Bibiliometric Analysis of the Scientific Production of Deep Learning and Big Data
Keywords:
Artificial intelligence, bibliometric analysis, big data, deep learningAbstract
This article presents a bibliometric analysis of the scientific production on deep learning and big data worldwide. Using the R package and the associated biblioshiny, the study analyzed 456 research articles published in Scopus between 2003 and 2023. The study applied performance analysis, keyword analysis, and thematic analysis. China is the country with the highest production (536 publications) followed by India (260 publications), likewise, most of these collaborations occur from China to the United States, Hong Kong, Sweden, Australia, Pakistan, Saudi Arabia and other countries. The rapid growth of the keywords Deep learning, big data, learning systems and data analytics; It demonstrated the interest of researchers, industry professionals, governments, investors and all other key players in the need to optimize information processing with artificial intelligence features. Finally, the thematic analysis shows that the predictive improvements through Big Data will be applied to traffic management, medical care and the forecasting of economic trends. As future work, Data Masking should be considered as a security measure, incorporation of multi-cloud architecture, Data Fabric and Data Mesh, to manage and improve the exchange of data from different sources.
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Melgar-García, Laura, et al. A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing, 2022, vol. 500, p. 268-278.
Osinga, Sjoukje A., et al. Big data in agriculture: Between opportunity and solution. Agricultural Systems, 2022, vol. 195, p. 103298.
Knutsson, Alex; Unnebäck, Jakob. Implementation and Visualization of Importance sampling in Deep learning. 2023.
García-Peñalvo, Francisco José. Developing robust state-of-the-art reports: Systematic Literature Reviews. 2022.
Arifah, Fitriana Hayyu, et al. A review of medicinal plants for the treatment of diabetes mellitus: The case of Indonesia. South African Journal of Botany, 2022, vol. 149, p. 537-558.
Cooper, I. Diane. Bibliometrics basics. Journal of the Medical Library Association: JMLA, 2015, vol. 103, no 4, p. 217.
Chen, Yuanfang; SHU, Lei; Wang, Lei. Traffic flow prediction with big data: A deep learning based time series model. En 2017 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, 2017. p. 1010-1011.
Najafabadi, Maryam M., et al. Deep learning applications and challenges in big data analytics. Journal of big data, 2015, vol. 2, no 1, p. 1-21.
Hossain, M. Shamim; Muhammad, Ghulam. Emotion recognition using deep learning approach from audio–visual emotional big data. Information Fusion, 2019, vol. 49, p. 69-78.
Wang, Jing, et al. Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach. En IEEE INFOCOM 2017-IEEE conference on computer communications. IEEE, 2017. p. 1-9.
Waheed, Hajra, et al. Predicting academic performance of students from VLE big data using deep learning models. Computers in Human behavior, 2020, vol. 104, p. 106189.
Chae, Sangwon; Kwon, Sungjun; Lee, Donghyun. Predicting infectious disease using deep learning and big data. International journal of environmental research and public health, 2018, vol. 15, no 8, p. 1596.
Li, Yu, et al. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods, 2019, vol. 166, p. 4-21.
Chatterjee, Ankush, et al. Understanding emotions in text using deep learning and big data. Computers in Human Behavior, 2019, vol. 93, p. 309-317.
Sohangir, Sahar, et al. Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 2018, vol. 5, no 1, p. 1-25.
Ning, Chao; You, Fengqi. Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Computers & Chemical Engineering, 2019, vol. 125, p. 434-448.
Hassan, Mohammad Mehedi, et al. A hybrid deep learning model for efficient intrusion detection in big data environment. Information Sciences, 2020, vol. 513, p. 386-396.
Liu, Jia, et al. Urban big data fusion based on deep learning: An overview. Information Fusion, 2020, vol. 53, p. 123-133.
Francis, Jack; BIAN, Linkan. Deep learning for distortion prediction in laser-based additive manufacturing using big data. Manufacturing Letters, 2019, vol. 20, p. 10-14.
