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Journal Of Big Data is a peer-reviewed journal published by Springer Nature, covering Anomaly Detection Techniques and Applications, Imbalanced Data Classification Techniques, Sentiment Analysis and Opinion Mining.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Journal Of Big Data 12, 45–58 (2023).
Formats any DOI in Journal Of Big Data style. No sign-up. |
| Publishes research in | Anomaly Detection Techniques and Applications Imbalanced Data Classification Techniques Sentiment Analysis and Opinion Mining Network Security and Intrusion Detection Machine Learning and Data Classification |
| ISSN | 2196-1115 |
| Citation impact (2-yr) | 14.13 |
| h-index | 122 |
| i10-index | 820 |
| Total citations | 100,997 |
| Article processing charge | $1,300 |
| Open access | Yes |
| Top institutions publishing here | Florida Atlantic University |
| Journal website | journalofbigdata.springeropen.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have…
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by…
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption…
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners…
Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as…
