{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:25:23Z","timestamp":1774535123854,"version":"3.50.1"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17890-6","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T06:02:27Z","timestamp":1702965747000},"page":"57317-57345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview"],"prefix":"10.1007","volume":"83","author":[{"given":"Neha","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Neeru","family":"Jindal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"17890_CR1","doi-asserted-by":"publisher","unstructured":"Goyal D, Goyal R, Rekha G, Malik S, & Tyagi AK (2020) Emerging trends and challenges in data science and big data analytics. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, 1\u20138. https:\/\/doi.org\/10.1109\/ic-ETITE47903.2020.316","DOI":"10.1109\/ic-ETITE47903.2020.316"},{"issue":"13","key":"17890_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17577\/IJERTCONV6IS13220","volume":"NCESC \u2013 6","author":"R Apoorva","year":"2018","unstructured":"Apoorva R, Arasa D, Jamadade S (2018) A survey on artificial intelligence. Int J Eng Res Technol (IJERT) NCESC \u2013 6(13):1\u20136. https:\/\/doi.org\/10.17577\/IJERTCONV6IS13220. (ISSN:2278-0181)","journal-title":"Int J Eng Res Technol (IJERT)"},{"issue":"1","key":"17890_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00445-7","volume":"8","author":"FA Batarseh","year":"2021","unstructured":"Batarseh FA, Freeman L, Huang CH (2021) A survey on artificial intelligence assurance. J Big Data 8(1):1\u201330. https:\/\/doi.org\/10.1186\/s40537-021-00445-7","journal-title":"J Big Data"},{"key":"17890_CR4","doi-asserted-by":"publisher","first-page":"729","DOI":"10.48550\/arXiv.1705.08807","volume":"62","author":"K Grace","year":"2018","unstructured":"Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2018) When will AI exceed human performance? Evidence from AI experts. J Artif Intell Res 62:729\u2013754. https:\/\/doi.org\/10.48550\/arXiv.1705.08807","journal-title":"J Artif Intell Res"},{"key":"17890_CR5","doi-asserted-by":"publisher","unstructured":"Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, ..., Zhang J (2021) Artificial intelligence: a powerful paradigm for scientific research. Innovation 2(4): 100179. https:\/\/doi.org\/10.1016\/j.xinn.2021.100179","DOI":"10.1016\/j.xinn.2021.100179"},{"issue":"1","key":"17890_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s43926-020-00001-4","volume":"1","author":"M Kuzlu","year":"2021","unstructured":"Kuzlu M, Fair C, Guler O (2021) Role of artificial intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet Things 1(1):1\u201314. https:\/\/doi.org\/10.1007\/s43926-020-00001-4","journal-title":"Discover Internet Things"},{"issue":"3","key":"17890_CR7","doi-asserted-by":"publisher","first-page":"676","DOI":"10.3390\/s21030676","volume":"21","author":"A Zgank","year":"2021","unstructured":"Zgank A (2021) IoT-based bee swarm activity acoustic classification using deep neural networks. Sensors 21(3):676. https:\/\/doi.org\/10.3390\/s21030676","journal-title":"Sensors"},{"key":"17890_CR8","doi-asserted-by":"publisher","unstructured":"Churcher A, Ullah R, Ahmad J, Ur Rehman, S, Masood, F, Gogate, M, ..., Buchanan, WJ (2021) An experimental analysis of attack classification using machine learning in IoT networks. Sensors 21(2): 446. https:\/\/doi.org\/10.3390\/s21020446","DOI":"10.3390\/s21020446"},{"key":"17890_CR9","unstructured":"Sriram GK (2022) The evolution of AI cloud computing and the future it holds. Int Res J Modern Eng Technol Sci 4(2):776\u2013787. e-ISSN: 2582\u20135208. https:\/\/www.researchgate.net\/publication\/358633514_THE_EVOLUTION_OF_AI_CLOUD_COMPUTING_AND_THE_FUTURE_IT_HOLDS"},{"key":"17890_CR10","unstructured":"Kumari S, Abhishek R, Panda BS (2013) Intelligent computing relating to cloud computing.