Relevant bibliographies by topics / Automated depression estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Automated depression estimation'

Author: Grafiati
Published: 16 November 2024
Last updated: 31 July 2025

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Journal articles on the topic "Automated depression estimation"

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Mohamed, Islam Ismail, Mohamed Tarek El-Wakad, Khaled Abbas Shafie, Mohamed A. Aboamer, and Nader A. Rahman Mohamed. "Major depressive disorder: early detection using deep learning and pupil diameter." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 916. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp916-932.

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Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was emp
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Islam, Ismail Mohamed Mohamed Tarek El-Wakad Khaled Abbas Shafie Mohamed A. Aboamer Nader A. Rahman Mohamed. "Major depressive disorder: early detection using deep learning and pupil diameter." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 916–32. https://doi.org/10.11591/ijeecs.v35.i2.pp916-932.

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Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was emp
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Bensassi, I., J. Lopez-Castroman, R. Calati, and P. Courtet. "Hippocampal Volume Recovery After Depression: Evidence from an Elderly Sample." European Psychiatry 41, S1 (2017): S170. http://dx.doi.org/10.1016/j.eurpsy.2017.01.2061.

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ObjectivesStructural neuroimaging studies have revealed a consistent pattern of volumetric reductions in both the hippocampus and the anterior cingulate cortex (ACC) of individuals with a major depressive episode (MDE). This study investigated hippocampal and ACC volume differences in the elderly comparing currently depressed individuals and individuals with a past lifetime history of MDE versus healthy controls.MethodsWe studied non-demented individuals from a cohort of community-dwelling people aged 65 and over (ESPRIT study). T1-weighted magnetic resonance images were used to acquire anatom
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KALPANA, V., S. T. HAMDE, and L. M. WAGHMARE. "NON-INVASIVE ESTIMATION OF DIABETES RELATED FEATURES FROM ECG USING GRAPHICAL PROGRAMAMING LANGUAGE AND MATLAB." Journal of Mechanics in Medicine and Biology 12, no. 04 (2012): 1240016. http://dx.doi.org/10.1142/s0219519412400167.

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Electrocardiography deals with the electrical activity of the heart. The condition of cardiac health is given by the electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of biomedical sciences and healthcare. The precision in the identification of various parameters in the ECG is of great importance for the reliability of an automated ECG analyzing system and diagnosis of cardiac diseases. Many algorithms have been developed in the last few years, each with their own advantages and limitations. In this work, we have developed an algorithm for 12-l
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Zhang, Xin, Binayak Ojha, Hermann Bichlmaier, Ingo Hartmann, and Heinz Kohler. "Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation." Sensors 23, no. 10 (2023): 4679. http://dx.doi.org/10.3390/s23104679.

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In view of the tremendous emissions of toxic gases and particulate matter (PM) by low-power firewood-fueled fireplaces, there is an urgent need for effective measures to lower emissions to keep this renewable and economical source for private home heating available in the future. For this purpose, an advanced combustion air control system was developed and tested on a commercial fireplace (HKD7, Bunner GmbH, Eggenfelden, Germany), complemented with a commercial oxidation catalyst (EmTechEngineering GmbH, Leipzig, Germany) placed in the post-combustion zone. Combustion air stream control of the
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Copăcean, Loredana, Luminiţa Cojocariu, M. Simon, I. Zisu, and C. Popescu. "GEOMATIC TECHNIQUES APPLIED FOR REMOTE DETERMINATION OF THE HAY QUANTITY IN AGROSILVOPASTORAL SYSTEMS." Present Environment and Sustainable Development 14, no. 2 (2020): 89–101. http://dx.doi.org/10.15551/pesd2020142006.

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The paper presents a descriptive model, applicable in agricultural theory and practice, for determining the quantity of alfalfa hay obtained from a land surface, using remote investigations, by geospatial methods and means. The working algorithm was tested in a rural area located in the northern part of Romania, in the Humor Depression, and the data acquisition was made with DJI Phantom 4 Pro - Unmanned Aerial Vehicle equipment. For the automated calculation of the amount of alfalfa hay harvested from a certain surface and stored as haystacks, the following steps were carried out: processing t
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An, Yi, Zhen Qu, Ning Xu, and Zhaxi Nima. "Automatic depression estimation using facial appearance." Journal of Image and Graphics 25, no. 11 (2020): 2415–27. http://dx.doi.org/10.11834/jig.200322.

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Sun, Hao, Jiaqing Liu, Shurong Chai, et al. "Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level." Sensors 21, no. 14 (2021): 4764. http://dx.doi.org/10.3390/s21144764.

