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 Classification Algorithms Used in the Study of EEG-Based Epileptic Seizure Detection Subject The topic of the resource Classification; Decision Tree Classifier; EEG; Epileptic Seizures; KNN; Linear Discriminant Analysis; SVM; XGBOOST Description An account of the resource Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent. 2021 IEEE. Creator An entity primarily responsible for making the resource Umme Salma M.; Najmusseher Source A related resource from which the described resource is derived Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021, pp. 1518-1521. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOSEC51865.2021.9591824" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOSEC51865.2021.9591824</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123219227&amp;doi=10.1109%2FICOSEC51865.2021.9591824&amp;partnerID=40&amp;md5=1289ffceb8a262902f5365d356bf1894" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123219227&amp;doi=10.1109%2fICOSEC51865.2021.9591824&amp;partnerID=40&amp;md5=1289ffceb8a262902f5365d356bf1894</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166543368-6 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 Umme Salma M., CHRIST (Deemed To Be University), Department of Computer Science, Bangalore, India; Najmusseher, CHRIST (Deemed To Be University), Department of Computer Science, Bangalore, India