Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Two-Dimensional Convolutional Neural Network for Depression Episodes Detection in Real Time Using Motor Activity Time Series of Depresjon Dataset
Carlos H. Espino-Salinas, Carlos E. Galván-Tejada, Huizilopoztli Luna-García, et al.
Bioengineering (2022) Vol. 9, Iss. 9, pp. 458-458
Open Access | Times Cited: 12

Showing 12 citing articles:

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Alaa Abd‐Alrazaq, Rawan AlSaad, Farag Shuweihdi, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 43
Depression detection from wearables using machine learning techniques
Rawan AlMakinah, M. Abdullah Canbaz, Abdülhamit Subaşı
Elsevier eBooks (2025), pp. 167-185
Closed Access
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study
Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, et al.
JMIR Aging (2025) Vol. 8, pp. e67715-e67715
Open Access
Convolutional Neural Network for Depression and Schizophrenia Detection
Carlos H. Espino-Salinas, Huizilopoztli Luna-García, Alejandra Cepeda-Argüelles, et al.
Diagnostics (2025) Vol. 15, Iss. 3, pp. 319-319
Open Access
Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions
Crescenzio Gallo
Bioengineering (2023) Vol. 10, Iss. 5, pp. 613-613
Open Access | Times Cited: 5
Performance of Artificial Intelligence in Predicting Future Depression Levels
Sarah Aziz, Rawan AlSaad, Alaa Abd‐Alrazaq, et al.
Studies in health technology and informatics (2023)
Open Access | Times Cited: 4
Predicting Depressive Behavior with Monitoring Activity Data Using Machine Learning and Feature Selection Approaches
Md. Hosain Sarder, M. Raihan, Anjan Debnath, et al.
(2024), pp. 284-289
Closed Access
Bovine colostrum supplementation as a new perspective in depression and substance use disorder treatment: a randomized placebo-controlled study
Krzysztof Durkalec–Michalski, Natalia Główka, Tomasz Podgórski, et al.
Frontiers in Psychiatry (2024) Vol. 15
Open Access
Machine Learning Models to Classify and Predict Depression in College Students
Orlando Iparraguirre-Villanueva, Cleoge Paulino-Moreno, Andrés Epifanía-Huerta, et al.
International Journal of Interactive Mobile Technologies (iJIM) (2024) Vol. 18, Iss. 14, pp. 148-163
Open Access
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study (Preprint)
Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, et al.
(2024)
Closed Access
Feature Selection of Motor Activity in Intervals of Time with Genetics Algorithms for Depression Detection
Carlos H. Espino-Salinas, Carlos E. Galván-Tejada, Ana G. Sánchez-Reyna, et al.
Revista Mexicana de Ingeniería Biomédica (2023) Vol. 44, Iss. 4, pp. 38-52
Open Access | Times Cited: 1
Depresyonda Motor Aktivitenin Makine Öğrenmesi ile Değerlendirilmesi
Selim Aras
Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi (2023)
Open Access
Page 1

Scroll to top