Hybrid model for sentiment analysis based on both text and audio data trained on MELD

The complexity of subject areas in which intelligent information systems operate is steadily increasing. Tasks assigned to smart agriculture systems are increasingly focused on automating and robotizing areas of human activity. Solving such tasks requires adaptive and flexible methods capable of accommodating dynamic changes in the environment in real-time. The mivar approach to creating intelligent decision-making systems enables working with adaptive discrete structures and provides methods for managerial decision-making based on adaptive active logical inference from the mivar rule knowledge base. The mivar logical inference machine forms the core of expert systems based on the mivar approach. As a result of the development of the mivar approach across various subject areas, different versions of mivar logical inference machines with their algorithms for rule traversal in the knowledge base have been created. Recent advancements in artificial intelligence and machine learning have opened new opportunities for enhancing the mivar approach. The integration of large language models for automating text processing in mivar systems significantly enhances the accuracy and efficiency of decision-making processes based on expert systems for sustainable agriculture. This paper demonstrates the feasibility of using automated text processing, intended for human training, through large language models, and its subsequent application in action planning tasks within technical systems. The proposed methodology is aimed at creating extensive knowledge bases based on textual information for real-time monitoring and decision-making in smart agriculture systems.

Text processing using LLM for automatic creation of agricultural crops knowledge bases

Andreev,

Kotsenko,

Varlamov

et al. 2024

Mivar-based route planning simulation model for obstacle-aware autonomous agricultural machinery

Kotsenko,

Varlamov,

Denisov

et al. 2024

Autonomous robot navigation is increasingly becoming an important task that requires solutions. This paper explores the practical application of logical artificial intelligence to address the problem of route planning, using the example of a computer game. Within the scope of this work, a knowledge base model was created, and a new version of the mivar reasoner was developed for integration with Unity. This reasoner allows the activation of logical rules with linear complexity. As a result, a system was developed that processes user input for position and moves an autonomous agent in a virtual environment according to the rules. This work confirms the feasibility of using mivar technologies to improve control systems of autonomous robots in the area of agriculture. The study also emphasizes the adaptability of mivar networks in dynamic environments, demonstrating their ability to effectively process changes in real-time. This research shows enhanced decision-making capabilities and reliable navigation strategies for autonomous agents, setting a precedent for future developments in autonomous digital solutions for agriculture. The results obtained open new prospects for the advancement of technologies in the field of autonomous navigation.

Automated formation of discipline sequences for higher ecological and agricultural education using mivar expert systems

Peresypkina,

Varlamov,

Kotsenko

et al. 2024

Training qualified specialists for agriculture, ecology and industry is becoming increasingly important in today’s rapidly changing world. The constant development of science and technology leads to an expansion of the required knowledge, which creates difficulties for students in assimilating huge amounts of information in a limited time. This discrepancy requires constant updating and improvement of educational programs, ensuring the inclusion of relevant courses and workshops, and maintaining a logical sequence to prevent knowledge gaps. At Bauman Moscow State Technical University (BMSTU), about 25,000 students study in more than 600 programs, including ecology and forestry, using the Electronic University system for automated management of educational processes. Logical AI helps in planning individual educational trajectories, improving decision-making and quality control, especially in the field of agriculture and ecology. The development of mivar networks for educational programs further optimizes management, an example of which is the construction of mivar networks for specific courses. This approach solves the problem of managing large unstructured volumes of data, providing a model for transforming input knowledge into competencies. The integration of mivar expert systems offers a structured method for sequencing courses, ultimately improving the educational structure at Bauman Moscow State Technical University.