English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The work investigates the use of two types of glottal flow derivative-based image variants of the input signal with an n-dilated (nD)-inception-layers-based deep learning model for providing optimal labels. The authors have proposed an n-dilated (nD) inception layer-based adversarial pathological response (APR) net deep learning model. This model is trained using the two image databases separately in an adversarial manner so that when a test image is common to test image is applied to both the networks. The results show a mean accuracy of 96.82%, 96.36%, and 99.35% for the Glottal inverse filtering with extended Kalman Filter-Morse scalogram (GIFEKF-MS) APRNet, Glottal inverse filtering with extended Kalman Filter-spectrogram (GIFEKF-S) APRNet, and proposed APR fusion net respectively using the VOice ICar fEDerico II (VOICED) dataset; and mean accuracies 95.67%, 93.27%, and 99.04% for the GIFEKF-MS APRNet, GIFEKF-S APRNet, and proposed APR fusion net respectively using the Saarbrucken voice database (SVD)dataset.

international journal of software innovation

An Intelligent System for the Diagnosis of Voice Pathology Based on Adversarial Pathological Response (APR) Net Deep Learning Model: An Intelligent System for the Diagnosis of Voice Pathology-Based Deep Learning