\u00a0Int J Mech Eng Comput Appl (IJMCA)\u00a01(1): 5\u20138. https:\/\/www.researchgate.net\/publication\/266023699_Intelligent_Computing_Relating_to_Cloud_Computing"},{"key":"17890_CR11","unstructured":"Varzeghani HN, Samadyar Z (2014) Intelligent agents: a comprehensive survey. Inte J Electron Commun Comput Eng 5(4):790\u2013798. ISSN 2249\u2013071X. https:\/\/www.researchgate.net\/publication\/264436271_Intelligent_Agents_A_Comprehensive_Survey"},{"key":"17890_CR12","doi-asserted-by":"publisher","unstructured":"Adetiba E, John T, Akinrinmade A, Moninuola F, Akintade O, Badejo J (2021) Evolution of artificial intelligence languages, a systematic literature review. arXiv:2101.11501. https:\/\/doi.org\/10.48550\/arXiv.2101.11501","DOI":"10.48550\/arXiv.2101.11501"},{"issue":"3","key":"17890_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1145\/356631.356632","volume":"6","author":"DG Bobrow","year":"1974","unstructured":"Bobrow DG, Raphael B (1974) New programming languages for artificial intelligence research. ACM Comput Surv (CSUR) 6(3):153\u2013174. https:\/\/doi.org\/10.1145\/356631.356632","journal-title":"ACM Comput Surv (CSUR)"},{"key":"17890_CR14","doi-asserted-by":"publisher","unstructured":"Zhang Z, Liu Y, Han C, Guo T, Yao T, Mei T (2022) Generalized one-shot domain adaption of generative adversarial networks. arXiv:2209.03665. https:\/\/doi.org\/10.48550\/arXiv.2209.03665","DOI":"10.48550\/arXiv.2209.03665"},{"key":"17890_CR15","unstructured":"Radford A, Kim JW, Xu T, Brockman G, McLeavey C, Sutskever I (2022) Robust speech recognition via large-scale weak supervision. Technical report. OpenAI. https:\/\/cdn.openai.com\/papers\/whisper.pdf"},{"key":"17890_CR16","doi-asserted-by":"publisher","unstructured":"Glaese A, McAleese N, Tr\u0119bacz M, Aslanides J, Firoiu V, Ewalds T, ..., Irving G (2022) Improving alignment of dialogue agents via targeted human judgments. arXiv:2209.14375. https:\/\/doi.org\/10.48550\/arXiv.2209.14375","DOI":"10.48550\/arXiv.2209.14375"},{"key":"17890_CR17","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1038\/s42256-022-00519-y","volume":"4","author":"S Choudhury","year":"2022","unstructured":"Choudhury S, Moret M, Salvy P, Weilandt D, Hatzimanikatis V, Miskovic L (2022) Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks. Nat Mach Intell 4:710\u2013719. https:\/\/doi.org\/10.1038\/s42256-022-00519-y","journal-title":"Nat Mach Intell"},{"key":"17890_CR18","doi-asserted-by":"publisher","first-page":"3876","DOI":"10.1038\/s41467-022-31245-z","volume":"13","author":"A Pandi","year":"2022","unstructured":"Pandi A et al (2022) A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 13:3876. https:\/\/doi.org\/10.1038\/s41467-022-31245-z","journal-title":"Nat Commun"},{"issue":"33","key":"17890_CR19","doi-asserted-by":"publisher","first-page":"e2201776119","DOI":"10.1073\/pnas.2201776119","volume":"119","author":"MM Hausladen","year":"2022","unstructured":"Hausladen MM, Zhao B, Kubala MS, Francis LF, Kowalewski TM, Ellison CJ (2022) Synthetic growth by self-lubricated photopolymerization and extrusion inspired by plants and fungi. Proc Natl Acad Sci 119(33):e2201776119. https:\/\/doi.org\/10.1073\/pnas.2201776119","journal-title":"Proc Natl Acad Sci"},{"key":"17890_CR20","doi-asserted-by":"publisher","unstructured":"Singer U, Polyak A, Hayes T, Yin X, An J, Zhang S, ..., Taigman Y (2022) Make-a-video: text-to-video generation without text-video data. arXiv: 2209:14792. https:\/\/doi.org\/10.48550\/arXiv.2209.14792","DOI":"10.48550\/arXiv.2209.14792"},{"key":"17890_CR21","doi-asserted-by":"publisher","unstructured":"Thambawita V, Isaksen JL, Hicks SA, Ghouse J, Ahlberg G, Linneberg A, ..., Kanters JK (2021) DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine. Sci Rep 11(1): 1\u20138. https:\/\/doi.org\/10.1038\/s41598-021-01295-2","DOI":"10.1038\/s41598-021-01295-2"},{"key":"17890_CR22","doi-asserted-by":"publisher","unstructured":"Wang X, Li Y, Zhang H, Shan Y (2021) Towards real-world blind face restoration with generative facial prior. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition:9168\u20139178. https:\/\/doi.org\/10.48550\/arXiv.2101.04061","DOI":"10.48550\/arXiv.2101.04061"},{"key":"17890_CR23","doi-asserted-by":"publisher","unstructured":"Maharana A, Hannan D, Bansal M (2022) StoryDALL-E: adapting pretrained text-to-image transformers for story continuation. arXiv:2209:06192. https:\/\/doi.org\/10.48550\/arXiv.2209.06192","DOI":"10.48550\/arXiv.2209.06192"},{"key":"17890_CR24","doi-asserted-by":"publisher","unstructured":"Abbasi NI, Spitale M, Anderson J, Ford T, Jones PB, Gunes H (2022) Can robots help in the evaluation of mental well-being in children? An empirical study. In: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) IEEE, 1459\u20131466. https:\/\/doi.org\/10.1109\/RO-MAN53752.2022.9900843","DOI":"10.1109\/RO-MAN53752.2022.9900843"},{"issue":"1","key":"17890_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-32012-w","volume":"13","author":"K Ellis","year":"2022","unstructured":"Ellis K, Albright A, Solar-Lezama A, Tenenbaum JB, O\u2019Donnell TJ (2022) Synthesizing theories of human language with Bayesian program induction. Nat Commun 13(1):1\u201313. https:\/\/doi.org\/10.1038\/s41467-022-32012-w","journal-title":"Nat Commun"},{"key":"17890_CR26","doi-asserted-by":"publisher","unstructured":"Kim SW, Zhou Y, Philion J, Torralba A, Fidler S (2020) Learning to simulate dynamic environments with GameGAN. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition:1231\u20131240. https:\/\/doi.org\/10.48550\/arXiv.2005.12126","DOI":"10.48550\/arXiv.2005.12126"},{"issue":"4","key":"17890_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/0008125619864925","volume":"61","author":"M Haenlein","year":"2019","unstructured":"Haenlein M, Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev 61(4):5\u201314. https:\/\/doi.org\/10.1177\/0008125619864925","journal-title":"Calif Manage Rev"},{"key":"17890_CR28","unstructured":"Mijwil MM (2015) History of artificial intelligence: 1\u20135. https:\/\/www.ijcai.org\/Proceedings\/77-2\/Papers\/083.pdf"},{"key":"17890_CR29","unstructured":"Kitchenham B (2004) Procedures for performing systematic reviews. Keele Univ, Keele, 1\u201326. https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=29890a936639862f45cb9a987dd599dce9759bf5"},{"key":"17890_CR30","doi-asserted-by":"crossref","unstructured":"Okoli C (2015) A guide to conducting a standalone systematic literature review. Commun Assoc Inf Syst 37: 43\u201352. https:\/\/hal.science\/hal-01574600\/","DOI":"10.17705\/1CAIS.03743"},{"key":"17890_CR31","unstructured":"Vom Brocke J, Simons A, Niehaves B, Riemer K, Plattfaut R, & Cleven A (2009) Reconstructing the giant: on the importance of rigour in documenting the literature search process. ECIS. Verona: 17th European Conference on Information Systems (ECIS). https:\/\/aisel.aisnet.org\/ecis2009\/161"},{"key":"17890_CR32","doi-asserted-by":"publisher","unstructured":"Kitchenham BA (2012) Systematic review in software engineering: where we are and where we should be going. In: Proceedings of the 2nd international Workshop on Evidential assessment of software technologies. pp 1\u20132. https:\/\/doi.org\/10.1145\/2372233.2372235","DOI":"10.1145\/2372233.2372235"},{"key":"17890_CR33","doi-asserted-by":"crossref","unstructured":"Leidner D, Kayworth T (2006) A review of culture in information systems research: toward a theory of information technology culture conflict. MIS Q 30(2): 357\u2013399. https:\/\/www.jstor.org\/stable\/25148735","DOI":"10.2307\/25148735"},{"key":"17890_CR34","doi-asserted-by":"publisher","unstructured":"Dyb\u00e5 T, Dings\u00f8yr T (2008) Strength of evidence in systematic reviews in software engineering. In: Proceedings of the Second ACM-IEEE international symposium on empirical software engineering and measurement. 