{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:19:20Z","timestamp":1751606360920,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819967018"},{"type":"electronic","value":"9789819967025"}],"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\/978-981-99-6702-5_18","type":"book-chapter","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T18:02:42Z","timestamp":1700503362000},"page":"219-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Transformer-Based Attention Model for Email Spam Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1153-234X","authenticated-orcid":false,"given":"V. Sri","family":"Vinitha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-4673","authenticated-orcid":false,"given":"D. Karthika","family":"Renuka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5962-2961","authenticated-orcid":false,"given":"L. Ashok","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"issue":"3","key":"18_CR1","first-page":"520","volume":"9","author":"MS Hadeel","year":"2021","unstructured":"Hadeel, M.S.: An Efficient feature selection algorithm for the spam email classification. Period. Eng. Nat. Sci. 9(3), 520\u2013531 (2021)","journal-title":"Period. Eng. Nat. Sci."},{"key":"18_CR2","unstructured":"Thirumagal Dhivya, S., Nithya, S., Sangavi Priya, G., Pugazhendi, E.: Email spam detection and data optimization using NLP techniques. Int. J. Eng. Res. Technol. 10(8), 38\u201349 (2021)"},{"issue":"9","key":"18_CR3","first-page":"409","volume":"3","author":"V Unnikrishnan","year":"2021","unstructured":"Unnikrishnan, V., Kamath, P.: Analysis of email spam detection using machine learning. Int. Res. J. Modern. Eng. Technol. Sci. 3(9), 409\u2013416 (2021)","journal-title":"Int. Res. J. Modern. Eng. Technol. Sci."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Bansal, L., Tiwari, N.: Feature selection based classification of spams using fuzzy support vector machine. In: Proceedings of the 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 258\u2013263 (2020)","DOI":"10.1109\/ICOSEC49089.2020.9215443"},{"key":"18_CR5","unstructured":"Lovelyn Rose, S., Ashok Kumar, L., Karthika Renuka, D.: Deep Learning using Python. Wiley, Hoboken (2019)"},{"issue":"4","key":"18_CR6","first-page":"349","volume":"8","author":"M Sethi","year":"2021","unstructured":"Sethi, M., Chandra, S., Chaudhary, V., Yash, S.: Email Spam detection using machine learning and neural networks. Int. Res. J. Eng. Technol. 8(4), 349\u2013355 (2021)","journal-title":"Int. Res. J. Eng. Technol."},{"issue":"3","key":"18_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s40096-020-00327-8","volume":"14","author":"RT Pashiri","year":"2020","unstructured":"Pashiri, R.T., Rostami, Y., Mahrami, M.: Spam detection through feature selection using artifcial neural network and sine\u2013cosine algorithm. J. Math. Sci. 14(3), 193\u2013199 (2020)","journal-title":"J. Math. Sci."},{"issue":"6","key":"18_CR8","doi-asserted-by":"publisher","first-page":"5721","DOI":"10.1007\/s12652-020-02087-8","volume":"12","author":"S Sumathi","year":"2020","unstructured":"Sumathi, S., Pugalendhi, G.K.: Cognition based spam mail text analysis using combined approach of deep neural network classifer and random forest. J. Ambient Intell. Hum. Comput. 12(6), 5721\u20135731 (2020)","journal-title":"J. Ambient Intell. Hum. Comput."},{"issue":"4","key":"18_CR9","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3844\/jcssp.2020.559.567","volume":"16","author":"I Basyar","year":"2020","unstructured":"Basyar, I., Adiwijaya, S., Murdiansyah, D.T.: Email spam classification using gated recurrent unit and long short-term memory. J. Comput. Sci.Comput. Sci. 16(4), 559\u2013567 (2020)","journal-title":"J. Comput. Sci.Comput. Sci."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Sriram, S., Vinayakumar, R., Sowmya, V., Krichen, M., Noureddine, D.B., Anivilla, S., Soman, K.P.: Deep convolutional neural network based image spam classification. In: Proceedings of the 6th Conference on Data Science and Machine Learning Applications (CDMA), pp. 112\u2013117, Riyadh, Saudi Arabia (2020)","DOI":"10.1109\/CDMA47397.2020.00025"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Jitendra Kumar, A., Santhanavijayan, S., Rajendran, B., Bindhumadhava, B.S.: An adaptive neural network for email spam classification. In: Proceedings of the 2019 Fifteenth International Conference on Information Processing (ICINPRO), pp. 1\u20137, Bengaluru, India (2019)","DOI":"10.1109\/ICInPro47689.2019.9092278"},{"issue":"3","key":"18_CR12","first-page":"396","volume":"11","author":"P Kulkarni","year":"2020","unstructured":"Kulkarni, P., Jatinderkumar, S., Saini, R., Acharya, H.: Effect of header-based features on accuracy of classifiers for spam email classification. Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl. 11(3), 396\u2013401 (2020)","journal-title":"Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Sri Vinitha, V., Karthika Renuka, D.: MapReduce mRMR: random forests-based email spam classification in distributed environment. In: Data Management, Analytics and Innovation, pp. 241\u2013253. Springer, Singapore (2020)","DOI":"10.1007\/978-981-32-9949-8_18"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1016\/j.procs.2021.03.107","volume":"184","author":"I AbdulNabi","year":"2021","unstructured":"AbdulNabi, I., Yaseen, Q.: Spam email detection using deep learning techniques. Proced. Comput. Sci. 184, 853\u2013858 (2021)","journal-title":"Proced. Comput. Sci."},{"issue":"4","key":"18_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.18201\/ijisae.2020466316","volume":"8","author":"S Isik","year":"2020","unstructured":"Isik, S., Kurt, Z., Anagun, Y., Ozkan, K.: Recurrent neural networks for spam e-mail classification on an agglutinative language. Int. J. Intell. Syst. Appl. Eng. 8(4), 221\u2013227 (2020)","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., Tao, H., Feng, S.: Text classification research based on Bert model and Bayesian network. In: Proceedings of the 2019 Chinese Automation Congress (CAC), pp. 5842\u20135846, Hangzhou, China (2019)","DOI":"10.1109\/CAC48633.2019.8996183"},{"issue":"10","key":"18_CR17","first-page":"423","volume":"11","author":"F Lagrari","year":"2020","unstructured":"Lagrari, F., Elkettani, Y.: Customized BERT with convolution model: a new heuristic enabled encoder for Twitter sentiment analysis. Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl. 11(10), 423\u2013431 (2020)","journal-title":"Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl."},{"key":"18_CR18","unstructured":"Si, S., Wang, R., Wosik, J., Zhang, H., Dov, D., Wang, G., Carin, L.: Students need more attention: BERT-based attention model for small data with application to automatic patient message triage. In: Machine Learning for Healthcare Conference; Virtual, pp. 436\u2013456 (2020)"},{"issue":"1","key":"18_CR19","first-page":"93","volume":"3","author":"N Ali","year":"2021","unstructured":"Ali, N., Fatima, A., Shahzadi, H., Ullah, A., Polat, K.: Feature extraction aligned email classification based on imperative sentence selection through deep learning. J. Artif. Intell. Syst. 3(1), 93\u2013114 (2021)","journal-title":"J. Artif. Intell. Syst."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Ablel-Rheem, D.M., Ibrahim, A.O., Kasim, S., Almazroi, A.A., Ismail, M.A.: Hybrid feature selection and ensemble learning method for spam email classification. Int. J. Adv. Trends Comput. Sci. Eng. 9(1), 217\u2013223 (2020)","DOI":"10.30534\/ijatcse\/2020\/3291.42020"},{"issue":"1","key":"18_CR21","first-page":"40","volume":"12","author":"P Bhattacharya","year":"2020","unstructured":"Bhattacharya, P., Singh, A.: E-mail spam filtering using genetic algorithm based on probabilistic weights and words count. Int. J. Integr. Eng. 12(1), 40\u201349 (2020)","journal-title":"Int. J. Integr. Eng."}],"container-title":["Smart Innovation, Systems and Technologies","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6702-5_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:07:41Z","timestamp":1728454061000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6702-5_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819967018","9789819967025"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6702-5_18","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers of Intelligent Computing: Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"11 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ficta.co.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}