The work is devoted to designing a manufacturing network incorporating logistic-production sites that are located at the nodes of the squared lattice with the help of the AI technique. We focused on qualitative analysis of the dynamic behavior of the dynamic lattice model. The model includes rate constants and initial conditions affecting the trajectories of the model which can be classified either as a stable node, limit cycle, or chaotic attractor. We aim to solve the problem of the model qualitative behavior as an AI classification problem. The training dataset is constructed with the help of Monte-Carlo simulation with high-performance computing in Julia. The AI model is built as a C5.0 decision tree. The work was fulfilled with the framework of Erasmus+ Project No. 2022-1-PL01- KA220-HED000088359 entitled “The Future is in Applied Artificial Intelligence” (FAAI) and offers a use case to be studied during the applied AI training course.

Full paper can be found at:https://ieeexplore.ieee.org/document/10316176

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We comprehensively examine the efficacy of LSTM models in predicting financial time series. We evaluate the performance of LSTM networks based on various numbers of units determined by temporal granularity, considering aspects such as prediction accuracy. This study contributes to the ongoing discourse on the role of AI in financial markets, offeringa nuanced perspective on the practicality and limitations of LSTM models in this critical domain.

Full paper can be found at: https://ieeexplore.ieee.org/document/10316038

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This article is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence (FAAI). It gives overview of current job market related to the field of Applied Artificial Intelligence. The data is obtained from online survey, and it gives highlights of severalaspects of labor market divided into research and analysis of the market, and specific requirements necessary. Regarding research and analysis, the data provided deals with:

– positions offered in the market.

– machine learning problems occurring.

– models being developed while resolving the realworld problem.

– machine learning tasks to be solved.

The collected data in the domain of job market requirements gives highlight about:

– required programming languages.

– educational requirements.

– required competencies.

Results given can serve as a guide to which competencies are necessary in the field of AAI and provide information for both professionals and curriculum creators.

Full paper can be found at: https://ieeexplore.ieee.org/document/10316155

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This article is a contribution within the Erasmus+ project titled “The Future Lies in Applied Artificial Intelligence(FAAI) and examines research of collected IT specifications of good practices in Area of Artificial Intelligence (AAI). The article describes research conducted, the purpose of which is to find IT specifications of good practices in AI and describe their characteristics, like an area of implementation of the AI solution, the result of processing the data, the source of data, Data processing, and quality, what tools are used for processing data, and others. AAI application cases and the technologies used for implementation are reviewed. The specifics of the data and the applications used are described. The examination of these technologies will provide insight into which ones are favored and provide an overview of what is commonly referred to as “best practices” in this particular domain.The research encompassed a global examination of cases. The analysis of the data offers valuable insights in various directions:

 Application area of ML/AI

 Type of machine learning problems in described good

practices in Artificial Intelligence

 Type of models were developed within the projects

 What is the area of implementation of AI solution

 Used AI libraries (frameworks).

 Source of data

 Data characteristics

 Tools are used to store data

 What platform solution is used

 What type of storage is used.

 

The full paper can be found at: https://ieeexplore.ieee.org/document/10316145

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The work is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and devoted to the development the methodology for collecting and analyzing good practices in the field of applied artificial intelligence (AAI) regarding the competences, training, existing solutions and real cases, which can be used for developing training courses of competence based education. Here we propose the definition of good practice in the field of AAI together with the corresponding criteria and features. The offered methodology uses system research based on the data gathered from existing training courses in AAI, labor market, surveys filled in by academics, students and employers, AAI use cases in science and industry.

The full paper can be found at: https://ieeexplore.ieee.org/document/10316104

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The article is devoted to the problem of developing a mathematical model of the response of a potentiometric biosensor for the determination of α-chaconine in the form of a system of seven differential equations that describe the dynamics of biochemical reactions during the full cycle of α-chaconine concentration measurement. At the same time, each of the differential equations establishes the concentration dependence of substrate, enzyme, inhibitor, enzyme-substrate, product, enzyme-inhibitor, enzyme-substrate-inhibitor complexes as a function of time. The mathematical model of the biosensor for the determination of α-chaconine was solved numerically in the R package. The input parameters of the system were used, namely, the concentrations of the enzyme, substrate, and inhibitor (5.8×10-4 M butyrylcholinesterase, 1×10-3 M butyrylcholine chloride, and 1×10−6; 2×10−6; 5×10−6; 10×10−6 M of α-chaconine, respectively), which are measured during experiments. To verify the model and compare it with the experimental response a potentiometric biosensor based on immobilized butyrylcholine chloride was used. Selection of direct and inverse rate constants of enzymatic reactions was carried out in such a way that the result of numerical modeling corresponded as much as possible to the experimental response of the studied biosensor. A comparative analysis of the experimental and simulated responses of the biosensor for the determination of αchaconine was established. It was found that the absolute error does not exceed 0.045 units. As a result of computer simullation, it was concluded that the developed kinetic model of the potentiometric biosensor makes it possible to identify all the main components that were measured this study.

The full paper can be found here: https://ceur-ws.org/Vol-3468/paper1.pdf

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