Machine learning:
uma análise bibliométrica
DOI:
https://doi.org/10.5585/2023.24056Palavras-chave:
Aprendizado de Máquina, Algoritmo, Análise de Big Data, Análise Bibliométrica, PreditivoResumo
Objetivo: Apresentar uma visão dos artigos científicos publicados nos últimos dez anos sobre o tema aprendizado de máquina, do inglês machine learning (ML), com ênfase nos algoritmos preditivos.
Método/abordagem: Análise bibliométrica, com apoio do protocolo PRISMA, para avaliar autores, universidades e países, quanto a produtividade, citações bibliográficas e focos sobre o tema, com amostra de 773 artigos das bases de dados Scopus e Web of Science, no período de 2013 a maio/2023.
Originalidade/valor: Há ausência de estudos na literatura que consolidem artigos relacionados a ML e Big Data. A pesquisa contribui para cobrir tal lacuna, favorecendo o delineamento de ações e pesquisas futuras.
Principais resultados: Foram identificados no corpus bibliométrico de ML: autores mais citados e com maior número de publicações, países e universidades mais produtivas, periódicos com maior número de publicações e citações, áreas de conhecimento com maior número de publicações e artigos de maior prestígio. Nos temas e domínios de ML, foram identificados: principais coocorrências de palavras-chaves, temas emergentes (agrupados em cinco clusters) e nuvem de palavras por título e por resumo. Os estudos sobre impacto da aquisição de dados e análise preditiva representam oportunidades para pesquisas futuras.
Contribuições teóricas/metodológicas: O protocolo PRISMA possibilitou a identificação e análises quantitativa e qualitativa relevantes dos artigos, consolidando o conhecimento científico sobre o tema.
Contribuições sociais/gerenciais: Facilidade de compreender a maturidade das pesquisas sobre ML e Big Data por parte de gestores de empresas e pesquisadores, quanto à viabilidade de investimentos para se obter vantagens competitivas com tais tecnologias.
Downloads
Referências
Ahani A., Nilashi M., Ibrahim O., Sanzogni L., Weaven S., (2019) - Market segmentation and travel choice prediction in Spa hotels through TripAdvisors online reviews https://doi.org/10.1016/j.ijhm.2019.01.003
Ahmadi H., Arji G., Shahmoradi L., Safdari R., Nilashi M., Alizadeh M., (2019) - The application of internet of things in healthcare a systematic literature review and classification. https://doi.org/10.1007/s10209-018-0618-4
Ali M.A.M., Bashar A., Rabbani M.R., Abdulla Y., (2020) - Transforming Business Decision Making with Internet of Things IoT and Machine Learning ML. https://doi.org/10.1109/dasa51403.2020.9317174
Alonso-Betanzos A., Bolon-Canedo V., (2018) - Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches. https://doi.org/10.1007/978-3-319-77932-4_37
Antonopoulos I., Robu V., Couraud B., Et Al (2020) - Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. https://doi.org/10.1016/j.rser.2020.109899
Athmaja S.; Hanumanthappa M., Kavitha V., (2017) - A Survey of Machine Learning Algorithms for Big Data Analytics. https://doi.org/10.1109/iciiecs.2017.8276028
Baryannis G., Validi S., Dani S., Antoniou G., (2019) - Supply chain risk management and artificial intelligence state of the art and future research directions. https://doi.org/10.1080/00207543.2018.1530476
Batistic S., Van D.L.P., (2019) - History Evolution and Future of Big Data and Analytics A Bibliometric Analysis of Its Relationship to Performance in Organizations. https://doi.org/10.1111/1467-8551.12340
Bhavnani S.P., Parakh K., Atreja A., Et Al (2017) - 2017 Roadmap for Innovation - ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data and Precision Health. https://doi.org/10.1016/j.jacc.2017.10.018
Bilgic E., Cakir O., Kantardzic M., Duan Y., Cao G., (2021) - Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis. https://doi.org/10.1080/09593969.2021.1915847
Böse J.-H., Flunkert V., Gasthaus J., Et Al (2017) - Probabilistic demand forecasting at scale. https://doi.org/10.14778/3137765.3137775
Bui T.D., Tsai F.M., Tseng M.L., Tan R.R., Yu K.D.S., Lim M.K., (2021) - Sustainable supply chain management towards disruption and organizational ambidexterity A data driven analysis. https://doi.org/10.1016/j.spc.2020.09.017
Calatayud A., Mangan J., Christopher M., (2019) - The self-thinking supply chain - Supply Chain Management - Emerald Group Holdings Ltd. - United Kingdom. https://doi.org/10.1108/SCM-03-2018-0136
