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Dynamic Pattern Recognition (NEAT Algorithm)

Published in Intelligent Computing by Taylor & Francis (2025). Adaptive neuroevolution model.

Peer-reviewed research exploring adaptive pattern recognition in interactive AI systems.

Traditional static difficulty and policy models in interactive AI systems failed to adapt to real-time performance fluctuations.

Engineered an adaptive pattern recognition architecture using NeuroEvolution of Augmenting Topologies (NEAT) and self-play reinforcement learning in Python with TensorFlow.

Achieved 70% performance gain across 50 generations. Published through Taylor & Francis (2025).

Zero-Overhead Autonomous Agent

ICRAECA & IFERP Certified. Autonomous resource management and execution pipeline.

Engineered to solve continuous configuration drift and manual resource tuning across multi-instance environments.

Multi-instance deployments required continuous manual resource tuning, consuming engineering hours and introducing runtime drift.

Engineered an autonomous resource management agent using a deterministic greedy optimization algorithm in Python with near-zero compute overhead.

Eliminated 95% of manual operational input across 60 instances. Presented at ICRAECA and certified by IFERP.

JWT Auth & Task Management API Server

Production-grade RESTful API server with RS256 token rotation and Redis session blacklisting.

Built to demonstrate backend architecture standards for authentication and session integrity.

Standard tutorial backends often omit refresh token revocation, rate limiting, and structured audit logs.

Engineered in Node.js/Express with PostgreSQL. Implemented RS256 JWT key signing, Redis token blacklisting, and OpenAPI documentation.

Sub-15ms response latency under benchmark load with zero memory leaks across 10k simulated concurrent sessions.