Review and content analysis of textual expressions as a marker for depressive and anxiety disorders (DAD) detection using machine learning DOI Creative Commons
Chandra Mani Sharma,

Darsh Damani,

Vijayaraghavan M. Chariar

et al.

Discover Artificial Intelligence, Journal Year: 2023, Volume and Issue: 3(1)

Published: Nov. 20, 2023

Abstract Depressive disorders (including major depressive disorder and dysthymia) anxiety (generalized or GAD) are the two most prevalent mental illnesses. Early diagnosis of these afflictions can lead to cost-effective treatment with a better outcome prospectus. With advent digital technology platforms, people express themselves by various means, such as social media posts, blogs, journals, instant messaging services, etc. Text remains common convenient form expression. Therefore, it be used predict onset depression. Scopus Web Science (WoS) databases were retrieve relevant literature using set predefined search strings. Irrelevant publications filtered multiple criteria. The research meta data was subsequently analyzed Biblioshiny Tool R. Finally, comparative analysis suitable documents is presented. A total 103 for bibliometric mapping in terms over past years, productivity authors, institutions, countries, collaborations, trend topics, keyword co-occurrence, Neural networks support vector machines popular ML techniques; word embeddings extensively text representations. There shift toward modalities. SVM, Naive Bayes, LSTM methods; source (Twitter platform); audio modality that combined (DAD) detection. provides good cues detection DAD machine learning. However, findings cases based on limited amount data. Using large amounts other modalities help develop more generalized DAD-detection systems. Asian countries leading output China India being top number publications. international collaborations needed. Limited exists disorders. Co-occurrence high (33% studies).

Language: Английский

Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model DOI Creative Commons

Mahmoud Selim,

Mohammed Assiri

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 493 - 502

Published: Feb. 8, 2025

Language: Английский

Citations

1
Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis DOI
Asifa Nazir, Ahsan Hussain, Mandeep Singh

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 5, 2024

Language: Английский

Citations

7
DepressionEmo: A novel dataset for multilabel classification of depression emotions DOI

Abu Bakar Siddiqur Rahman,

Hoang-Thang Ta,

Lotfollah Najjar

et al.

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 366, P. 445 - 458

Published: Aug. 28, 2024

Language: Английский

Citations

5
Physics-Constrained Three-Dimensional Swin Transformer for Gravity Data Inversion DOI Creative Commons
Ping Yu,

Longran Zhou,

Shuai Zhou

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 113 - 113

Published: Jan. 1, 2025

This paper proposes a physics-constrained 3D Swin Transformer (ST) for gravity inversion. By leveraging the self-attention mechanism in ST, method effectively models global dependencies within data, enabling network to reweight features globally and focus on critical anomalous regions. Additionally, prior gradient information is integrated into loss function, hierarchical weight allocation strategy adopted guide model learning boundary of density structures deep-seated more effectively. Synthetic experiments demonstrate that proposed achieves lower errors, better alignment, higher inversion accuracy. The approach further validated using anomaly observations from Gonghe Basin Qinghai, yielding reliable precise results.

Language: Английский

Citations

0
Early Depression Detection from Social Media: State-of-the-Art Approaches DOI

A. Alsaedi,

Wael M. S. Yafooz

Studies in computational intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 61 - 75

Published: Jan. 1, 2025

Language: Английский

Citations

0
Advancing depression detection on social media platforms through fine-tuned large language models DOI
Shahid Munir Shah,

Syeda Anshrah Gillani,

Mirza Baig

et al.