178\u2013187. https:\/\/doi.org\/10.1145\/1414004.1414034","DOI":"10.1145\/1414004.1414034"},{"key":"17890_CR35","doi-asserted-by":"publisher","unstructured":"Levy Y, Ellis TJ (2006) A systems approach to conduct an effective literature review in support of information systems research. Inform Sci .9:181\u2013212. https:\/\/doi.org\/10.28945\/479","DOI":"10.28945\/479"},{"issue":"2","key":"17890_CR36","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1002\/jrsm.1378","volume":"11","author":"M Gusenbauer","year":"2020","unstructured":"Gusenbauer M, Haddaway NR (2020) Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar. PubMed and 26 other resources. Res Synth Methods 11(2):181\u2013217. https:\/\/doi.org\/10.1002\/jrsm.1378","journal-title":"Res Synth Methods"},{"key":"17890_CR37","doi-asserted-by":"publisher","unstructured":"Lopez-Cozar ED, Orduna-Malea E, Mart\u00edn-Mart\u00edn A (2019) Google Scholar as a data source for research assessment. Springer handbook of science and technology indicators. Springer: 95\u2013127.  https:\/\/doi.org\/10.48550\/arXiv.1806.04435","DOI":"10.48550\/arXiv.1806.04435"},{"key":"17890_CR38","doi-asserted-by":"publisher","first-page":"100517","DOI":"10.1016\/j.cosrev.2022.100517","volume":"47","author":"P Ciancarini","year":"2023","unstructured":"Ciancarini P, Farina M, Okonicha O, Smirnova M, Succi G (2023) Software as storytelling: a systematic literature review. Comput Sci Rev 47:100517. https:\/\/doi.org\/10.1016\/j.cosrev.2022.100517","journal-title":"Comput Sci Rev"},{"key":"17890_CR39","doi-asserted-by":"publisher","unstructured":"Paul J, Khatri P, Kaur Duggal H (2023) Frameworks for developing impactful systematic literature reviews and theory building: what, why and how? J Decis Syst:1\u201314. https:\/\/doi.org\/10.1080\/12460125.2023.2197700","DOI":"10.1080\/12460125.2023.2197700"},{"issue":"1","key":"17890_CR40","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s11192-020-03690-4","volume":"126","author":"A Mart\u00edn-Mart\u00edn","year":"2021","unstructured":"Mart\u00edn-Mart\u00edn A, Thelwall M, Orduna-Malea E, Delgado L\u00f3pez-C\u00f3zar E (2021) Google scholar, microsoft academic, scopus, dimensions, web of science, and OpenCitations\u2019 COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1):871\u2013906. https:\/\/doi.org\/10.1007\/s11192-020-03690-4","journal-title":"Scientometrics"},{"issue":"1","key":"17890_CR41","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1162\/qss_a_00231","volume":"4","author":"AP Fabiana","year":"2023","unstructured":"Fabiana AP, Rog\u00e9rio M (2023) Mapping the use of google scholar in evaluative bibliometric or scientometric studies: a bibliometric review. Quant Sci Stud 4(1):233\u2013245. https:\/\/doi.org\/10.1162\/qss_a_00231","journal-title":"Quant Sci Stud"},{"issue":"4","key":"17890_CR42","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/TVT.2019.2897134","volume":"68","author":"H Ye","year":"2019","unstructured":"Ye H, Li GY, Juang BHF (2019) Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans Veh Technol 68(4):3163\u20133173. https:\/\/doi.org\/10.1109\/TVT.2019.2897134","journal-title":"IEEE Trans Veh Technol"},{"key":"17890_CR43","unstructured":"Szczepanski M (2020) Is data the new oil? Competition issues in the digital economy. https:\/\/www.europarl.europa.eu\/thinktank\/en\/document\/EPRS_BRI(2020)646117"},{"key":"17890_CR44","doi-asserted-by":"publisher","unstructured":"Pham QV, Pham XQ, Nguyen TT, Han Z, Kim DS (2022) Artificial intelligence for the metaverse: a survey. https:\/\/doi.org\/10.48550\/arXiv.2202.10336","DOI":"10.48550\/arXiv.2202.10336"},{"key":"17890_CR45","unstructured":"Ishaq Azhar Mohammed (2020) Artificial intelligence for cybersecurity: a systematic mapping of literature. Int J Innov Eng Res Technol (IJIERT) 7(9):172\u2013176. ISSN: 2394- 3696. https:\/\/www.researchgate.net\/publication\/353887583_ARTIFICIAL_INTELLIGENCE_FOR_CYBERSECURITY_A_SYSTEMATIC_MAPPING_OF_LITERATURE"},{"key":"17890_CR46","unstructured":"Brundage M, Avin S, Clark J, Toner H, Eckersley P, Garfinkel B, Dafoe A, Scharre P, Zeitzoff T, Filar B, Anderson H (2018) The malicious use of artificial intelligence: forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228. 