{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:57:40Z","timestamp":1743026260075,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819982950"},{"type":"electronic","value":"9789819982967"}],"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\/978-981-99-8296-7_29","type":"book-chapter","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:04:14Z","timestamp":1700179454000},"page":"403-417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluate the Efficiency of Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory in Information Technology Job Graph Network"],"prefix":"10.1007","author":[{"given":"Nguyen Minh","family":"Nhut","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dang Minh","family":"Quan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le Mai Duy","family":"Khanh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Dinh","family":"Thuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"29_CR1","unstructured":"Hayes, A.: Blockchain facts: what is it, how it works, and how it can be used (2022). https:\/\/www.investopedia.com\/terms\/b\/blockchain.asp."},{"key":"29_CR2","unstructured":"IBM, What are smart contracts on blockchain? (2022). https:\/\/www.ibm.com\/topics\/smart-contracts"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Thuan, N.D., Nhut, N.M., Quan, D.M.: Using blockchain and artificial intelligence to build a job recommendation system for students in information technology. In: 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE (2022)","DOI":"10.1109\/RIVF55975.2022.10013916"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Lu, J., et al: A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting. In: 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE (2019)","DOI":"10.1109\/PESGM40551.2019.8973549"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Agga, A., et al.: CNN-LSTM: an efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electr. Power Syst. Res. 208, 107908 (2022)","DOI":"10.1016\/j.epsr.2022.107908"},{"issue":"18","key":"29_CR6","doi-asserted-by":"publisher","first-page":"e7015","DOI":"10.1002\/cpe.7015","volume":"34","author":"H Daneshvar","year":"2022","unstructured":"Daneshvar, H., Ravanmehr, R.: A social hybrid recommendation system using LSTM and CNN. Concurr. Comput.: Pract. Exp. 34(18), e7015 (2022)","journal-title":"Concurr. Comput.: Pract. Exp."},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Joshi, S., Jain, T., Nair, N.: Emotion based music recommendation system using LSTM-CNN architecture. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE (2021)","DOI":"10.1109\/ICCCNT51525.2021.9579813"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"75464","DOI":"10.1109\/ACCESS.2019.2919566","volume":"7","author":"J Li","year":"2019","unstructured":"Li, J., Li, X., He, D.: A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction. IEEE Access 7, 75464\u201375475 (2019)","journal-title":"IEEE Access"},{"key":"29_CR9","unstructured":"Nyamathulla, S., et al.: A review on selenium web driver with python (2021)"},{"key":"29_CR10","unstructured":"Gundecha, U.: Learning Selenium Testing Tools with Python: A Practical Guide on Automated Web Testing with Selenium Using Python. Packt Publishing (2014)"},{"issue":"6","key":"29_CR11","first-page":"3389","volume":"28","author":"E Uzun","year":"2020","unstructured":"Uzun, E.: A regular expression generator based on CSS selectors for efficient extraction from HTML pages. Turk. J. Electr. Eng. Comput. Sci. 28(6), 3389\u20133401 (2020)","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"29_CR12","unstructured":"TopDev, TopDev \u2013Top IT Jobs. https:\/\/topdev.vn\/"},{"issue":"9","key":"29_CR13","first-page":"1","volume":"14","author":"W McKinney","year":"2011","unstructured":"McKinney, W.: Pandas: a foundational Python library for data analysis and statistics. Python High Perform. Sci. Comput. 14(9), 1\u20139 (2011)","journal-title":"Python High Perform. Sci. Comput."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Vlasova, A., et al.: Lupa: a framework for large scale analysis of the programming language usage. In: 2022 IEEE\/ACM 19th International Conference on Mining Software Repositories (MSR) (2022)","DOI":"10.1145\/3524842.3528477"},{"key":"29_CR15","unstructured":"Zaveria: Top 10 programming languages in 2023 with the largest developer communities (2023)"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT) (2018)","DOI":"10.23919\/ICACT.2018.8323688"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, P., et al.: Beyond one-hot encoding: lower dimensional target embedding (2018)","DOI":"10.1016\/j.imavis.2018.04.004"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Al-Shehari, T., Alsowail, R.A.: An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques (2021)","DOI":"10.3390\/e23101258"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Albawi, A., et al.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET) (2017)","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"29_CR20","unstructured":"Saxena, S.: Learn about long short-term memory (LSTM) algorithms (2021)"},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D 404, 132306 (2020)","journal-title":"Phys. D"},{"issue":"8","key":"29_CR22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"J Schmidhuber","year":"1997","unstructured":"Schmidhuber, J., Hochreiter, S.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"11","key":"29_CR23","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"29_CR24","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/make1030048","volume":"1","author":"M Rhanoui","year":"2019","unstructured":"Rhanoui, M., et al.: A CNN-BiLSTM model for document-level sentiment analysis. Mach. Learn. Knowl. Extract. 1(3), 832\u2013847 (2019)","journal-title":"Mach. Learn. Knowl. Extract."},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)","DOI":"10.3115\/v1\/P15-1150"},{"issue":"24","key":"29_CR26","doi-asserted-by":"publisher","first-page":"9920","DOI":"10.3390\/s22249920","volume":"22","author":"L Parida","year":"2022","unstructured":"Parida, L., et al.: A novel CNN-LSTM hybrid model for prediction of electro-mechanical impedance signal based bond strength monitoring. Sensors 22(24), 9920 (2022)","journal-title":"Sensors"},{"key":"29_CR27","doi-asserted-by":"publisher","first-page":"180544","DOI":"10.1109\/ACCESS.2020.3028281","volume":"8","author":"M Alhussein","year":"2020","unstructured":"Alhussein, M., Aurangzeb, K., Haider, S.I.: Hybrid CNN-LSTM model for short-term individual household load forecasting. IEEE Access 8, 180544\u2013180557 (2020)","journal-title":"IEEE Access"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Filip\u010di\u0107, S.: Web3 & DAOs: an overview of the development and possibilities for the implementation in research and education (2022)","DOI":"10.23919\/MIPRO55190.2022.9803324"},{"key":"29_CR29","unstructured":"Garreta, R., Moncecchi, G.: Learning Scikit-Learn: Machine Learning in Python. Packt Publishing Ltd. (2013)"},{"key":"29_CR30","unstructured":"B. T, Comprehensive Guide on Multiclass Classification Metrics, Medium (2023). https:\/\/towardsdatascience.com\/comprehensive-guide-on-multiclass-classification-metrics-af94cfb83fbd"},{"key":"29_CR31","doi-asserted-by":"publisher","unstructured":"Grandini, M., Bagli, E., Visani, G.: Metrics for multi-class classification: an overview. arXiv (2020). https:\/\/doi.org\/10.48550\/arXiv.2008.05756","DOI":"10.48550\/arXiv.2008.05756"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8296-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:12:09Z","timestamp":1700179929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8296-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819982950","9789819982967"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8296-7_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Da Nang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"38","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":"8","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":"28% - 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":"6","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)"}}]}}