# A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-deep-hybrid-learning-model-to-detect-unsafe-behavior-integrating-convolution/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Lieyun Ding,Weili Fang,Hanbin Luo,Peter E.D. Love,Botao Zhong,Xi Ouyang |
| Citations | 469 |
| DOI | 10.1016/j.autcon.2017.11.002 |
| Fields | Computer Science,Engineering,Health Professions |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2768148056 |
| Type | article |
| Year | 2017 |

## Paper authors

- [Peter E.D. Love](https://scholariq.org/researchers/peter-e-d-love/)

## Paper primary topic

- [Occupational Health and Safety Research](https://scholariq.org/topics/occupational-health-and-safety-research/)

## Paper topics

- [Occupational Health and Safety Research](https://scholariq.org/topics/occupational-health-and-safety-research/)
- [Infrastructure Maintenance and Monitoring](https://scholariq.org/topics/infrastructure-maintenance-and-monitoring/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

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