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Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-mod
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Qin, Jinghui, Changsong Liu, Tianchi Tang, et al. "Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 25029–37. https://doi.org/10.1609/aaai.v39i23.34687.

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Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Early detection and treatment are crucial to mitigate the negative effects these disorders can have on daily life. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. To address this, we introduce the Multi-Modal Psychological assessment corpus (MMPsy), a large-scale dataset containing audio recordings and transcripts from Mandarin-speaking adolescents undergoing automated anxiety/depression assessment intervi
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Kashid, Onkar, Rashmi Bhumbare, Eshwar Dange, Ajit Waghmare, and Raj Nikam. "Depression Monitoring System via Social Media Data using Machine Learning frameworkk." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3431–37. http://dx.doi.org/10.22214/ijraset.2023.51811.

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Abstract: Stress and Depression is one of the most widely recognized and handicapping mental issue that relevantly affects society. Automatic health monitoring systems could be crucial and important to improve depression and stress detection system using social networking. Sentiment Analysis alludes to the utilization of natural language processing and content mining approaches planning to recognize feeling or opinion. Full of feeling Computing is the examination and advancement of frameworks and gadgets that can perceive, decipher, process, and mimic human effects. Sentiment Analysis and deep
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Dissertations / Theses on the topic "Automated depression estimation"

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Agarwal, Navneet. "Autοmated depressiοn level estimatiοn : a study οn discοurse structure, input representatiοn and clinical reliability". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC215.

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Compte tenu de l'impact sévère et généralisé de la dépression, des initiatives de recherche significatives ont été entreprises pour définir des systèmes d'évaluation automatisée de la dépression. La recherche présentée dans cette thèse tourne autour des questions suivantes qui restent relativement inexplorées malgré leur pertinence dans le domaine de l'évaluation automatisée de la dépression : (1) le rôle de la structure du discours dans l'analyse de la santé mentale, (2) la pertinence de la représentation de l'entrée pour les capacités prédictives des modèles de réseaux neuronaux, et (3) l'im
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Conference papers on the topic "Automated depression estimation"

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Wang, Han Yi, Xujin Liu, Pulkit Grover, and Alireza Chamanzar. "A Spatial-Temporal Graph Attention Network for Automated Detection and Width Estimation of Cortical Spreading Depression Using Scalp EEG." In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2023. http://dx.doi.org/10.1109/embc40787.2023.10340281.

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Gabín, Jorge, Anxo Pérez, and Javier Parapar. "Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation." In XoveTIC Conference. MDPI, 2021. http://dx.doi.org/10.3390/engproc2021007023.

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Gabín, Jorge, Anxo Pérez, and Javier Parapar. "Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation." In XoveTIC Conference. MDPI, 2021. http://dx.doi.org/10.3390/engproc2021007023.

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Craiu, Marius, Andreea Craiu, Marmureanu Alexandru, Mihail Diaconescu, and Marius Mihai. "NEAR-REAL TIME SOURCE PARAMETERS ESTIMATION OF THE INTENSE SEISMIC SEQUENCE RECORDED IN 2023 - TG. JIU AREA, ROMANIA." In 23rd SGEM International Multidisciplinary Scientific GeoConference 2023. STEF92 Technology, 2023. http://dx.doi.org/10.5593/sgem2023/1.1/s05.70.

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An intense seismic activity started on February, 2023, close to Targu Jiu city, in Gorj region (Romania), as part of the Getic Depression. The earthquakes were located nearby the contact between Getic Depression and the Carpathians orogen. The seismic sequence consisted of more than 3100 foreshocks with the highest magnitude of Mw=4.9 (occurred on 13 February 2023), respectively Mw= 5.4 (on 14 February). A stable and automatic method, implemented and optimized in the real time data acquisition and processing system (ANTELOPE) to estimate in real time the seismic moment, the moment magnitude an
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Ling, Tianfei, Deyuan Chen, Tingshao Zhu, and Baobin Li. "Fusing Local-Global Facial Features by NFFT for Automatic Depression Estimation." In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023. http://dx.doi.org/10.1109/bibm58861.2023.10385416.

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Smailis, Christos, Nikolaos Sarafianos, Theodoros Giannakopoulos, and Stavros Perantonis. "Fusing active orientation models and mid-term audio features for automatic depression estimation." In PETRA '16: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, 2016. http://dx.doi.org/10.1145/2910674.2935856.

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Wu, Wen, Chao Zhang, and Philip C. Woodland. "Confidence Estimation for Automatic Detection of Depression and Alzheimer’s Disease Based on Clinical Interviews." In Interspeech 2024. ISCA, 2024. http://dx.doi.org/10.21437/interspeech.2024-546.

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