Official repo of "EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)"

Kuzushi — Agentic SAST scanner with AI triage

GUARDIUM is an intelligent Wazuh rule optimization framework designed to reduce false positives, improve alert accuracy, and assist SOC teams in maintaining high-quality SIEM detections. GUARDIUM combines rule analysis, threat context, and Large Language Models (LLMs) to automatically evaluate, explain, and optimize Wazuh rules.

When does suppressing low-confidence clinical alerts actually reduce alarm fatigue? Empirical two-regime analysis on PhysioNet 2019 sepsis data — ceiling effects, floor effects, and when neither applies.

Python security gate with intelligent ML scoring that reduces false positives by 95%. Orchestrates Bandit, pip-audit, and Semgrep into a unified CI/CD pipeline. Includes baseline management, policy enforcement, and explainable predictions. Production-ready with comprehensive tests.

Analytical portfolio demonstrating transaction monitoring, judgment-based alert review, and Excel-driven risk analysis across fraud, AML, and KYC workflows, with a focus on regulator-safe decisioning and operational consistency.

Lightweight network based host & service watchdog - very smart check definitions to cut down on false positives. Eight different nest-able, AND/OR-able health check types. Email, Signal and Webhook alerts. SOC/NOC wallboard, SLA tracking and more.

🚀 SmartAlert AI - Production-ready adaptive ML system for intelligent incident prediction. Achieves 44% F1-Score on challenging datasets with 75% false alarm reduction. Features 118 sophisticated features and real-time adaptive learning.

🔍 Analyze transaction data to identify fraud risks, streamline alert reviews, and ensure compliance in AML and KYC contexts using Excel-driven techniques.