;doi=10.1007%2f978-3-031-27409-1_108&amp;partnerID=40&amp;md5=cb46eaf136448ba367c406bff0a336a7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303127408-4 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 Aswathy S.U., Department of Computer Science and Engineering, Marian Engineering College, Kerala, Thiruvananthapuram, India; Vincent B., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Jacob P.M., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Aniyan N., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Daniel D., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Thomas J., Department of Computer Science and Engineering, Christ University, Bangalore, India