Big data deep learning: Challenges and perspectives
Open accessAbstract
Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
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Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544 · see citing works
A survey towards an integration of big data analytics to big insights for value-creation
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Wang, M., Luo, Y., Wang, T., Wan, C., Pan, L., Pan, S., He, K., Neo, A., & Chen, X. (2021) · Advanced Materials · doi:10.1002/adma.202003014 · Cited by 451 · see citing works
Self-citationHan, E.-R., Yeo, S., Kim, M.-J., Lee, Y.-H., Park, K.-H., & Roh, H. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1891-5 · Cited by 393 · see citing works
Privacy preservation in federated learning: An insightful survey from the GDPR perspective
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Deep learning in drug discovery: opportunities, challenges and future prospects
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Big data reduction framework for value creation in sustainable enterprises
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EMG Pattern Recognition in the Era of Big Data and Deep Learning
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“We selected articles dating from 2010 in order to include the period before the major growth in electronic health records and digital information in 2011.(32,33) Article inclusion criteria included studies discussing the need for AI to be incorporated in the medical curriculum either at undergraduate level.”
BackgroundMethodRetrieval-Based Prompt Selection for Code-Related Few-Shot Learning
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A deep learning approach for fault diagnosis of induction motors in manufacturing
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Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
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Software engineering challenges for machine learning applications: A literature review
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In Search of Big Medical Data Integration Solutions - A Comprehensive Survey
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AI system engineering key challenges and lessons learned
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Data-driven event triggering for IoT applications
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Secure and robust machine learning for healthcare: A survey
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Deep Learning Based Affective Model for Speech Emotion Recognition
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Determination Human Behavior Prediction Supported by Cognitive Computing-Based Neural Network
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Self-citation
- Authors
- Chen, X.-W., & Lin, X.
- Affiliations
- Wayne State University (US)
- Oakland University (US)
- ORCID
- Lin, Xiaotong
- Venue
- IEEE Access
- Published
- 2014
- Volume
- 2
- Pages
- 514-525
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W1984020445
- Cited by
- 1,255
- References
- 102
- Citations by year
- 2026: 35 · 2025: 69 · 2024: 107 · 2023: 125 · 2022: 126 · 2021: 156 · 2020: 156 · 2019: 172 · 2018: 144 · 2017: 83 · 2016: 50 · 2015: 31 · 2014: 1
- FWCI
- 102.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- deep learning, big data, big data challenges, predictive analytics, machine learning, pattern recognition, future trends
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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