<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI | Joshua Seyi Ibitoye</title><link>https://jsibitoye.com/tags/ai/</link><atom:link href="https://jsibitoye.com/tags/ai/index.xml" rel="self" type="application/rss+xml"/><description>AI</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Jun 2026 13:00:00 +0000</lastBuildDate><image><url>https://jsibitoye.com/media/icon_hu_982c5d63a71b2961.png</url><title>AI</title><link>https://jsibitoye.com/tags/ai/</link></image><item><title>Credit Card Fraud Detection Using Machine Learning: An AI-Driven Approach for Financial Security</title><link>https://jsibitoye.com/events/ieee_2026_conference/</link><pubDate>Mon, 01 Jun 2026 13:00:00 +0000</pubDate><guid>https://jsibitoye.com/events/ieee_2026_conference/</guid><description>&lt;p&gt;This presentation explains how supervised machine-learning models can support fraud detection in highly imbalanced financial datasets, with emphasis on recall, explainability, and operational security value.&lt;/p&gt;</description></item><item><title>Self-Healing Network Prototype</title><link>https://jsibitoye.com/projects/self-healing-network-prototype/</link><pubDate>Thu, 07 Aug 2025 00:00:00 +0000</pubDate><guid>https://jsibitoye.com/projects/self-healing-network-prototype/</guid><description>&lt;p&gt;Developed a prototype architecture for networks that can detect disruptions, identify likely root causes, and trigger recovery workflows with less manual triage.&lt;/p&gt;
&lt;p&gt;The design combines telemetry, root-cause analysis, anomaly detection, and automated remediation patterns to make recovery faster and more consistent during cyber incidents.&lt;/p&gt;</description></item></channel></rss>