Machine learning on big data: Opportunities and challenges

By Lina Zhou, Shimei Pan, Jianwu Wang, Athanasios V. Vasilakos. Neurocomputing, 2017

1,043 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/079b2cfe950a96d5a43a3febc983d151fb533b53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ayça Deniz
2. Yanming Miao
3. Tansel Dökeroğlu
4. Qian Bai
5. Shaobo Li
6. Sanlong Jiang
7. Jing Yang
8. Hakan Ezgi Kiziloz

In Management Science and Operations Research

1. Tansel Dökeroğlu

Citations in the corpus

• Review of Image Classification Algorithms Based on Convolutional Neural Networks (2021, background): “More than ten years after LeNet-5 was proposed, neural networks were once surpassed by other machine learning methods [99], such as support vector machines (SVM) [100].”
• Literature review of Industry 4.0 and related technologies (2018, neutral): “To mention some, Zhou et al. (2016a) studied big data performance and better algorithms for programming of Industry 4.0 systems.”
• A comprehensive survey on recent metaheuristics for feature selection (2022, background): “Therefore, feature selection has been the focus of many studies for quite some time [3].”
• Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0 (2020, neutral): “Moreover, as one of the characteristics of big data is the variety (in type, nature, format, etc.) (Zhou et al. 2017), it is crucial to employ data pre-processing activities to ensure the quality of the final models.”