Cerruela García G., Luque Ruiz I., Gómez-Nieto M., (2016) - State of the art trends and future of bluetooth low energy near field communication and visible light communication in the development of smart cities - Sensors (Switzerland) - MDPI AG – Spain. https://doi.org/10.3390/s16111968
Chandra S. E Verma S., (2021) - Big Data and Sustainable Consumption A Review and Research Agenda – Vision - Sage Publications India Pvt. Ltd – India. https://doi.org/10.1177/09722629211022520
Chang, P.C., Liu, C.H., And Fan, C.Y. (2009) - Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. https://doi.org/10.1016/j.knosys.2009.02.005
Chen M., Mao S., Liu Y., (2014) - Big data: A survey - Mobile Networks and Applications. https://doi.org/10.1007/s11036-013-0489-0
Chen M., Hao Y.X., Hwang K., Wang L., Wang L., (2017) - Disease Prediction by Machine Learning Over Big Data From Healthcare Communities. https://doi.org/10.1109/access.2017.2694446
Choi T.-M., Wallace S.W., Wang Y., (2018) - Big Data Analytics in Operations Management. https://doi.org/10.1111/poms.12838
Dinov I.D., Heavner B., Tang M., et al (2016) - Predictive Big Data Analytics A Study of Parkinsons Disease Using Large Complex Heterogeneous Incongruent MultiSource and Incomplete Observations - Plos One - Public Library Science - United States. https://doi.org/10.1371/journal.pone.0157077
Duan Y., Edwards J.S., Dwivedi Y.K., (2019) - Artificial Intelligence for Decision Making In The Era Of Big Data Evolution Challenges And Research Agenda. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dwivedi Y.K., Hughes L., Ismagilova E., et al (2021) - Artificial Intelligence AI Multidisciplinary perspectives on emerging challenges opportunities and agenda for research practice and policy. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
George G., Osinga E., Lavie D., Scott B., (2016) - Big data and data science methods for management research. https://doi.org/10.5465/amj.2016.4005
Gill S. S., Tuli S., Xu M., et al, (2019) - Transformative effects of IoT Blockchain and Artificial Intelligence on cloud computing Evolution vision trends and open challenges. https://doi.org/10.1016/j.iot.2019.100118
Gupta N., Ahuja N., Malhotra S., Bala A., Kaur G., (2017) - Intelligent heart disease prediction in cloud environment through ensembling - Expert Systems – Wiley – India. https://doi.org/10.1111/exsy.12207
Hashimoto D.A., Rosman G., Rus D., Meireles O.R., (2018) - Artificial Intelligence in Surgery Promises and Perils - Annals of Surgery - Lippincott Williams & Wilkins - United States. http://dx.doi.org/10.1097/SLA.0000000000002693
Hassija V., Chamola V., Saxena V., Jain D., Goyal P., Sikdar B., (2019) - A Survey on IoT Security Application Areas Security Threats and Solution Architectures. https://doi.org/10.1109/access.2019.2924045
Hu H., Wen Y., Chua T-S., Li X., (2014) - Toward scalable systems for big data analytics A technology tutorial - IEEE Access - Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/access.2014.2332453
Kitchens B., Dobolyi D., Li J., Abbasi A., (2018) - Advanced Customer Analytics Strategic Value Through Integration of RelationshipOriented Big Data. https://doi.org/10.1080/07421222.2018.1451957
Kou G., Chao X., Peng Y., Alsaadi F.E., Herrera-Viedma E., (2019) - Machine learning methods for systemic risk analysis in financial sectors. https://doi.org/10.3846/tede.2019.8740
Kousis A. E Tjortjis C., (2021) - Data mining algorithms for smart cities A bibliometric analysis - Algorithms - MDPI AG – Greece. https://doi.org/10.3390/a14080242
Lichman, M. (2013) - UCI Machine Learning Repository. Disponível em: https://archive.ics.uci.edu/ml/datasets/wine
Johnson A.E.W., Ghassemi M.M., Nemati S., Niehaus K.E., Clifton D.A., Clifford G.D., (2016) - Machine Learning and Decision Support in Critical Care. https://doi.org/10.1109/jproc.2015.2501978
Jordan, M.I. E Mitchell, T.M. (2015) - Machine learning: Trends perspectives and prospects. Science, 349:255–260. https://doi.org/10.1126/science.aaa8415
Ke J., Zheng H., Yang H., Chen X. (2017) - Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. https://doi.org/10.1016/j.trc.2017.10.016
Krawczyk B., (2016) - Learning from imbalanced data open challenges and future directions - Progress in Artificial Intelligence – Springernature – Poland. https://doi.org/10.1007/s13748-016-0094-0
L'heureux A., Grolinger K., Elyamany H.F., Capretz M.A.M., (2017) - Machine Learning with Big Data Challenges and Approaches - IEEE Access - Institute of Electrical and Electronics https://doi.org/10.1109/access.2017.2696365
