{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:27:28Z","timestamp":1757312848808,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031519390"},{"type":"electronic","value":"9783031519406"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-51940-6_4","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T15:02:22Z","timestamp":1705676542000},"page":"29-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A New Approach for Counting and Identification of Students Sentiments in Online Virtual Environments Using Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5184-0005","authenticated-orcid":false,"given":"Jos\u00e9 Alberto","family":"Hern\u00e1ndez-Aguilar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8842-0899","authenticated-orcid":false,"given":"Yasm\u00edn","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"Lizmary","family":"Rivera Cruz","sequence":"additional","affiliation":[]},{"given":"Juan Carlos","family":"Bonilla Robles","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"4_CR1","unstructured":"Almog, U.: Yolov3 Explained (2020). https:\/\/towardsdatascience.com\/yolo-v3-explained-ff5b850390f. Accessed 14 Oct 2023"},{"key":"4_CR2","unstructured":"Liu, S., Zhao, Y., Xue, F., Chen, B., Chen, X.: DeepCount: Crowd counting with WiFi via deep learning. (2019). arXiv preprint arXiv:1903.05316 . Accessed 15 Oct 2023"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Hardjono, B., Tjahyadi, H., Rhizma, M.G., Widjaja, A.E., Kondorura, R., Halim, A.M.: Vehicle counting quantitative comparison using background subtraction, viola jones and deep learning methods. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 556\u2013562. IEEE (2018)","DOI":"10.1109\/IEMCON.2018.8615085"},{"issue":"2","key":"4_CR4","doi-asserted-by":"publisher","first-page":"416","DOI":"10.3390\/s18020416","volume":"18","author":"D Mehta","year":"2018","unstructured":"Mehta, D., Siddiqui, M.F.H., Javaid, A.Y.: Facial emotion recognition: a survey and real-world user experiences in mixed reality. Sensors 18(2), 416 (2018)","journal-title":"Sensors"},{"key":"4_CR5","unstructured":"DeepFace: Deepface 0.0.79. (2023). https:\/\/pypi.org\/project\/deepface\/. Accessed 16 Oct 2023"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Serengil, S.I., Ozpinar, A.: Lightface: a hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/ASYU50717.2020.9259802"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification, (2014). arXiv preprint arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"key":"4_CR8","volume-title":"Reliable Face Recognition Methods: System Design, Implementation and Evaluation","author":"H Wechsler","year":"2009","unstructured":"Wechsler, H.: Reliable Face Recognition Methods: System Design, Implementation and Evaluation, vol. 7. Springer, Boston (2009)"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Bonilla-Robles, J.C., Hern\u00e1ndez-Aguilar, J.A., Santamar\u00eda-Bonfil, G.: Face Detection with Applications in Education. In: Online Learning Analytics, pp. 213\u2013228. Auerbach Publications (2021)","DOI":"10.1201\/9781003194620-12"},{"issue":"2","key":"4_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1080\/17439884.2020.1686014","volume":"45","author":"M Andrejevic","year":"2020","unstructured":"Andrejevic, M., Selwyn, N.: Facial recognition technology in schools: critical questions and concerns. Learn. Media Technol. 45(2), 115\u2013128 (2020)","journal-title":"Learn. Media Technol."},{"key":"4_CR11","unstructured":"McDaniel, B., D\u2019Mello, S., King, B., Chipman, P., Tapp, K., Graesser, A.: Facial features for affective state detection in learning environments. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 29. no. 9 (2007)"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Bosch, N., et al.: Automatic detection of learning-centered affective states in the wild. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 379\u2013388 (2015)","DOI":"10.1145\/2678025.2701397"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Raveendran, S., Edavoor, P.J., YB, N.K. Vasantha, M.: Design and implementation of reversible logic-based RGB to grayscale color space converter. In: TENCON 2018\u20132018 IEEE Region 10 Conference, IEEE (2018)","DOI":"10.1109\/TENCON.2018.8650243"},{"key":"4_CR14","unstructured":"Google, D.: Classification and recovering. (2023). https:\/\/developers.google.com\/machine-learning\/crash-course\/classification\/precision-and-recall?hl=es-419. Accessed 31 Oct 2023"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"90495","DOI":"10.1109\/ACCESS.2020.2993803","volume":"8","author":"K Patel","year":"2020","unstructured":"Patel, K., et al.: Facial sentiment analysis using AI techniques: state-of-the-art, taxonomies, and challenges. IEEE Access 8, 90495\u201390519 (2020)","journal-title":"IEEE Access"},{"key":"4_CR16","volume-title":"Artificial Intelligence, Mixed Reality, and the Redefinition of the Classroom","author":"SM Martin","year":"2019","unstructured":"Martin, S.M.: Artificial Intelligence, Mixed Reality, and the Redefinition of the Classroom. Rowman & Littlefield, Lanham (2019)"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Ciolacu, M., Tehrani, A.F., Binder, L., Svasta, P.M.: Education 4.0-artificial intelligence assisted higher education: early recognition system with machine learning to support students\u2019 success. In: 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging\u200b(SIITME), pp. 23\u201330. IEEE (2018)","DOI":"10.1109\/SIITME.2018.8599203"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Baecchi, C., Uricchio, T., Bertini, M., Del Bimbo, A.: Deep sentiment features of context and faces for affective video analysis. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 72\u201377, (2017)","DOI":"10.1145\/3078971.3079027"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence. MICAI 2023 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-51940-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T18:02:39Z","timestamp":1711735359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-51940-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031519390","9783031519406"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-51940-6_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yucat\u00e1n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft's CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"115","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}