Online Social Networks and Media, Journal Year: 2025, Volume and Issue: 46, P. 100311 - 100311

Published: March 22, 2025

Language: Английский

Citations

0
Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis DOI Creative Commons
Doreen Phiri, Frank Makowa, Vivi Leona Amelia

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e59002 - e59002

Published: April 11, 2025

Background Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research. Objective This review aims to assess the effectiveness user-generated texts and evaluate influence demographic, language, activity, temporal features on through Methods We searched studies 11 databases (CINHAL [through EBSCOhost], PubMed, Scopus, Ovid MEDLINE, Embase, PubPsych, Cochrane Library, Web Science, ProQuest, IEEE Explore, ACM digital library) January 2008 August 2023. included that used texts, learning, reported area under curve, Pearson r, specificity sensitivity (or for their calculation) predict depression. Protocol papers not written English were excluded. extracted study characteristics, population outcome measures, prediction factors each study. A random effects model was extract effect sizes with 95% CIs. Study heterogeneity evaluated forest plots P values Cochran Q test. Moderator analysis performed identify sources heterogeneity. Results total 36 included. observed significant overall correlation between depression, large size (r=0.630, CI 0.565-0.686). noted same demographic (largest size; r=0.642, 0.489-0.757), activity (r=0.552, 0.418-0.663), language (r=0.545, 0.441-0.649), (r=0.531, 0.320-0.693). The platform type (public or private; P<.001), learning approach (shallow deep; P=.048), use measures (yes no; P<.001) moderators. Sensitivity revealed no change results, indicating result stability. Begg-Mazumdar rank (Kendall τb=0.22063; P=.058) Egger test (2-tailed t34=1.28696; P=.207) confirmed absence publication bias. Conclusions Social content can be useful tool Demographics, should considered maximize accuracy models. Additionally, type, approach, models need attention. challenging, findings may apply broader population. Nevertheless, our offer valuable future Trial Registration PROSPERO CRD42023427707; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023427707

Language: Английский

Citations

0
EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals DOI
Mustufa Haider Abidi,

Khaja Moiduddin,

Rashid Ayub

et al.

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(9), P. 5168 - 5179

Published: May 15, 2024

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of human brain, it is considered an effective tool for analyzing depression. However, manual detection using EEG signals time-consuming tedious. To address this, fully automatic identification models have been designed assist clinicians. In this study, we propose novel automated deep learning-based system signals. The required are gathered from publicly available databases, three sets features extracted original signal. Firstly, spectrogram images generated signal, 3-dimensional Convolutional Neural Networks (3D-CNN) employed extract features. Secondly, 1D-CNN utilized collected Thirdly, spectral Following feature extraction, optimal weights fused with selection carried out developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, undergo analysis Self-Attention-based Gated Densenet (SA-GDensenet) detection. parameters within network optimized assistance same COIWSO. Finally, implementation results analyzed comparison existing models. experimentation findings model show 96% accuracy. Throughout empirical result, better performance than traditional approaches.

Language: Английский

Citations

3
Detection of Depression in Social Media Posts using Emotional Intensity Analysis DOI Open Access

M. Kiran Myee,

R. Deepthi Crestose Rebekah,

T K Deepa

et al.

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(5), P. 16207 - 16211

Published: Oct. 9, 2024

Tapping into digital footprints on social media, this research focuses providing new insights detecting depression through textual analysis. Initially, emotional raw data found in media posts, aimed particularly at the expressions of anger, fear, joy, and sadness, were collected analyzed. These emotions, each scored by their intensity, offer a quantifiable view users' mental state, serving as possible markers. Central to methodological framework adopted is binary classification system, which classifies texts depressive or non-depressive states, well founded patterns unearthed from data. The proposed model rigorously trains Artificial Intelligence/Machine Learing (AI/ML) models traverse complexities natural language, concentrating noticing delicate indications that signal depression. introduced are tested measured with accuracy, precision, recall, F1-score. RoBERTa, DistilBERT, Electra transformer-based emphasized research. Their performance critically evaluated, results denoting particular capabilities understanding contextualizing key advantage early identification health issues. This stands intersection technology health, revolutionizing monitoring intervention.

Language: Английский

Citations

3
Sentiment analysis applications using deep learning advancements in social networks: A systematic review DOI
Erfan Bakhtiari Ramezani

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129862 - 129862

Published: March 1, 2025

Language: Английский

Citations

0