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 An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders Subject The topic of the resource Adaptive Harris Hawk Optimizer; Adolescence; Emotion; Mental Health; Sparse Autoencoder Description An account of the resource Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024. Creator An entity primarily responsible for making the resource Vandana; Srivastava S.; Arora N.; Gupta V. Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Technovation, Vol-6, No. 4, pp. 106-123. Publisher An entity responsible for making the resource available Asian Research Association 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/10.54392/irjmt2449" target="_blank" rel="noreferrer noopener">https://doi.org/10.54392/irjmt2449</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201567119&amp;doi=10.54392%2Firjmt2449&amp;partnerID=40&amp;md5=7d50209ab7a98ebfced765bdf534c6fe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201567119&amp;doi=10.54392%2firjmt2449&amp;partnerID=40&amp;md5=7d50209ab7a98ebfced765bdf534c6fe</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25821040 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 Vandana, School of Sciences, Christ University, Bengaluru, India; Srivastava S., School of Sciences, Christ University, Bengaluru, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Gupta V., School of Sciences, Christ University, Bengaluru, India