## 2 Materials and Methods
### Computational Methods  
In this study, we used MLNET-based models in Visual Studio 2022 (Microsoft, Tulsa, OK, USA). This is based on the authors' experience and the fact that C# is still a strong option for building mobile, desktop, and web applications (including industrial applications), and the ML.NET platform helps to implement machine learning into these systems.ML.NET Model Builder provides a visual interface for building, training, and deploying custom ML models and no prior ML experience is required. NET capabilities can run offline using local data or locally stored models, meaning ML.NET can be an ideal option for on-device ML scenarios or scenarios involving critical security or cost factors. While.NET provides robust frameworks and libraries for machine learning, Python has a vast ecosystem of libraries and tools designed specifically for data science and machine learning. Developers often use.NET for general application development and leverage Python for ML-specific tasks but ML.NET is faster and easier to use in industrial applications. By default, AutoML supports binary classification, multiclass classification, regression, and many other scenarios: recommendation, forecasting. ML.NET provides APIs to train custom deep learning models and use them for more advanced predictions.ML.NET also uses Tensorflow.NET as one of the backends for model training and inference, which provides better integration with the.NET platform.  
C# and ML.NET remain highly effective and widely adopted options for developers looking to integrate machine learning (ML) into industrial, desktop, web, and mobile applications. The.NET ecosystem is widely used across sectors, with C# being used by over 25% of developers worldwide, making it one of the most widely used frameworks, especially for desktop and enterprise software development. Its continued popularity is attributed to its compatibility, ease of use, and ability to integrate with other.NETbased applications. In industrial applications, ML.NET offers unique advantages through its compatibility with.NET and seamless integration with existing.NET environments, which is a significant advantage over some Python-based ML tools (e.g., TensorFlow or Scikit-learn). Industries like healthcare, automotive, finance, and manufacturing are using ML.NET because of its easy implementation in cloud services like Azure and its ability to handle industry-specific tasks like anomaly detection, predictive maintenance, and real-time decision-making. Additionally, ML.NET's AutoML feature accelerates the model development process by automating hyperparameter tuning and minimizing the time to deploy models to production. In terms of performance, ML.NET is highly optimized for.NET applications, which means faster model training and lower latency in real-time applications compared to deploying external ML frameworks. This optimization, combined with the availability of tools like Model Builder and native language support (C# and F#), makes ML.NET a practical choice for companies that want to avoid complex integrations or retraining teams in new ML-specific languages. Therefore, C# and ML.NET offer a competitive solution in terms of performance, integration, and ease of use, making them particularly suitable for enterprise applications where existing.NET infrastructure and resources can be used for ML workflows.