Pinned Repositories
Context-Informed-Multi-Seq-2-Single-Seq
VDS stations are more densely deployed for monitoring traffic and providing real-time information. By matching VDS stations to CCS sites, a deep learning model called Contextual Informed Multi-Sequence to Single-Sequence (CIMS2SS) is created to generate or estimate CCS data from VDS data.
DINO_concise
Enabling-Risk-Aware-Routing
Enabling Risk-Aware Routing: Harnessing Feature-Rich Big Data for High-Resolution Crash Risk Modeling Using Tree Ensemble
GDOT-RP20-07
RP 20-07: Improving Traffic Data Quality by Cross-Checking Loop-Based Data with Video Detection Data
Improving-Crash-Classification-Modeling-with-Resampling-Methods
Improving Crash Classification Modeling with Resampling Methods
llm-guided-evolution
LLM Guided Evolution - The Automation of Models Advancing Models
Perceiver-Transformer
synthetic-parallel-selenium
To maximize data acquisition, the speed of selenium scraping can be significantly improved by employing a synthetic multi-threaded approach
Tutorials
Miscellaneous Tutorials
yolo-assisted-image-scrape
Utilizing self-supervised targeted image scraping techniques can enhance sparse and imbalanced datasets, to improved performance of classification algorithms.
clint-kristopher-morris's Repositories
clint-kristopher-morris/DINO_concise
clint-kristopher-morris/Tutorials
Miscellaneous Tutorials
clint-kristopher-morris/llm-guided-evolution
LLM Guided Evolution - The Automation of Models Advancing Models
clint-kristopher-morris/Perceiver-Transformer
clint-kristopher-morris/Improving-Crash-Classification-Modeling-with-Resampling-Methods
Improving Crash Classification Modeling with Resampling Methods
clint-kristopher-morris/synthetic-parallel-selenium
To maximize data acquisition, the speed of selenium scraping can be significantly improved by employing a synthetic multi-threaded approach
clint-kristopher-morris/anomaly-detection-by-deep-learning-wavelet-transform
clint-kristopher-morris/Big-Data-Quality-Cross-Check
Improving Traffic Data Quality by Cross-Checking Loop-Based Data with Video Detection Data
clint-kristopher-morris/Enabling-Risk-Aware-Routing
Enabling Risk-Aware Routing: Harnessing Feature-Rich Big Data for High-Resolution Crash Risk Modeling Using Tree Ensemble
clint-kristopher-morris/GDOT-RP20-07
RP 20-07: Improving Traffic Data Quality by Cross-Checking Loop-Based Data with Video Detection Data
clint-kristopher-morris/Multi-Vehicle-Collision-Patterns-on-Freeways
clint-kristopher-morris/reliable-transposition-of-labeled-images
Enhancing Classifier Training with Advanced Image Augmentation Techniques
clint-kristopher-morris/Romberg-Integration-Visualization
Romberg Integration: Visualization
clint-kristopher-morris/sequential-connected-component-labeling-c-and-python
clint-kristopher-morris/Wells-Fargo-Anomaly-Detection-Competition
clint-kristopher-morris/yolo-assisted-image-scrape
Utilizing self-supervised targeted image scraping techniques can enhance sparse and imbalanced datasets, to improved performance of classification algorithms.
clint-kristopher-morris/Context-Informed-Multi-Seq-2-Single-Seq
VDS stations are more densely deployed for monitoring traffic and providing real-time information. By matching VDS stations to CCS sites, a deep learning model called Contextual Informed Multi-Sequence to Single-Sequence (CIMS2SS) is created to generate or estimate CCS data from VDS data.