Machine Learning

The rapid growth of spatiotemporal data makes trajectory modeling critical for extracting patterns from large-scale, dynamic mobility datasets. However, many existing methods face challenges with scalability and computational inefficiency. To address these challenges, we propose VecLSTM—a vectorized Long Short-Term Memory (LSTM) framework designed to improve both predictive accuracy and processing performance. VecLSTM introduces a novel dynamic vectorization layer that converts raw GPS trajectories into structured vector embeddings, enabling efficient storage, retrieval, and preprocessing.

The Forbes 2022 Billionaires List dataset contains information about the world's wealthiest individuals, including their net worth, industry, country, and key business ventures. The dataset provides structured details such as rankings, company associations, and financial status, making it useful for various NLP tasks like table-to-text generation, entity recognition, and financial analysis.

This dataset comprises a comprehensive collection of educational courses, each characterized by several key attributes: interests, title, description, category, level, past experience, and rating.

This dataset contains high-resolution wind measurement data collected from 22 channels at varying heights, providing valuable insights for wind energy assessment, atmospheric research, and meteorological studies. The dataset includes wind speed, wind direction, and environmental parameters measured at multiple altitudes ranging from 10m to 120m. Each channel records parameters such as average wind speed, standard deviation, minimum and maximum values, gust speed, and wind vane direction. Additionally, atmospheric parameters such as temperature, relative humidity, and pressure are included.