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 Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification Subject The topic of the resource Enhanced sunflower optimisation; Gait recognition; GAIT-IT; GAIT-ITS; Improved convolutional neural network; Median filter; Modified bidirectional LSTM; Parkinsons disease Description An account of the resource The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. Creator An entity primarily responsible for making the resource Reshma S.; Chennakesavulu M.; Patil S.S.; Lamani M.R. Source A related resource from which the described resource is derived International Journal of Information Technology (Singapore), Vol-16, No. 6, pp. 3963-3971. Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. 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/10.1007/s41870-024-01886-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-024-01886-y</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191988973&amp;doi=10.1007%2Fs41870-024-01886-y&amp;partnerID=40&amp;md5=bf0d81b093125d57da7290dcd2d81d77" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191988973&amp;doi=10.1007%2fs41870-024-01886-y&amp;partnerID=40&amp;md5=bf0d81b093125d57da7290dcd2d81d77</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25112104 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 Reshma S., Dept of Artificial Intelligence and Machine Learning, Dayananda Sagar College of Engineering, Bangalore, India; Chennakesavulu M., Department of Electronics and Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous), Andhra Pradesh, Nandyal, 518501, India; Patil S.S., Rajarambapu Institute of Technology, Rajaramnagar, Uran Islampur, India; Lamani M.R., CHRIST(Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India