ArticleScientific reports2025
Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
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- Apple Leaf Disease Detection Based on Improved YOLOv11 with DSSA Mechanism.Plants (Basel, Switzerland) · 2026Article
- Resonance peak extraction method based on human ear model and its application in bearing fault diagnosis.Scientific reports · 2025Article
- SHAP enhanced transformer GWO boosting model for transparent and robust anomaly detection in IIoT environments.Scientific reports · 2025Article
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2 authors.
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Abstract
Three machine learning algorithms-Logistic Boosting, Random Forest, and Support Vector Machines (SVM)-were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset of 15,000 instances from factory sensors was analyzed using ROC curves, confusion matrices, and standard metrics. Logistic Boosting outperformed other models with an AUC of 0.992 (96.6% accuracy, 93.5% precision, 94.8% recall, F1-score = 0.941), demonstrating superior handling of imbalanced data (134 FPs, 117 FNs). While Random Forest achieved strong results (AUC = 0.982) and SVM showed high recall, Logistic Boosting's ensemble approach proved most effective for industrial IoT classification. The findings provide actionable insights for real-time detection systems and suggest future directions in hybrid architectures and edge optimization.
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