{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T04:45:17Z","timestamp":1761972317234,"version":"build-2065373602"},"reference-count":23,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,11,1]]},"DOI":"10.1587\/transfun.2024eap1025","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T18:06:30Z","timestamp":1747073190000},"page":"1514-1525","source":"Crossref","is-referenced-by-count":0,"title":["Fusing Deep Learning Algorithms to Predict COVID-19 Vaccination Adverse Events of Recovered, Hospitalized or Died"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Sapiah","family":"SAKRI","sequence":"first","affiliation":[{"name":"Princess Nourah bint Abdulrahman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuhaira Muhammad","family":"ZAIN","sequence":"additional","affiliation":[{"name":"Princess Nourah bint Abdulrahman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghada","family":"ALDEHIM","sequence":"additional","affiliation":[{"name":"Princess Nourah bint Abdulrahman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nazik","family":"ALTURKI","sequence":"additional","affiliation":[{"name":"Princess Nourah bint Abdulrahman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadil","family":"SHAIBA","sequence":"additional","affiliation":[{"name":"Princess Nourah bint Abdulrahman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jasni Mohamad","family":"ZAIN","sequence":"additional","affiliation":[{"name":"Universiti Teknologi MARA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azlinah","family":"MOHAMED","sequence":"additional","affiliation":[{"name":"Universiti Teknologi MARA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saiful Farik Mat","family":"YATIN","sequence":"additional","affiliation":[{"name":"Universiti Teknologi MARA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] M.T. Lee, W. Choi, S.H. You, S. Park, J.Y. Kim, D.R. Nam, J.W. Lee, and S.Y. Jung, \u201cSafety profiles of mRNA COVID-19 vaccines using World Health Organization global scale database (VigiBase): A latent class analysis,\u201d Infect. Dis. Ther., vol.12, no.2, pp.443-458, 2023. 10.1007\/s40121-022-00742-5","DOI":"10.1007\/s40121-022-00742-5"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] W. Msemburi, A. Karlinsky, V. Knutson, S. Aleshin-Guendel, S. Chatterji, and J. Wakefield, \u201cThe WHO estimates of excess mortality associated with the COVID-19 pandemic,\u201d Nature, vol.613, no.7942, pp.130-137, 2023. 10.1038\/s41586-022-05522-2","DOI":"10.1038\/s41586-022-05522-2"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] A. Shah, P.W. Marks, and S.M. Hahn, \u201cUnwavering regulatory safeguards for COVID-19 vaccines,\u201d JAMA, vol.32410, pp.931-932, 2020. 10.1001\/jama.2020.15725","DOI":"10.1001\/jama.2020.15725"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] G. Arora, R. Misra, and A. Sajid, \u201cModel systems for pulmonary infectious diseases: Paradigms of anthrax and tuberculosis,\u201d Current Topics in Medicinal Chemistry, vol.17, no.18, pp.2077-2099, 2017. 10.2174\/1568026617666170130111324","DOI":"10.2174\/1568026617666170130111324"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] P. Shah, F. Kendall, S. Khozin, R. Goosen, J. Hu, J. Laramie, M. Ringel, and N. Schork, \u201cArtificial intelligence and machine learning in clinical development: A translational perspective,\u201d npj Digital Medicine, vol.2, p.69, 2019. 10.1038\/s41746-019-0148-3","DOI":"10.1038\/s41746-019-0148-3"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] T. Davenport and R. Kalakota, \u201cThe potential for artificial intelligence in healthcare,\u201d Future Healthcare Journal, vol.6, no.2, pp.94-98, 2019. 10.7861\/futurehosp.6-2-94","DOI":"10.7861\/futurehosp.6-2-94"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] E. Saad, S. Sadiq, R. Jamil, F. Rustam, A. Mehmood, G.S. Choi, and I. Ashraf, \u201cNovel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data,\u201d PLoS ONE, vol.17, no.6, e0270327, 2022. 10.1371\/journal.pone.0270327","DOI":"10.1371\/journal.pone.0270327"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] M.M. Ahamad, S. Aktar, M.J. Uddin, M. Rashed-Al-Mahfuz, A.K.M. Azad, S. Uddin, S.A. Alyami, I.H. Sarker, A. Khan, P. Li\u00f3, J.M.W. Quinn, and M.A. Moni, \u201cAdverse effects of COVID-19 vaccination: Machine learning and statistical approach to identify and classify incidences of morbidity and postvaccination reactogenicity,\u201d Healthcare, vol.11, no.1, p.31, 2022. 10.3390\/healthcare11010031","DOI":"10.3390\/healthcare11010031"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] R. Sujatha, B.V.S. Krishna, J.M. Chatterjee, P.R. Naidu, N. Jhanjhi, C. Charita, E.N. Mariya, and M. Baz, \u201cPrediction of suitable candidates for COVID-19 vaccination,\u201d Intelligent Automation & Soft Computing, vol.32, no.1, pp.525-541, 2022. 