In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries.

In the early decades of the twenty-first century, the application of artificial intelligence has been expanding across all sectors of society, including industrial energy systems. This paper emphasizes the significance of integrating artificial neural networks into boilers with automatic firing, as part of a research project currently in its fifth year of experimental validation. The implementation of neural networks in such systems has demonstrated promising results in the domain of risk management, particularly through the prediction of system malfunctions and their proactive elimination via software interventions. The application of AI-based solutions in boiler control not only contributes to the reduction of environmental impact but also enhances operational safety by preventing accidents that may endanger human health and cause material losses.

In this work, a method for automated generating grammatically correct sentences in Serbian language has been presented. This work presented a significant challenge, as the Serbian language is a highly inflected language, with complex word morphology, noun and adjective genders and declinations, verbal conjugations and concordance rules. The word components, such as word roots and their inflectional and morphological particles have been stored in JSON structures. The word components have been combined using custom produced software in Python programming language. The main software functions are the access to JSON data base of linguistic data, and the execution of algorithms for combining word parts into grammatically correct words, and words into syntactically and semantically correct sentences. The principal features of the software include morphological formation of verbs, nouns and adjectives, and combining these words with prepositions in a way to form sentences that appear to belong to the natural language use in Serbian language. The examples of generated sentences by this method show that such sentences, albeit somewhat simple, can be successfully generated by using this approach. The applications of the method presented here are numerous: from educational use, e.g. in language training, to more general research tools in the domain of natural language processing (NLP), not only for the Serbian language, but for a wider family of languages following complex grammatical rules, such as highly inflected, and morphologically complex languages.

In today’s globalised society, where more and more of the goods and services are being routed automatically, there is a growing necessity to use a global location coding system. The longitude and the latitude are global indicators of dimensionless points on the sphere, but, in reality, most locations of practical interest could be considered as areas covering a definite surface. It may be argued that a point within that surface may be used as a location indicator, but this indicator is not unique. The other issue with the geographical coordinates is that to the coordinate of any particular point of interest a tolerance interval need be included. Thus, location indication through area codes is natural and more useful. In this work, the authors propose a global system for indicating geographic areas through limited sequences of decimal digits. Each such sequence of a certain length indicate in a unique manner a definite geographic area. Sequences of increasing lengths indicate the smaller areas, i.e. with a narrower tolerance. This method for location coding is achieved by dividing recursively the surface of the Earth in sections, and then recursively into smaller and smaller subsections, with each new division encoded by a single digit. E.g. a twelve digit sequence indicates an area of about 50 m in diameter. Using such global coding system for geographic locations may substantially simplify the delivery of goods and services, direct and focus a remedial effort in emergencies, or be used advantageously for planning and improvement of city management.

This paper examines the application of artificial intelligence in automated boiler systems with the aim of improving environmental risk management and reducing emissions generated during combustion processes. The study focuses on the use of neural network models as intelligent monitoring and predictive control tools in industrial heating systems. The main objective of the research is to evaluate how artificial intelligence can support safer and more efficient operation of automated boilers while contributing to lower fuel consumption and reduced environmental impact. The research methodology is based on experimental data collected from an automated boiler system of the OZON 55 type equipped with sensor-based monitoring devices. Operational parameters such as temperature, air supply, fuel characteristics, and gas emissions were recorded and analyzed using recurrent neural network models designed to predict deviations in combustion behavior. The obtained results indicate that neural network–based predictive monitoring can detect anomalies in operational parameters at an early stage and enable timely adjustments of combustion conditions. Such improvements contribute to increased operational safety, improved fuel efficiency, and lower emissions of harmful gases. The findings suggest that the integration of artificial intelligence into automated boiler systems represents an effective technological approach for enhancing environmental protection, improving risk management, and supporting more sustainable energy use in industrial heating systems.