2018 Feb 20. https:\/\/arxiv.org\/ftp\/arxiv\/papers\/1802\/1802.07228.pdf"},{"key":"17890_CR47","unstructured":"Economist (2018) The challenger: technoplotics. https:\/\/www.economist.com\/briefing\/2018\/03\/15\/the-challenger"},{"key":"17890_CR48","doi-asserted-by":"publisher","unstructured":"Lin J, Zhu L, Chen WM, Wang WC, Gan C, Han S (2022) On-device training under 256KB memory. arXiv: 2206:15472. https:\/\/doi.org\/10.48550\/arXiv.2206.15472","DOI":"10.48550\/arXiv.2206.15472"},{"issue":"2","key":"17890_CR49","doi-asserted-by":"publisher","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6(2):94. https:\/\/doi.org\/10.7861\/futurehosp.6-2-94","journal-title":"Future Healthc J"},{"key":"17890_CR50","doi-asserted-by":"publisher","unstructured":"Wani SUD, Khan NA, Thakur G, Gautam SP, Ali M, Alam P, ..., Shakeel F (2022) Utilization of artificial intelligence in disease prevention: diagnosis, treatment, and implications for the healthcare workforce. Healthcare 10(4): 608. https:\/\/doi.org\/10.3390\/healthcare10040608. (MDPI)","DOI":"10.3390\/healthcare10040608"},{"key":"17890_CR51","unstructured":"Utermohlen K (2018) Four robotic process automation (RPA) applications in the healthcare industry. Medium. https:\/\/medium.com\/@karl.utermohlen\/4-robotic-process-automation-rpa-applications-in-the-healthcare-industry-4d449b24b613"},{"key":"17890_CR52","doi-asserted-by":"publisher","first-page":"104593","DOI":"10.1016\/j.compedu.2022.104593","volume":"189","author":"H Huang","year":"2022","unstructured":"Huang H, Hwang GJ, Jong MSY (2022) Technological solutions for promoting employees\u2019 knowledge levels and practical skills: an SVVR-based blended learning approach for professional training. Comput Educ 189:104593. https:\/\/doi.org\/10.1016\/j.compedu.2022.104593. (ISSN:0360-1315)","journal-title":"Comput Educ"},{"issue":"1","key":"17890_CR53","doi-asserted-by":"publisher","first-page":"20","DOI":"10.33897\/fujeas.v2i1.380","volume":"2","author":"A Ali","year":"2021","unstructured":"Ali A (2021) Artificial intelligence potential trends in military. Foundation Univ J Eng Appl Sci 2(1):20\u201330. https:\/\/doi.org\/10.33897\/fujeas.v2i1.380. (HEC Recognized Y Category ISSN 2706-7351)","journal-title":"Foundation Univ J Eng Appl Sci"},{"issue":"3","key":"17890_CR54","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1108\/TR-03-2017-0049","volume":"74","author":"J Beck","year":"2019","unstructured":"Beck J, Rainoldi M, Egger R (2019) Virtual reality in tourism: a state-of-the-art review. Tour Rev 74(3):586\u2013612. https:\/\/doi.org\/10.1108\/TR-03-2017-0049","journal-title":"Tour Rev"},{"issue":"2","key":"17890_CR55","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/JAS.2020.1003021","volume":"7","author":"Y Ma","year":"2020","unstructured":"Ma Y, Wang Z, Yang H, Yang L (2020) Artificial intelligence applications in the development of autonomous vehicles: a survey. IEEE\/CAA J Autom Sin 7(2):315\u2013329. https:\/\/doi.org\/10.1109\/JAS.2020.1003021","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"14","key":"17890_CR56","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1080\/1369118X.2020.1754877","volume":"23","author":"K De Vries","year":"2020","unstructured":"De Vries K (2020) You never fake alone. Creative AI in action. Inf Commun Soc 23(14):2110\u20132127. https:\/\/doi.org\/10.1080\/1369118X.2020.1754877","journal-title":"Inf Commun Soc"},{"key":"17890_CR57","unstructured":"Davenport TH, Glaser J (2002) Just-in-time delivery comes to knowledge management. Harvard Business Review. https:\/\/hbr.org\/2002\/07\/just-in-time-delivery-comes-to-knowledge-management"},{"key":"17890_CR58","doi-asserted-by":"publisher","first-page":"104927","DOI":"10.48550\/arXiv.1905.12502","volume":"186","author":"H Hayashi","year":"2019","unstructured":"Hayashi H, Abe K, Uchida S (2019) GlyphGAN: style-consistent font generation based on generative adversarial networks. Knowl-Based Syst 186:104927. https:\/\/doi.org\/10.48550\/arXiv.1905.12502","journal-title":"Knowl-Based Syst"},{"key":"17890_CR59","doi-asserted-by":"publisher","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 8110\u20138119. https:\/\/doi.org\/10.48550\/arXiv.1912.04958","DOI":"10.48550\/arXiv.1912.04958"},{"key":"17890_CR60","unstructured":"Joji LE, Kanjirappally K, Joseph G (2022) Grey coloured anime character. In: Proceedings of the National Conference on Emerging Computer Applications (NCECA). 