Jan, Bilal, et al. Deep learning in big data analytics: a comparative study. Computers & Electrical Engineering, 2019, vol. 75, p. 275-287.
Faker, Osama; Dogdu, Erdogan. Intrusion detection using big data and deep learning techniques. En Proceedings of the 2019 ACM Southeast conference. 2019. p. 86-93.
Chen, Kai-Wei; TSAI, Ming-Jong. Multi-nozzle pneumatic extrusion based additive manufacturing system for fabricating a sandwich structure with soft and hard material. En 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. p. 1-6.
Dzogbewu, Thywill Cephas, et al. Multi-material additive manufacturing of electronics components: A bibliometric analysis. Results in Engineering, 2023, p. 101318.
Kiganda, Cylas; Akcayol, Muhammet Ali. Forecasting the spread of COVID-19 using deep learning and big data analytics methods. SN Computer Science, 2023, vol. 4, no 4, p. 374.
Alzubaidi, Laith, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 2021, vol. 8, p. 1-74.
Rahmaty, Maryam. Machine learning with big data to solve real-world problems. Journal of Data Analytics, 2023, vol. 2, no 1, p. 9-16.
Lin, Haifeng, et al. Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine. Digital Communications and Networks, 2023, vol. 9, no 1, p. 111-124.
Rafierad, Soodeh, et al. Construction and Validation of Dimensions and Components of the Organizational Anomie Scale in order to provide a Native Model in Government Hospitals. Journal of System Management, 2022, vol. 8, no 2, p. 57-73.
Balseca-Chávez, Fabián; Colina-Vargas, Alejandra Mercedes; ESPINOZA-MINA, Marcos Antonio. Identificación de amenazas informáticas aplicando arquitecturas de Big Data. INNOVA Research Journal, 2021, vol. 6, no 3.2, p. 141-167.
Gómez-Larrakoetxea, Nerea, et al. Estrategia de selección de hardware y algoritmos de inteligencia artificial en entornos “edge computing”. DYNA-Ingeniería e Industria, 2023, vol. 98, no 1.
Verma, Sneha, et al. Uncertainty quantification of spectral predictions using deep neural networks. Chemical Communications, 2023, vol. 59, no 46, p. 7100-7103.
Pan, Qinghe, et al. Predicting the Price of Second-Hand Housing Based on Lambda Architecture and KD Tree. INFOCOMMUNICATIONS JOURNAL, 2022, vol. 14, no 1, p. 2-10.
Ding, Yao, et al. Evaluating Surface Water Nitrogen Pollution via Visual Clustering in Megacity Chengdu. Water, 2023, vol. 15, no 11, p. 2113.
Casaccia, Sara, et al. Assessment of domestic well-being: from perception to measurement. IEEE Instrumentation & Measurement Magazine, 2021, vol. 24, no 6, p. 58-67.
Lingling, T. O. N. G., et al. Data masking model for heterogeneous big data environment. 北京航空航天大学学报, 2022, vol. 48, no 2, p. 249-257.
Hechler, Eberhard; WEIHRAUCH, Maryela; WU, Yan. Data Fabric and Data Mesh Research Areas. En Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Cataloging, Governance, Integration, Orchestration, and Consumption. Berkeley, CA: Apress, 2023. p. 375-392.
Debellis, Michael; PINERA, Livia; CONNOR, Christopher. Interoperability Frameworks: Data Fabric and Data Mesh Architectures. En Data Science with Semantic Technologies. CRC Press, 2023. p. 267-286.
Karatas, Mumtaz, et al. Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 2022, vol. 200, p. 116912.
Paigude, S. ., Pangarkar, S. C. ., Hundekari, S. ., Mali, M. ., Wanjale, K. ., & Dongre, Y. . (2023). Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 01–10. https://doi.org/10.17762/ijritcc.v11i3s.6149
Christopher Davies, Matthew Martinez, Catalina Fernández, Ana Flores, Anders Pedersen. Applying Recommender Systems in Educational Platforms. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/171
Dhabliya, D., Dhabliya, R. Key characteristics and components of cloud computing (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 12-18.
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