# VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration

arXiv: https://arxiv.org/abs/2409.19258 · HTML: https://mlsanity.com/paper/2409.19258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Adaptive Gradient Methods with Dynamic Bound of Learning Rate](https://mlsanity.com/paper/1902.09843/) (2019)
- [TPNet: Trajectory Proposal Network for Motion Prediction](https://mlsanity.com/paper/2004.12255/) (2020)
- [VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search](https://mlsanity.com/paper/2409.17383/) (2024)
- [A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks](https://mlsanity.com/paper/1810.07742/) (2018)
- [Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction](https://mlsanity.com/paper/2006.05905/) (2020)
- [A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion](https://mlsanity.com/paper/2101.08969/) (2021)

## Built on by (most cited first, 1 of 1)

- [NexusIndex: Integrating Advanced Vector Indexing and Multi-Model Embeddings for Robust Fake News Detection](https://mlsanity.com/paper/2410.18294/) (2024)

## Abstract (arXiv)

Activity recognition is a challenging task due to the large scale of trajectory data and the need for prompt and efficient processing. Existing methods have attempted to mitigate this problem by employing traditional LSTM architectures, but these approaches often suffer from inefficiencies in processing large datasets. In response to this challenge, we propose VecLSTM, a novel framework that enhances the performance and efficiency of LSTM-based neural networks. Unlike conventional approaches, VecLSTM incorporates vectorization layers, leveraging optimized mathematical operations to process input sequences more efficiently. We have implemented VecLSTM and incorporated it into the MySQL database. To evaluate the effectiveness of VecLSTM, we compare its performance against a conventional LSTM model using a dataset comprising 1,467,652 samples with seven unique labels. Experimental results demonstrate superior accuracy and efficiency compared to the state-of-the-art, with VecLSTM achieving a validation accuracy of 85.57%, a test accuracy of 85.47%, and a weighted F1-score of 0.86. Furthermore, VecLSTM significantly reduces training time, offering a 26.2% reduction compared to traditional LSTM models.