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 Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy Subject The topic of the resource Electroencephalograph (eeg); Emotion recognition; Feature selection; Linear regression based correlation coefficient; Machine learning techniques; Multi-layer stacking model Description An account of the resource Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved. Creator An entity primarily responsible for making the resource Tahseen S.; Danti A. Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications, Vol-13, No. 6, pp. 433-442. Publisher An entity responsible for making the resource available Science and Information Organization 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/10.14569/IJACSA.2022.0130654" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2022.0130654</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133405438&amp;doi=10.14569%2FIJACSA.2022.0130654&amp;partnerID=40&amp;md5=7e9025aa218d53d6f7c1110b4f8fa1e1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133405438&amp;doi=10.14569%2fIJACSA.2022.0130654&amp;partnerID=40&amp;md5=7e9025aa218d53d6f7c1110b4f8fa1e1</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 2158107X 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 Tahseen S., Dept. of Computer Science and Engineering, Christ University, Bangalore, India; Danti A., Dept. of Computer Science and Engineering, Christ University, Bangalore, India