{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:50:34Z","timestamp":1742914234743,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819972531"},{"type":"electronic","value":"9789819972548"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-7254-8_31","type":"book-chapter","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T05:01:47Z","timestamp":1697864507000},"page":"397-406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Neural Network-Based Approach for\u00a0IoT Service QoS Prediction"],"prefix":"10.1007","author":[{"given":"Christson","family":"Awanyo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nawal","family":"Guermouche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"31_CR1","unstructured":"DeepTSQP: temporal-aware service QoS prediction via deep neural network and feature integration. Knowl.-Based Syst. (2022)"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Al-Ghuribi, S., Noah, S.A.M.: Multi-criteria review-based recommender system - the state of the art. IEEE Access (2019)","DOI":"10.1109\/ACCESS.2019.2954861"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhang, P., Chen, Y., Zhou, M., Al-Turki, Y., Abusorrah, A.: QoS prediction model of cloud services based on deep learning. IEEE\/CAA J. Autom. Sin. (2022)","DOI":"10.1109\/JAS.2021.1004392"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Jin, Y., Guo, W., Zhang, Y.: A time-aware dynamic service quality prediction approach for services. Tsinghua Sci. Technol. (2020)","DOI":"10.26599\/TST.2019.9010007"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Khadir, K., Guermouche, N., Guittoum, A., Monteil, T.: A genetic algorithm-based approach for fluctuating QoS aware selection of IoT services. IEEE Access (2022)","DOI":"10.1109\/ACCESS.2022.3145853"},{"key":"31_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Li, S., Wen, J., Luo, F., Cheng, T., Xiong, Q.: A location and reputation aware matrix factorization approach for personalized quality of service prediction. In: IEEE International Conference on Web Services (ICWS) (2017)","DOI":"10.1109\/ICWS.2017.78"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Lo, W., Yin, J., Deng, S., Li, Y., Wu, Z.: Collaborative web service QoS prediction with location-based regularization (2012)","DOI":"10.1109\/ICWS.2012.49"},{"key":"31_CR11","unstructured":"Luo, X., Zhou, M., Xia, Y., Zhu, Q.: Predicting web service QoS via matrix-factorization-based collaborative filtering under non-negativity constraint. In: 2014 23rd Wireless and Optical Communication Conference (WOCC) (2014)"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 2673\u20132681 (1997)","DOI":"10.1109\/78.650093"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Liang, T., Chen, M., Yin, Y., Zhou, L., Ying, H.: Recurrent neural network based collaborative filtering for QoS prediction in IoV. IEEE Trans. Intell. Transp. Syst. 2400\u20132410 (2022)","DOI":"10.1109\/TITS.2021.3099346"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Cabrera, C., Clarke, S.: Iotpredict: collaborative QoS prediction in IoT. In: 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2018)","DOI":"10.1109\/PERCOM.2018.8444598"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Xu, J., Zheng, Z., Lyu, M.R.: Web service personalized quality of service prediction via reputation-based matrix factorization. IEEE Trans. Reliab. (2016)","DOI":"10.1109\/TR.2015.2464075"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Yan, C., Zhang, Y., Zhong, W., Zhang, C., Xin, B.: A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing. Tsinghua Sci. Technol. (2022)","DOI":"10.26599\/TST.2021.9010040"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Yin, Y., Chen, L., Xu, Y., Wan, J., Zhang, H., Mai, Z.: QoS prediction for service recommendation with deep feature learning in edge computing environment. Mob. Netw. Appl. (2020)","DOI":"10.1007\/s11036-019-01241-7"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, W., Xu, L., Yan, M., Wang, Z., Fu, C.: A probability distribution and location-aware ResNet approach for QoS prediction. CoRR (2020)","DOI":"10.13052\/jwe1540-9589.20415"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QoS of real-world web services. IEEE Trans. Serv. Comput. 32\u201339 (2014)","DOI":"10.1109\/TSC.2012.34"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: a collaborative filtering based web service recommender system. In: IEEE International Conference on Web Services (2009)","DOI":"10.1109\/ICWS.2009.30"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, H., Lyu, M.R., King, I.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE Trans. Serv. Comput. (2013)","DOI":"10.1109\/TSC.2011.59"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7254-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T05:08:25Z","timestamp":1697864905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7254-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819972531","9789819972548"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7254-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"25 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.wise-conferences.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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"137","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":"33","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":"40","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":"24% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}