# Journal Of Big Data

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/journal-of-big-data/

## Facts

| Field | Value |
| --- | --- |
| Open Access | true |
| ISSN-L | 2196-1115 |
| ISSNs | 2196-1115 |
| OpenAlex ID | https://openalex.org/S2737955091 |
| Publisher | Springer Science+Business Media |

## Journal papers

- [Selecting critical features for data classification based on machine learning methods](https://scholariq.org/papers/selecting-critical-features-for-data-classification-based-on-machine-learning/)
- [A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions](https://scholariq.org/papers/a-review-of-graph-neural-networks-concepts-architectures-techniques-challenges/)
- [Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis](https://scholariq.org/papers/review-of-deep-learning-methods-for-remote-sensing-satellite-images/)
- [Advanced machine learning techniques for cardiovascular disease early detection and diagnosis](https://scholariq.org/papers/advanced-machine-learning-techniques-for-cardiovascular-disease-early-detection/)
- [Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics](https://scholariq.org/papers/machine-learning-based-mathematical-modelling-for-prediction-of-social-media/)
- [DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks](https://scholariq.org/papers/dfrdrl-a-dynamic-fuzzy-routing-algorithm-based-on-deep-reinforcement-learning/)
- [Leveraging deep learning for risk prediction and resilience in supply chains: insights from critical industries](https://scholariq.org/papers/leveraging-deep-learning-for-risk-prediction-and-resilience-in-supply-chains/)
- [Enhancing argumentation component classification using contextual language model](https://scholariq.org/papers/enhancing-argumentation-component-classification-using-contextual-language-model/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.