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Artificial Intelligence and Machine Learning Model Implementation for UC Irvine Dataset 322

Year: 2026

Type: preprint

Abstract: The dataset, sourced from the UCI repository, underscores the value of open-source resources in advancing sensor research. In this study, raw sensor data were compressed using polynomial fitting (polyfit), achieving nearly a 100-fold reduction, and subsequently analyzed with the Isolation Forest algorithm to detect anomalies. Python libraries, including scikit-learn, were deployed in an isolated environment to ensure reproducibility and scalability. The workflow was further integrated into Microsoft Copilot, enabling natural language interaction with the AI agent for streamlined big data modeling.

Source: ChemRxiv

DOI: 10.26434/chemrxiv.15011624/v1

Authors: Netra Velu, Alagumeenal Rajagopal, Singaravelu Velayudham

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7225192347

Cites: 11

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Related to: 10