Levy, Y.; Ellis, T.J. A system approach to conduct an effective literature review in support of information systems research. Informing Science Journal, v.9, p.181-212, 2006. https://doi.org/10.28945/479
Ma C., Zhang H.H., Wang X.F., (2014) - Machine learning for Big Data analytics in plants - Trends in Plant Science - Elsevier Science London – China. https://doi.org/10.1016/j.tplants.2014.08.004
Mishra D., Gunasekaran A., Papadopoulos T., Childe S.J., (2018) - Big Data and supply chain management a review and bibliometric analysis. https://doi.org/10.1007/s10479-016-2236-y
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Stewart, L. A. (2015) - Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1). https://doi.org/10.1186/2046-4053-4-1
Moreira Mwl., Rodrigues Jjpc., Kumar N., Saleem K., Illin Iv, (2019) - Postpartum depression prediction through pregnancy data analysis for emotionaware smart systems updates. https://doi.org/10.1016/j.inffus.2018.07.001
Nguyen H.D., Tran K.P., Thomassey S., Hamad M., (2021) - Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. https://doi.org/10.1016/j.ijinfomgt.2020.102282
Nguyen T., Zhou L., Spiegler V., Ieromonachou P., Lin Y., (2018) - Big data analytics in supply chain management A stateoftheart literature review. https://doi.org/10.1016/j.cor.2017.07.004
Qian T.Q., Zhu S.J., Hoshida Y., (2019) - Use of big data in drug development for precision medicine an update. https://doi.org/10.1080/23808993.2019.1617632
Razavian N., Blecker S., Schmidt A.M., Smith-Mclallen A., Nigam S., Sontag D., (2015) -PopulationLevel Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors https://doi.org/10.1089/big.2015.0020
Sahoo S., (2021) - Big data analytics in manufacturing a bibliometric analysis of research in the field of business management. https://doi.org/10.1080/00207543.2021.1919333
Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., Kumar, A., (2020) - A systematic literature review on machine learning applications for sustainable agriculture supply chain performance - Computers & Operations Research - Pergamon-Elsevier Science Ltd – England. https://doi.org/10.1016/j.cor.2020.104926
Shokouhyar S., Shokoohyar S., Sobhani A., Gorizi A.J., (2021) - Shared mobility in post-COVID era: New challenges and opportunities - Sustainable Cities and Society - Elsevier Ltd https://doi.org/10.1016/j.scs.2021.102714
Silver, D., Huang, A. E Guez, A. (2016) - Mastering the game of go with deep neural networks and tree search - Nature, 529:484–489. https://doi.org/10.1038/nature16961
Silver, D., Schrittwieser, J., Simonyan, K. E Antonoglou, I. (2017) - Mastering the game of go without human knowledge - Nature, 550:354–359. https://doi.org/10.1038/nature24270
Raschka, S. E Mirjalili, V. (2017) - Python Machine Learning, 2nd Ed.- Packt Publishing, Birmingham, UK, 2 edition.
Trieu V.-H., (2017) - Getting value from Business Intelligence systems A review and research agenda - Decision Support Systems - Elsevier B.V. – Australia. https://doi.org/10.1016/j.dss.2016.09.019
Tzeng G.-H., Shen K.-Y., (2017) - New concepts and trends of hybrid multiple criteria decision making - ISBN 9780367573133
Wanasinghe T.R., Wroblewski L., Petersen B.K., et al (2020) - Digital Twin for the Oil and Gas Industry Overview Research Trends Opportunities and Challenges. https://doi.org/10.1109/access.2020.2998723
Wang D., Liu X., Wang, M., (2013) - A dt-svm strategy for stock futures prediction with big data - IEEE 16th International Conference on Computational Science and Engineering. https://doi.org/10.1109/cse.2013.147
Wang J.L., Zhao P.L., Hoi S.C.H., Jin R., (2014) - Online Feature Selection and Its Applications - IEEE Transactions on Knowledge and Data Engineering - IEEE Computer Soc - United States. https://doi.org/10.1109/tkde.2013.32
Wang W., Gao J.Y., Zhang M.H., et al (2018) - Rafiki Machine Learning as an Analytics Service System - Proceedings of The Vldb Endowment - Assoc Computing Machinery – China. https://doi.org/10.48550/arXiv.1804.06087
Wang Y., Chen Q., Hong T., Kang C., (2019) - Review of Smart Meter Data Analytics Applications Methodologies and Challenges. https://doi.org/10.1109/tsg.2018.2818167
Xu J., Huang E., Chen C.-H., Lee L.H., (2015) - Simulation optimization A review and exploration in the new era of cloud computing and big data. https://doi.org/10.1142/s0217595915500190
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 Emerson Martins, Napoleao Verardi Galegale

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Resumo 642
- pdf 555
- pdf (English) 232