Information system based on a complex model using machine learning for spectral analysis

The research is devoted to the design and development of an information system based on a complex model using machine learning methods to automate spectral analysis to increase the accuracy and speed of data processing. The history of the research is connected with the development of analytical methods in physics, chemistry and biology, where spectral analysis has traditionally played a key role. However, modern challenges, in particular the growth of data volumes and the need for automation, have stimulated the introduction of innovative methods based on artificial intelligence. The relevance is due to the need to process large volumes of complex spectral data in real time, which is important for medicine, ecology, chemistry and other industries. Traditional analysis methods have limitations, so the use of machine learning is appropriate to increase the efficiency of the process. The research focuses on the following issues: how to automate spectral data processing, how to ensure the integration of classical methods with machine learning, and how to increase the accuracy and scalability of the analysis. For this purpose, signal processing methods were applied, including noise filtering, smoothing, baseline correction, and peak analysis using derivatives and numerical integration. Machine learning was implemented through Random Forest models and neural networks adapted for predicting spectrum parameters. The results showed that the developed system provides high accuracy and speed of spectral data analysis, interactive visualization of spectrum parameters, as well as the ability to integrate with other information platforms. This significantly simplifies analysis processes, reduces dependence on expert intervention, and increases productivity. Research prospects include optimizing mathematical models for even greater accuracy, integration with IoT systems, and expanding the functionality for analyzing complex multidimensional spectra. This opens up opportunities for application in interdisciplinary projects, such as monitoring environmental changes or diagnosing medical conditions.