RIVET — Environmental Incident Investigator

Wed, 21 Oct 2026 09:04:48 +0000

RIVET uses computer vision and water-quality sensors to investigate suspicious changes in waterways, verify evidence, and generate traceable environmental incident reports.

Inspiration

Environmental changes in waterways can go unnoticed when monitoring depends on isolated measurements or occasional inspections. We wanted to explore how computer vision and intelligent investigation could help identify suspicious changes and direct attention toward situations that need further examination.

What it does

RIVET (Environmental Incident Investigator) is a proposed system that combines OpenCV-based video analysis with water-quality sensor readings. It aims to detect unusual changes, compare visual evidence with sensor measurements, investigate uncertainty through additional analysis, and generate traceable incident reports.

How we built it

The planned prototype uses Python and OpenCV to analyse video over time. Water-quality sensors will provide independent measurements, while an AI agent will select follow-up analyses when observations are incomplete or conflicting. AWS will support the data-processing and storage workflow.

Challenges we expect

Important challenges include distinguishing genuine environmental changes from lighting variations, synchronising video with sensor readings, collecting reliable test data, and reducing false alarms.

Accomplishments we're aiming for

Our goal is to build a working prototype that demonstrates evidence-driven environmental investigation, combines computer vision with sensor measurements, and evaluates its performance against simpler monitoring methods.

What we aim to learn

We aim to investigate whether adaptive visual analysis and sensor fusion can improve environmental anomaly detection, how uncertainty should influence an AI agent's next action, and how to make automated environmental monitoring more transparent and reliable.

Fake News Detection

Sun, 03 May 2026 14:19:18 +0000

Excited to share my latest project: Fake News Detection using Machine Learning & NLP!
In today's world of misinformation, I built an end-to-end system that automatically detects whether a news article is FAKE or REAL using Python, NLP, and Deep Learning.
What I built: ✅ Complete NLP pipeline with text cleaning & lemmatization ✅ TF-IDF vectorization with bigrams (5000 features) ✅ 6 Machine Learning models compared side by side ✅ Deep Neural Network with Dropout regularization ✅ Real-time user input prediction system ✅ EDA with Word Clouds & visualizations
Models Used: ▸ Logistic Regression ▸ Naive Bayes ▸ Random Forest ▸ Decision Tree ▸ Gradient Boosting ▸ Linear SVM ▸ Dense Neural Network (TensorFlow/Keras)
Key Findings: ▸ Fake news uses emotional, sensational & uppercase language ▸ Real news is formal, structured & factual ▸ Logistic Regression achieved excellent accuracy on TF-IDF features ▸ Neural Network with Early Stopping & Dropout prevented overfitting
️ Tech Stack: Python | Pandas | Scikit-learn | TensorFlow | Keras | NLTK | Matplotlib | Seaborn
This project taught me how powerful simple NLP techniques like TF-IDF can be when combined with the right models. Sometimes you don't need GPT — a well-tuned Logistic Regression can be just as effective!
Full code available on GitHub https://github.com/Urooj25/News_Detection_Model.git

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RIVET — Environmental Incident Investigator

Fake News Detection