# HYBRID DEEP SPIKING RESIDUAL BELIEF NETWORK FOR INJURY DETECTION AND CLASSIFICATION

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/hybrid-deep-spiking-residual-belief-network-for-injury-detection-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Geetamma Tummalapalli,Balajee Maram,Parul Datta,Anupama Angadi,Guru Kesava Dasu Gopisetty,Malathy Vanniappan |
| Citations | 0 |
| DOI | 10.1142/s0219519425500071 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4407579715 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Balajee Maram](https://scholariq.org/researchers/balajee-maram/)

## Paper journal

- [Journal of Mechanics in Medicine and Biology](https://scholariq.org/journals/journal-of-mechanics-in-medicine-and-biology/)

## Paper primary topic

- [Fire Detection and Safety Systems](https://scholariq.org/topics/fire-detection-and-safety-systems/)

## Paper topics

- [Fire Detection and Safety Systems](https://scholariq.org/topics/fire-detection-and-safety-systems/)
- [Gait Recognition and Analysis](https://scholariq.org/topics/gait-recognition-and-analysis/)
- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)

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