4(1):89. https:\/\/nceca.in\/2022\/21_Grey_Coloured_Anime_Character.pdf"},{"key":"17890_CR61","doi-asserted-by":"publisher","unstructured":"Balasubramanian S, Balasubramanian VN (2019) Teaching gans to sketch in vector format. https:\/\/doi.org\/10.48550\/arXiv.1904.03620","DOI":"10.48550\/arXiv.1904.03620"},{"issue":"9","key":"17890_CR62","doi-asserted-by":"publisher","first-page":"101515","DOI":"10.1016\/j.isci.2020.101515","volume":"23","author":"Z Epstein","year":"2020","unstructured":"Epstein Z, Levine S, Rand DG, Rahwan I (2020) Who gets credit for ai-generated art? Iscience 23(9):101515. https:\/\/doi.org\/10.1016\/j.isci.2020.101515","journal-title":"Iscience"},{"key":"17890_CR63","doi-asserted-by":"publisher","unstructured":"Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical text-conditional image generation with clip latents. arXiv:2204:06125. https:\/\/doi.org\/10.48550\/arXiv.2204.06125","DOI":"10.48550\/arXiv.2204.06125"},{"issue":"19","key":"17890_CR64","doi-asserted-by":"publisher","first-page":"6791","DOI":"10.3390\/app10196791","volume":"10","author":"J Yu","year":"2020","unstructured":"Yu J, Park S, Kwon SH, Ho CMB, Pyo CS, Lee H (2020) AI-based stroke disease prediction system using real-time electromyography signals. Appl Sci 10(19):6791. https:\/\/doi.org\/10.3390\/app10196791","journal-title":"Appl Sci"},{"issue":"1","key":"17890_CR65","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1109\/TMI.2020.3029205","volume":"40","author":"H Qi","year":"2021","unstructured":"Qi H, Fuin N, Cruz G, Pan J, Kuestner T, Bustin A, Botnar RM, Prieto C (2021) Non-rigid respiratory motion estimation of wholeheart coronary MR images using unsupervised deep learning. IEEE Trans Med Imaging 40(1):444\u2013454. https:\/\/doi.org\/10.1109\/TMI.2020.3029205","journal-title":"IEEE Trans Med Imaging"},{"key":"17890_CR66","doi-asserted-by":"publisher","unstructured":"Wu P, Ding W, You Z, An P (2019) Virtual reality video quality assessment based on 3D convolutional neural networks. In: Proc. IEEE International Conference on Image Processing (ICIP) Taipei, Taiwan, 3187\u20133191. https:\/\/doi.org\/10.1109\/ICIP.2019.8803023","DOI":"10.1109\/ICIP.2019.8803023"},{"issue":"3","key":"17890_CR67","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MCI.2019.2919363","volume":"14","author":"NA Barriga","year":"2019","unstructured":"Barriga NA, Stanescu M, Besoain F, Buro M (2019) Improving RTS game AI by supervised policy learning, tactical search, and deep reinforcement learning. IEEE Comput Intell Mag 14(3):8\u201318. https:\/\/doi.org\/10.1109\/MCI.2019.2919363","journal-title":"IEEE Comput Intell Mag"},{"issue":"6","key":"17890_CR68","doi-asserted-by":"publisher","first-page":"6073","DOI":"10.1109\/TVT.2021.3076780","volume":"70","author":"H Liu","year":"2021","unstructured":"Liu H, Zhang S, Zhang P, Zhou X, Shao X, Pu G, Zhang Y (2021) Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans Veh Technol 70(6):6073\u20136084. https:\/\/doi.org\/10.1109\/TVT.2021.3076780","journal-title":"IEEE Trans Veh Technol"},{"key":"17890_CR69","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/ACCESS.2019.2961372","volume":"8","author":"S Tanwar","year":"2019","unstructured":"Tanwar S, Bhatia Q, Patel P, Kumari A, Singh PK, Hong WC (2019) Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8:474\u2013488. https:\/\/doi.org\/10.1109\/ACCESS.2019.2961372","journal-title":"IEEE Access"},{"issue":"10","key":"17890_CR70","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1109\/TITS.2019.2906365","volume":"20","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial\u2013temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913\u20133926. https:\/\/doi.org\/10.1109\/TITS.2019.2906365","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"17890_CR71","doi-asserted-by":"publisher","unstructured":"Park S, Cha HS, Kwon J, Kim