anomoly-detection

Exploring possible methods for Audio Anomaly Detection - on machine sounds (MIMII dataset)

Edge-computed multi-sensor platform correlating hydrophone acoustics with environmental data (temp/pH/turbidity/salinity) for marine ecosystem health monitoring.

Data pipeline and analytics toolkit for Garmin smartwatch data using GarminDB exports. Extracts, aggregates, and cleans health metrics in SQLite, with tools for modeling, anomaly detection, and visualization.

Coding interview platform that turns browser, editor, and webcam signals into an explainable integrity confidence score. Every flag comes with its evidence, and a human reviewer makes the final call. Built with Next.js, Express, Node.js, Python, and LiveKit.

🚀 Detect anomalies in structured datasets with this AI-driven ETL pipeline, ensuring data quality through seamless ingestion and machine learning insights.

[Anomaly detection] refers to the process of identifying patterns in data that do not conform to expected behavior. This project aims to develop a machine learning model to predict and identify potential attacks in IoT networks, thus helping to secure these networks from malicious activities.

Project on implementation of XAI in wearables using a dataset provided by Kaggle

An automated, real-time EWS verification platform replacing paper certificate fraud with cross-registry telemetry (Income Tax, UPI volumes, Aadhaar family graphs, and digitized land records). Programmatically enforces PPP urban tiering and structural agricultural land caps. Designed by Srinivasa.

Group 13's project for the WASP course: Scalable Data Science and Distributed Machine Learning

Flow-level behavioural detection of command-and-control beaconing under timing jitter, size variation, burst traffic, hard benign profiles, and CTU-13 public-data domain shift. Includes synthetic benchmarking, interpretable/statistical/anomaly/supervised baselines, minimum-evidence analysis, CTU-native validation, and report-ready results.

AI-powered ETL pipeline with ML anomaly detection and FastAPI deployment

ᴀqᴜᴀꜰʟᴏᴡ ɪꜱ ᴀ ꜰᴜʟʟ-ꜱᴛᴀᴄᴋ ᴘʟᴀᴛꜰᴏʀᴍ ᴛʀᴀɴꜱꜰᴏʀᴍɪɴɢ ᴇɴᴠɪʀᴏɴᴍᴇɴᴛᴀʟ ᴛʀᴀᴄᴋɪɴɢ ɪɴᴛᴏ ᴘʀᴏᴀᴄᴛɪᴠᴇ ʙʀᴇᴀᴄʜ ᴘʀᴇᴠᴇɴᴛɪᴏɴ. ʙᴜɪʟᴛ ᴀꜱ ᴀ ʜɪɢʜ-ᴘᴇʀꜰᴏʀᴍᴀɴᴄᴇ ᴀᴘᴘʟɪᴄᴀᴛɪᴏɴ, ɪᴛ ꜱᴛʀᴇᴀᴍꜱ ʟɪᴠᴇ ᴍᴜʟᴛɪ-ᴘᴀʀᴀᴍᴇᴛᴇʀ ɪᴏᴛ ᴅᴀᴛᴀ ᴠɪᴀ ꜰᴀꜱᴛᴀᴘɪ ꜱꜱᴇ ᴛᴏ ᴀ ɴᴇxᴛ.ᴊꜱ 14 ᴅᴀꜱʜʙᴏᴀʀᴅ, ᴜꜱɪɴɢ ᴀ ᴅᴜᴀʟ-ʟᴀʏᴇʀ ᴀɪ ᴇɴɢɪɴᴇ ᴛᴏ ꜰᴏʀᴇᴄᴀꜱᴛ ᴄᴏᴍᴘʟɪᴀɴᴄᴇ ʀɪꜱᴋꜱ ʙᴇꜰᴏʀᴇ ᴛʜᴇʏ ᴍᴀɴɪꜰᴇꜱᴛ. ᴜꜱᴇꜱ ᴀᴘᴀᴄʜᴇ ᴀɪʀꜰʟᴏᴡ ꜰᴏʀ ᴅᴀᴛᴀ ᴀᴜᴛᴏᴍᴀᴛɪᴏɴ

BigFoot is a comprehensive analytics tool designed for the analysis of Bigfoot sighting data, using machine learning and data visualisation. The application imports verified sighting reports from the BFRO (Bigfoot Field Researchers Organisation) and provides interactive dashboards, maps, and predictive tools for exploring cryptid reports.

A software engineering project implementing continuous system monitoring, predictive anomaly detection, automated alerting, modular architecture, and comprehensive testing.

Machine Learning-based credit card fraud detection system using classification algorithms, imbalance handling, and performance optimization for secure transactions.

Benchmarking Temporal Convolutional Networks (TCN) vs RNN, LSTM, and GRU for long-range sequence modeling with PyTorch; includes modular framework, stability improvements, and empirical analysis.

A predictive maintenance framework contrasting feed-forward and sequential machine learning models to forecast industrial machinery failures, achieving near-perfect AUROC scores with GRU models.

Ranks 6,819 companies by bankruptcy risk using SQL control tests plus machine learning. ROC-AUC 0.929.

Honeywell Technologies Campus Connect 2026 Hackathon Submission: AI-Powered Behavioral Anomaly Detection for Cybersecurity

Agentic Data Engineering Platform is an open-source, production-ready ETL solution that combines the Medallion Architecture with AI-powered agents that autonomously profile, clean, and optimize your data—so you can focus on insights, not infrastructure.