# Novel Dynamic Batch-Sensitive Adam Optimiser for Vehicular Accident Injury Severity Prediction

arXiv: https://arxiv.org/abs/2605.15083 · HTML: https://mlsanity.com/paper/2605.15083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 72 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 14 of 14)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization](https://mlsanity.com/paper/1810.02263/) (2018)
- [Active label cleaning for improved dataset quality under resource constraints](https://mlsanity.com/paper/2109.00574/) (2021)
- [A Comparison of Optimization Algorithms for Deep Learning](https://mlsanity.com/paper/2007.14166/) (2020)
- [Classification of datasets with imputed missing values: does imputation quality matter?](https://mlsanity.com/paper/2206.08478/) (2022)
- [On the Convergence Proof of AMSGrad and a New Version](https://mlsanity.com/paper/1904.03590/) (2019)
- [Automatic Curriculum Learning with Gradient Reward Signals](https://mlsanity.com/paper/2312.13565/) (2023)
- [An Exploration of Dropout with RNNs for Natural Language Inference](https://mlsanity.com/paper/1810.08606/) (2018)
- [Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model](https://mlsanity.com/paper/2310.05840/) (2023)

## Abstract (arXiv)

The choice of optimiser is important in deep learning, as it strongly influences model efficiency and speed of convergence. However, many commonly used optimisers encounter difficulties when applied to imbalanced and sequential datasets, limiting their ability to capture patterns of minority classes. In this study, we propose Dynamic Batch-Sensitive Adam (DBS-Adam), an optimiser that dynamically scales the learning rate using a batch difficulty score derived from exponential moving averages of gradient norms and batch loss. DBS-Adam improves training stability and accelerates convergence by increasing updates for difficult batches and reducing them for easier ones. We evaluate DBS-Adam by integrating it with Bi-Directional LSTM networks for accident injury severity prediction, addressing class imbalance through SMOTE-ENN resampling and Focal Loss. Four experimental configurations compare baseline Bi-LSTM models and alternative architectures to assess optimiser impact. Rigorous comparison against state-of-the-art optimisers (AMSGrad, AdamW, AdaBound) across five random seeds demonstrated DBS-Adam's competitive performance with statistically significant precision improvements (p=0.020). Results indicate that DBS-Adam outperforms standard optimisation approaches, achieving 95.22% test accuracy, 96.11% precision, 95.28% recall, 95.39% F1-score, and a test loss of 0.0086. The proposed framework enables effective real-time accident severity classification for targeted emergency response and road safety interventions, demonstrating the value of DBS-Adam for learning from imbalanced sequential data.