H, Im CH (2020) Development of an online home appliance control system using augmented reality and an ssvep-based brain-computer interface. In: 2020 8th International Winter Conference on Brain-Computer Interface (BCI) IEEE, 1\u20132 https:\/\/doi.org\/10.1109\/ACCESS.2019.2952613","DOI":"10.1109\/ACCESS.2019.2952613"},{"issue":"5","key":"17890_CR72","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TNSRE.2020.2981659","volume":"28","author":"JH Jeong","year":"2020","unstructured":"Jeong JH, Shim KH, Kim DJ, Lee SW (2020) Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans Neural Syst Rehabil Eng 28(5):1226\u20131238. https:\/\/doi.org\/10.1109\/TNSRE.2020.2981659","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"17890_CR73","doi-asserted-by":"publisher","first-page":"16191","DOI":"10.1007\/s00521-020-04881-z","volume":"32","author":"R Sharma","year":"2020","unstructured":"Sharma R, Morwal S, Agarwal B, Chandra R, Khan MS (2020) A deep neural network-based model for named entity recognition for Hindi language. Neural Comput Appl 32:16191\u201316203. https:\/\/doi.org\/10.1007\/s00521-020-04881-z","journal-title":"Neural Comput Appl"},{"issue":"3","key":"17890_CR74","doi-asserted-by":"publisher","first-page":"55","DOI":"10.48550\/arXiv.1708.02709","volume":"13","author":"T Young","year":"2018","unstructured":"Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55\u201375. https:\/\/doi.org\/10.48550\/arXiv.1708.02709","journal-title":"IEEE Comput Intell Mag"},{"key":"17890_CR75","doi-asserted-by":"publisher","first-page":"110 209","DOI":"10.1109\/ACCESS.2021.3102227","volume":"9","author":"SA Bhat","year":"2021","unstructured":"Bhat SA, Huang NF (2021) Big data and AI revolution in precision agriculture: survey and challenges. IEEE Access 9:110 209-110 222. https:\/\/doi.org\/10.1109\/ACCESS.2021.3102227","journal-title":"IEEE Access"},{"key":"17890_CR76","doi-asserted-by":"publisher","first-page":"84","DOI":"10.48550\/arXiv.2106.03253","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv R, Armon A (2022) Tabular data: deep learning is not all you need. Inf Fusion 81:84\u201390. https:\/\/doi.org\/10.48550\/arXiv.2106.03253","journal-title":"Inf Fusion"},{"key":"17890_CR77","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition:11976\u201311986. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"17890_CR78","doi-asserted-by":"publisher","unstructured":"Lin T, Wang Y, Liu X, Qiu X (2021) A survey of transformers. arXiv:2106.04554. https:\/\/doi.org\/10.48550\/arXiv.2106.04554","DOI":"10.48550\/arXiv.2106.04554"},{"key":"17890_CR79","doi-asserted-by":"publisher","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR: 1597\u20131607. https:\/\/doi.org\/10.48550\/arXiv.2002.05709","DOI":"10.48550\/arXiv.2002.05709"},{"key":"17890_CR80","doi-asserted-by":"publisher","unstructured":"Chen H, Wang Y, Xu C, Shi B, Xu C, Tian Q, Xu C (2020) AdderNet: do we really need multiplications in deep learning? In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition:1468\u20131477. https:\/\/doi.org\/10.48550\/arXiv.1912.13200","DOI":"10.48550\/arXiv.1912.13200"},{"key":"17890_CR81","doi-asserted-by":"publisher","unstructured":"Chollet F (2019) On the measure of intelligence. arXiv:1911.01547. https:\/\/doi.org\/10.48550\/arXiv.1911.01547","DOI":"10.48550\/arXiv.1911.01547"},{"key":"17890_CR82","doi-asserted-by":"publisher","unstructured":"Do\u0161ilovi\u0107 FK, Br\u010di\u0107 M, Hlupi\u0107 N (2018) Explainable artificial intelligence: a survey. In: 2018 41st international convention on information and communication technology. electronics and microelectronics (MIPRO) IEEE, 0210\u20130215 https:\/\/doi.org\/10.23919\/MIPRO.2018.8400040","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"17890_CR83","doi-asserted-by":"publisher","unstructured":"Jiang AQ, Welleck S, Zhou JP, Li W, Liu J, Jamnik M, ..., Lample G (2022) Draft, sketch, and prove: guiding formal theorem provers with informal proofs. arXiv preprint arXiv:2210.12283. https:\/\/doi.org\/10.48550\/arXiv.2210.12283","DOI":"10.48550\/arXiv.2210.12283"},{"key":"17890_CR84","doi-asserted-by":"crossref","unstructured":"Huang J, Gu SS, Hou L, Wu Y, Wang X, Yu H, Han J (2022) large language models can self-improve. arXiv preprint arXiv:2210.11610. https:\/\/openreview.net\/forum?id=NiEtU7blzN","DOI":"10.18653\/v1\/2023.emnlp-main.67"},{"key":"17890_CR85","doi-asserted-by":"publisher","unstructured":"Yang K, Peng N, Tian Y, Klein D (2022) Re3: generating longer stories with recursive reprompting and revision. arXiv:2210.06774. https:\/\/doi.org\/10.48550\/arXiv.2210.06774","DOI":"10.48550\/arXiv.2210.06774"},{"key":"17890_CR86","doi-asserted-by":"publisher","unstructured":"Kreuzberger D, K\u00fchl N, Hirschl S (2022) Machine Learning Operations (MLOps): overview, definition, and architecture. arXiv:2205.02302. https:\/\/doi.org\/10.48550\/arXiv.2205.02302","DOI":"10.48550\/arXiv.2205.02302"},{"key":"17890_CR87","doi-asserted-by":"publisher","unstructured":"Lin J, Zhu L, Chen WM, Wang WC, Gan C, Han S (2022) On-device training under 256KB memory. arXiv preprint arXiv:2206.15472. https:\/\/doi.org\/10.48550\/arXiv.2206.15472","DOI":"10.48550\/arXiv.2206.15472"},{"key":"17890_CR88","doi-asserted-by":"publisher","unstructured":"Koizumi Y, Yatebe K, Zen H, Bacchiani M (2022) WaveFit: an iterative and non-autoregressive neural vocoder based on fixed-point iteration. arXiv:2210.01029. Audio and speech processing. https:\/\/doi.org\/10.48550\/arXiv.2210.01029","DOI":"10.48550\/arXiv.2210.01029"},{"key":"17890_CR89","doi-asserted-by":"crossref","unstructured":"Lewis S, Pavlasek J, Jenkis OC (2022) NARF22: neural articulated radiance fields for configuration-aware rendering. arXiv:2210.01166v1. https:\/\/arxiv.org\/abs\/2210.01166","DOI":"10.1109\/IROS47612.2022.9982194"},{"key":"17890_CR90","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00540-1","author":"P Hess","year":"2022","unstructured":"Hess P et al (2022) Physically constrained generative adversarial networks for improving precipitation fields from earth system models. Nat Mach Intell. https:\/\/doi.org\/10.1038\/s42256-022-00540-1","journal-title":"Nat Mach Intell"},{"key":"17890_CR91","doi-asserted-by":"crossref","unstructured":"Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K, ..., Geras KJ (2022) Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med 14(664). eabo4802. https:\/\/www.science.org\/doi\/abs\/10.1126\/scitranslmed.abo4802","DOI":"10.1126\/scitranslmed.abo4802"},{"key":"17890_CR92","unstructured":"Chowdhury M, Sadek AW (2012) Advantages and limitations of artificial intelligence. Artif Intell Appl Critical Transport Issues 6(3): 360\u2013375. https:\/\/onlinepubs.trb.org\/onlinepubs\/circulars\/ec168.pdf#page=14"},{"key":"17890_CR93","doi-asserted-by":"publisher","DOI":"10.1049\/PBPC057E","author":"C Prabha","year":"2022","unstructured":"Prabha C, Singh J, Rasool R (2022). AIoT technologies and applications for smart environments. https:\/\/doi.org\/10.1049\/PBPC057E","journal-title":"AIoT technologies and applications for smart environments."},{"key":"17890_CR94","doi-asserted-by":"publisher","unstructured":"Dhiman P, Kaur A, Bonkra A (2023) Fake information detection using deep learning methods: a survey. In: 2023 International Conference on Artificial Intelligence and Smart Communication (AISC):858\u2013863. IEEE. https:\/\/doi.org\/10.1109\/AISC56616.2023.10085519","DOI":"10.1109\/AISC56616.2023.10085519"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17890-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17890-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17890-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T06:34:07Z","timestamp":1716618847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17890-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":94,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["17890"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17890-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,19]]},"assertion":[{"value":"2 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}