{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:31:54Z","timestamp":1743006714581,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811961410"},{"type":"electronic","value":"9789811961427"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-6142-7_1","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:05:36Z","timestamp":1666281936000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TE-BiLSTM: Improved Transformer and BiLSTM on Fraudulent Phone Text Recognition"],"prefix":"10.1007","author":[{"given":"Hongkui","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangkun","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zifeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Sahin, M., Francillon, A., Gupta, P., Ahamad, M.: Sok: fraud in telephony networks. In: Proceedings of the 2017 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 235\u2013250 (2017)","DOI":"10.1109\/EuroSP.2017.40"},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.13992\/j.cnki.tetas.2017.04.025","volume":"30","author":"Z Wang","year":"2017","unstructured":"Wang, Z., Qu, J.: Research on anti telecommunications fraud technology based on big data. Telecom Eng. Tech. Stand. 30, 86\u201389 (2017). https:\/\/doi.org\/10.13992\/j.cnki.tetas.2017.04.025","journal-title":"Telecom Eng. Tech. Stand."},{"key":"1_CR3","first-page":"13","volume":"27","author":"J Cheng","year":"2020","unstructured":"Cheng, J., Xiao, Y., Fang, Y., Li, S.: Research on telephone fraud prevention architecture based on big data. Telecom World 27, 13\u201315 (2020)","journal-title":"Telecom World"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Xing, J., Yu, M., Wang, S., Zhang, Y., Ding, Y.: Automated fraudulent phone call recognition through deep learning. Wire. Commun. Mob. Comput. (2020)","DOI":"10.1155\/2020\/8853468"},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"140","DOI":"10.23919\/JCC.2020.03.012","volume":"17","author":"S Zhou","year":"2020","unstructured":"Zhou, S., Wang, X., Yang, Z.: Monitoring and early warning of new cyber-telecom crime platform based on BERT migration learning. China Commun. 17, 140\u2013148 (2020)","journal-title":"China Commun."},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Ying, J.J.-C., Zhang, J., Huang, C.-W., Chen, K.-T., Tseng, V.S.: PFrauDetector: a parallelized graph mining approach for efficient fraudulent phone call detection. In: Proceedings of the 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), pp. 1059\u20131066 (2016)","DOI":"10.1109\/ICPADS.2016.0140"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3390\/electronics9030483","volume":"9","author":"NC Dang","year":"2020","unstructured":"Dang, N.C., Moreno-Garc\u00eda, M.N., De la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics 9, 483 (2020)","journal-title":"Electronics"},{"key":"1_CR8","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"44883","DOI":"10.1109\/ACCESS.2019.2909180","volume":"7","author":"MM Tadesse","year":"2019","unstructured":"Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of depression-related posts in reddit social media forum. IEEE Access 7, 44883\u201344893 (2019)","journal-title":"IEEE Access"},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Li, C., Zhan, G., Li, Z.: News text classification based on improved Bi-LSTM-CNN. In: Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 890\u2013893 (2018)","DOI":"10.1109\/ITME.2018.00199"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Nowak, J., Taspinar, A., Scherer, R.: LSTM recurrent neural networks for short text and sentiment classification. In: Proceedings of the International Conference on Artificial Intelligence and Soft Computing, pp. 553\u2013562 (2017)","DOI":"10.1007\/978-3-319-59060-8_50"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Cho, K.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"129626","DOI":"10.1109\/ACCESS.2020.3007889","volume":"8","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Sun, S., Hu, Y., Liu, J., Guo, Y.: Sentiment classification for chinese text based on interactive multitask learning. IEEE Access 8, 129626\u2013129635 (2020)","journal-title":"IEEE Access"},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TVCG.2020.3028976","volume":"27","author":"JF DeRose","year":"2020","unstructured":"DeRose, J.F., Wang, J., Berger, M.: Attention flows: analyzing and comparing attention mechanisms in language models. IEEE Trans. Visual Comput. Graph. 27, 1160\u20131170 (2020)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"1_CR17","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xu, Q.: Short Text classification model based on multi-attention. In: Proceedings of the 2020 13th International Symposium on Computational Intelligence and Design (ISCID), pp. 225\u2013229 (2020)","DOI":"10.1109\/ISCID51228.2020.00057"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/asi4040085","volume":"4","author":"HS Sharaf Al-deen","year":"2021","unstructured":"Sharaf Al-deen, H.S., Zeng, Z., Al-sabri, R., Hekmat, A.: An improved model for analyzing textual sentiment based on a deep neural network using multi-head attention mechanism. Appl. Syst. Innov. 4, 85 (2021)","journal-title":"Appl. Syst. Innov."},{"issue":"8","key":"1_CR20","doi-asserted-by":"publisher","first-page":"12581","DOI":"10.1007\/s11042-020-10336-3","volume":"80","author":"X-L Leng","year":"2021","unstructured":"Leng, X.-L., Miao, X.-A., Liu, T.: Using recurrent neural network structure with enhanced multi-head self-attention for sentiment analysis. Multimedia Tools Appl. 80(8), 12581\u201312600 (2021). https:\/\/doi.org\/10.1007\/s11042-020-10336-3","journal-title":"Multimedia Tools Appl."},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Xu, G., Zhou, D., Liu, J.: Social network spam detection based on ALBERT and combination of Bi-LSTM with self-attention. Secur. Commun. Netw. 2021 (2021)","DOI":"10.1155\/2021\/5567991"},{"key":"1_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"1_CR23","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.3390\/s21041322","volume":"21","author":"W Graterol","year":"2021","unstructured":"Graterol, W., Diaz-Amado, J., Cardinale, Y., Dongo, I., Lopes-Silva, E., Santos-Libarino, C.: Emotion detection for social robots based on NLP transformers and an emotion ontology. Sensors 21, 1322 (2021)","journal-title":"Sensors"},{"key":"1_CR24","doi-asserted-by":"publisher","first-page":"5841","DOI":"10.3390\/app10175841","volume":"10","author":"B Jang","year":"2020","unstructured":"Jang, B., Kim, M., Harerimana, G., Kang, S.-U., Kim, J.W.: Bi-LSTM model to increase accuracy in text classification: combining Word2vec CNN and attention mechanism. Appl. Sci. 10, 5841 (2020)","journal-title":"Appl. Sci."},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18, 602\u2013610 (2005)","journal-title":"Neural Netw."},{"key":"1_CR26","first-page":"85","volume":"26","author":"L Xie","year":"2020","unstructured":"Xie, L.: Research on information investigation of telecom network fraud. J. People\u2019s Pub. Secur. Univ. China (Sci. Technol.) 26, 85\u201393 (2020)","journal-title":"J. People\u2019s Pub. Secur. Univ. China (Sci. Technol.)"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Cho, K., Grishman, R.: Joint event extraction via recurrent neural networks. In: Proceedings of the Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 300\u2013309 (2016)","DOI":"10.18653\/v1\/N16-1034"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pp. 207\u2013212 (2016)","DOI":"10.18653\/v1\/P16-2034"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-6142-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T16:10:27Z","timestamp":1666282227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-6142-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811961410","9789811961427"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-6142-7_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2022\/","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":"205","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":"77","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":"38% - 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":"3.09","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":"3.68","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)"}}]}}