4 citations · 4 across the 5 of their papers we have counts for
6 papers
Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri +1
Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive and cover only a small share…
LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection
Bahareh Golchin, Banafsheh Rekabdar, Danielle Justo
Detecting anomalies in time series data is crucial for finance, healthcare, sensor networks, and industrial monitoring applications. However, time series anomaly detection often su…
Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach
Bahareh Golchin, Banafsheh Rekabdar
Detecting anomalies in multivariate time series is essential for monitoring complex industrial systems, where high dimensionality, limited labeled data, and subtle dependencies bet…
DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection
Bahareh Golchin, Banafsheh Rekabdar, Kunpeng Liu
Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with li…
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
Bahareh Golchin, Banafsheh Rekabdar
A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Trad…
Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks
Bahareh Golchin, Noushin Riahi
One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotio…