Automated System-Cognitive Analysis in Agronomy

Lutsenko, Y. V.

doi:10.21515/1990-4665-136-011

Cited by 1 publication

(2 citation statements)

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“…Then the initial data on the project in a particular field needs to be added. In the Eidos system, it is necessary to perform an ASC analysis because the result classifies the predicted risk level of the project being implemented [28]. The Eidos system's knowledge models are fuzzy declarative hybrid models that incorporate certain advantageous aspects of both neural network and frame models of knowledge representation.…”

Section: Resultsmentioning

confidence: 99%

“…The Eidos system visualizes non-local neurons as unique graphic shapes, where the dendritic color and thickness indicate the direction and intensity of the impact of neuron receptors on the degree of activation or inhibition. The method for predicting an object's future states may be seen visually thanks to non-local neurons [31][32][33]. The future state of the control object is represented by a non-local neuron, which indicates the intensity and direction of the variables that are significantly impacting it [34][35][36].…”

Section: Resultsmentioning

confidence: 99%

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Intelligent Risk Analysis of Investment Projects in the Extractive Industry

Al-Ansi,

Garad,

Ryabtsev

2024

JII

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This study introduces an advanced technology for risk analysis in investment projects within the extractive industry, specifically focusing on innovative mining ventures. The research primarily investigates various determinants influencing project risks, including production efficiency, cost, informational content, resource potential, organizational structure, external environmental influences, and environmental impacts. In addressing the research challenge, system-cognitive models from the Eidos intellectual framework are employed. These models quantitatively reflect the informational content observed across different gradations of descriptive scales, predicting the transition of the modelled object into a state corresponding to specific class gradations. A comprehensive analysis of strengths, weaknesses, opportunities and threats (SWOT) has been conducted, unveiling the dynamic interplay of development factors against the backdrop of threats and opportunities within mineral deposits exploitation projects. This analysis facilitates the identification of critical problem areas, bottlenecks, prospects, and risks, considering environmental considerations. The application of this novel intelligent technology significantly streamlines the development process for mining investment projects, guiding the selection of ventures that promise enhanced production efficiency, cost reduction, and minimized environmental harm. The methodological approach adopted in this study aligns with the highest standards of academic rigour, ensuring consistency in the use of professional terminology throughout the article and adhering to the stylistic and structural norms prevalent in leading academic journals. By leveraging an intelligent, systematic framework for risk analysis, this research contributes valuable insights into optimizing investment decisions in the mining sector, emphasizing sustainability and economic viability.

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Section: Resultsmentioning

confidence: 99%