What question did this study set out to answer?

The aim is to present SmartAnom, a framework for anomaly detection in low-code/no-code environments.

February 26, 2026SoftwareX0 citationsOpen Access

SmartAnom: Sigmoid-based anomaly detection in low-code/No-code frameworks

SUSerpil UstebayIstanbul University-Cerrahpaşa

Key Points

The aim is to present SmartAnom, a framework for anomaly detection in low-code/no-code environments.
Open-source framework for evaluating anomaly detection models
Supports data loading, synthetic data generation, and model selection
Incorporates hyperparameter optimization and result visualization
Utilizes deep learning and classical models for anomaly detection
Facilitates easier experimentation in anomaly detection
Enhances interpretability through integrated SHAP analysis
Provides alternatives to classical scoring approaches

Abstract

SmartAnom is an open-source, modular low-code/no-code software framework developed for comparing and evaluating anomaly detection models. It enables users to perform data loading, synthetic data generation, model selection, hyperparameter optimization, and result visualization without requiring any programming knowledge. The system supports Isolation Forest-based methods, as well as deep learning and classical models. The Sigmoid-Based Anomaly Scoring (SBAS), located at the core of SmartAnom, is designed as an alternative to the classical scoring approach used in Isolation Forest-based algorithms. Integrated SHAP analysis enhances interpretability. Overall, SmartAnom simplifies experimentation and bridges research with real-world applications.

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