CNN-CatBoost: Model Evaluation Results for ADSS Cable Vibration Recognition

Experimental results for a hybrid LSTM-CNN-CatBoost-GSSSA model designed to recognize external disturbances on underground optical cables. The dataset, shared by Xiaojuan Chen on figshare under CC-BY-4.0, contains model performance metrics, including an accuracy of 97.6% as reported in the description. The file was last updated on 2026-05-04.

Use Cases

• Benchmarking hybrid machine learning models based on reported accuracy comparisons against SVM, CNN, and other architectures.
• Analyzing vibration signal recognition performance for power grid security applications as described.
• Optimizing hyperparameters for CatBoost models using the improved golden sine–enhanced Sparrow Search Algorithm (GSSSA) mentioned in the description.

Strengths

• Model performance is quantified with a specific accuracy metric of 97.6%.
• Results are directly comparable to six other models (SVM, CNN, LSTM-CatBoost, etc.) with percentage improvements listed.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope, likely containing only summary results.

Provenance

Source

figshare

Collection Method

Likely contains experimental results from model evaluation.

Time Range

Experimental timeframe not specified.

Freshness

Last updated 2026 05 04 17:27:08; freshness should be verified.

Geography

Focus is on urban areas, but specific location not stated.