10.32604\/iasc.2022.021216","DOI":"10.32604\/iasc.2022.021216"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] H. Gupta and O.P. Verma, \u201cVaccine hesitancy in the post-vaccination COVID-19 era: A machine learning and statistical analysis driven study,\u201d Evol. Intel., vol.16, no.3, pp.739-757, 2023. 10.1007\/s12065-022-00704-3","DOI":"10.1007\/s12065-022-00704-3"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] F. Mukhlif, N. Ithnin, R. Alroobaea, S. Algarni, W.Y. Alghamdi, and I. Hashem, \u201cIntelligence COVID-19 monitoring framework based on deep learning and smart wearable IoT sensors,\u201d Comput. Mater. Contin., vol.77, no.1, pp.583-599, 2023. 10.32604\/cmc.2023.038757","DOI":"10.32604\/cmc.2023.038757"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] H.M. Farghaly, M.M. Gomaa, E. Elgeldawi, H. Askr, Y.A. Elshaier, H.A. Ella, A. Darwish, and A.E. Hassanien, \u201cA deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines,\u201d Sci. Rep., vol.13, no.1, p.9171, 2023. 10.1038\/s41598-023-36319-6","DOI":"10.1038\/s41598-023-36319-6"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] H. Monadhel, A.R. Abbas, and A.J. Mohammed, \u201cCOVID-19 vaccine: Predicting vaccine types and assessing mortality risk through ensemble learning algorithms,\u201d F1000Research, vol.12, 1200, 2023. 10.12688\/f1000research.140395.2","DOI":"10.12688\/f1000research.140395.2"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] O. Daramola, T.D. Kavu, M.J. Kotze, O. Kamati, Z. Emjedi, B. Kabaso, T. Moser, K. Stroetmann, I. Fwemba, F. Daramola, M. Nyirenda, S.J. van Rensburg, P.S. Nyasulu, and J.L. Marnewick, \u201cDetecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning,\u201d Digital Health, vol.9, no.20552076231207593, 2023. 10.1177\/20552076231207593","DOI":"10.1177\/20552076231207593"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] M.B. Albayati and A.M. Altamimi, \u201cAnalyzing COVID-19 vaccine adverse reactions using machine learning techniques,\u201d Karbala International Journal of Modern Science, vol.9, no.2, p.11, 2023. 10.33640\/2405-609x.3271","DOI":"10.33640\/2405-609X.3271"},{"key":"16","unstructured":"[16] VAERS - Data, https:\/\/vaers.hhs.gov\/data.html, Accessed 28 Jun 2023."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] R. Kumar, R. Arora, V. Bansal, V.J. Sahayasheela, H. Buckchash, J. Imran, N. Narayanan, \u201cAccurate prediction of COVID-19 using chest X-ray images through deep feature learning model with SMOTE and machine learning classifiers,\u201d MedRxiv, pp.2020-04, 2020. 10.1101\/2020.04.13.20063461","DOI":"10.1101\/2020.04.13.20063461"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M.S. Sheela and C.A. Arun, \u201cHybrid PSO-SVM algorithm for Covid-19 screening and quantification,\u201d Int. J. Inf. Tecnol., vol.14, pp.2049-2056, 2022 10.1007\/s41870-021-00856-y","DOI":"10.1007\/s41870-021-00856-y"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] A. Rashno, M. Shafipour, and S. Fadaei, \u201cParticle ranking: An efficient method for multi-objective particle swarm optimization feature selection,\u201d Knowledge-Based Systems, vol.245, p.108640, 2020. 10.1016\/j.knosys.2022.108640","DOI":"10.1016\/j.knosys.2022.108640"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.R. M\u00fcller, \u201cEvaluating the visualization of what a deep neural network has learned,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.28, no.11, pp.2660-2673, 2016. 10.1109\/tnnls.2016.2599820","DOI":"10.1109\/TNNLS.2016.2599820"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] S. Li, H.T. Nguyen, and C.C. Cheah, \u201cA theoretical framework for end-to-end learning of deep neural networks with applications to robotics,\u201d IEEE Access, vol.11, pp.21992-22006, 2023. 10.1109\/access.2023.3249280","DOI":"10.1109\/ACCESS.2023.3249280"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] M.F. Aslan, M.F. Andersen, K. Sabanci, and A. Durdu, \u201cCNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection,\u201d Applied Soft Computing, vol.98, p.106912, 2021. 10.1016\/j.asoc.2020.106912","DOI":"10.1016\/j.asoc.2020.106912"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] R. Chandra, A. Jain, and D. Singh Chauhan, \u201cDeep learning via LSTM models for COVID-19 infection forecasting in India,\u201d PLoS ONE, vol.17, no.1, e0262708, 2022. 10.1371\/journal.pone.0262708","DOI":"10.1371\/journal.pone.0262708"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/11\/E108.A_2024EAP1025\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T03:39:20Z","timestamp":1761968360000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/11\/E108.A_2024EAP1025\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":23,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2024eap1025","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"type":"print","value":"0916-8508"},{"type":"electronic","value":"1745-1337"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"article-number":"2024EAP1025"}}