المؤلفون: Alexandra König, Johannes Tröger, Elisa Mallick, Mario Mina, Nicklas Linz, Carole Wagnon, Julia Karbach, Caroline Kuhn, Jessica Peter

المصدر: BMC Psychiatry, Vol 22, Iss 1, Pp 1-8 (2022)

مصطلحات موضوعية: Depressive symptoms, Automated speech analysis, Acoustic features, Textual features, Machine learning, Psychiatry, RC435-571

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/1471-244XTest

الوصول الحر: https://doaj.org/article/44177b145fef4cf18722e82b51e0e781Test

المؤلفون: König, Alexandra, Tröger, Johannes, Mallick, Elisa, Mina, Mario, Linz, Nicklas, Wagnon, Carole, Karbach, Julia, Kuhn, Caroline, Peter, Jessica

المساهمون: Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

المصدر: ISSN: 1471-244X ; BMC Psychiatry ; https://hal.science/hal-03968260Test ; BMC Psychiatry, 2022, 22, ⟨10.1186/s12888-022-04475-0⟩.

مصطلحات موضوعية: Depressive symptoms Automated speech analysis Acoustic features Textual features Machine learning, Depressive symptoms, Automated speech analysis, Acoustic features, Textual features, Machine learning, [SCCO]Cognitive science

العلاقة: hal-03968260; https://hal.science/hal-03968260Test; https://hal.science/hal-03968260/documentTest; https://hal.science/hal-03968260/file/Koenig2022-BMC.pdfTest

الإتاحة: https://doi.org/10.1186/s12888-022-04475-0Test
https://hal.science/hal-03968260Test
https://hal.science/hal-03968260/documentTest
https://hal.science/hal-03968260/file/Koenig2022-BMC.pdfTest

المؤلفون: Salah Bouktif, Ali Fiaz, Mamoun Awad

المصدر: IEEE Access, Vol 8, Pp 40269-40282 (2020)

مصطلحات موضوعية: Machine learning, model stacking, sentiment analysis, stock movement direction prediction, textual features extraction, tweets mining, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

العلاقة: https://ieeexplore.ieee.org/document/9016182Test/; https://doaj.org/toc/2169-3536Test

الوصول الحر: https://doaj.org/article/87f86d3f962d457a982098d27cd67f67Test

المؤلفون: Khalilipour, Alireza, Bozyiğit, Fatma, Utku, Can, Challenger, Moharram

المساهمون: Işık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, Işık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Utku, Can

مصطلحات موضوعية: Machine learning, Metamodel, Model driven engineering, Model management, Text mining, Classification (of information), Learning algorithms, Life cycle, Text processing, Machine-learning, Model classification, Model-driven engineering, Structural feature, Structural information, Text-mining, Textual features, Textual information, Software, Software maintenance, Software development process

وصف الملف: application/pdf

العلاقة: Communications in Computer and Information Science; Konferans Öğesi - Uluslararası - İdari Personel ve Öğrenci; WOS; Scopus; Conference Proceedings Citation Index – Science (CPCI-S); Khalilipour, A., Bozyiğit, F., Utku, C. & Challenger, M. (2022). Machine learning-based model categorization using textual and structural features. Paper presented at the Communications in Computer and Information Science, 1652, 425-436. doi:10.1007/978-3-031-15743-1_39; https://hdl.handle.net/11729/5110Test; http://dx.doi.org/10.1007/978-3-031-15743-1_39Test; 1652; 425; 436

الإتاحة: https://doi.org/10.1007/978-3-031-15743-1_39Test
https://hdl.handle.net/11729/5110Test

المؤلفون: Ali Fiaz, Salah Bouktif, Mamoun Awad

المصدر: IEEE Access, Vol 8, Pp 40269-40282 (2020)

مصطلحات موضوعية: General Computer Science, Computer science, Feature selection, 02 engineering and technology, 01 natural sciences, 010305 fluids & plasmas, Machine learning, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Econometrics, General Materials Science, Predictability, Stock market prediction, model stacking, business.industry, Deep learning, Sentiment analysis, General Engineering, textual features extraction, stock movement direction prediction, tweets mining, sentiment analysis, 020201 artificial intelligence & image processing, Stock market, lcsh:Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business, lcsh:TK1-9971

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b2d6dd44ba41645f8a5333e727a8ccaTest
https://doi.org/10.1109/access.2020.2976725Test

المؤلفون: Huang, Qiang

المساهمون: Tiihonen, Topi, Sähkötekniikan korkeakoulu, Zhou, Quan, Aalto-yliopisto, Aalto University

مصطلحات موضوعية: real estate apprasial, machine learning, textual features, visual features

العلاقة: https://aaltodoc.aalto.fi/handle/123456789/39845Test; URN:NBN:fi:aalto-201908254906

الإتاحة: https://aaltodoc.aalto.fi/handle/123456789/39845Test