There is a need to develop effective curricula based on real-life cases that can be used to broaden students’ participation and motivate them during their studies. Real-life case examples are crucial to justify the learning of the respective discipline. In our study, we explore the application of image processing using Python in the context of processing ancient manuscripts, particularly palimpsests. Palimpsests are invaluable historical manuscripts, often rewritten and reused over time. They pose challenges for researchers due to legibility and interpretation issues. Python and AI can help decipher them. This interdisciplinary study not only introduces students to applied AI techniques, but also enriches their practice with the use of various image preprocessing techniques to enhance the readability of palimpsests. Specific methods such as Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gaussian smoothing are detailed to demonstrate various preprocessing strategies. Current educational trends in Applied Artificial Intelligence (AAI) are discussed in this paper. Experimental techniques are elucidated through example code implementations showing the practical application of these technologies. The research contributes to the advancement of both educational and scientific applications by introducing learners to the history of their country through the analysis of ancient manuscripts. By engaging with these materials, students gain practical skills in AI while deepening their historical knowledge, fostering a holistic and interdisciplinary approach to learning.

The palimpsests represent unique historical sources that hold the potential for new insights in the field of human history. These manuscripts, rewritten and reused over time, pose challenges in the research related to their readability and interpretation. The present study aims to investigate the readability of palimpsests through the use of image preprocessing techniques. The article focuses on methods for the preprocessing of palimpsests that could lead to a significant improvement in the readability of the ‘hidden’ text. The challenges encountered during the processing of palimpsests are explored and various techniques applicable to improving the readability of these manuscripts are analyzed.

The primary goal of preprocessing in this context is to separate the ‘hidden’ text from the visible one, neutralizing material defects and aging. The article presents specific methods such as extracting specific color range from an palimsest image. The experimental techniques are highlighted with sample codes illustrating the application of the respective technology. The current research attempts to advance the development of methods for processing palimpsests and opens up new perspectives for extracting information from those historically valuable manuscripts.

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 use of data derived from Brain Computer Interaction (BCI) is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and other related fields. In this study, we propose the quantitative analysis method for feature selection based on descriptive statistics (maximum, minimum, mean and median).The purpose of this paper is to show that different trials of the same individual at the same symbol yield similar brain profiles regardless of their mental state, and that different individuals have different brain profiles at the same visual signals.

The obtained data are discussed in the context of studying the nature of brain signals.

The study of brain signals based on efficient computing is a new beginning in the research field aimed at finding a connection between human emotions and recorded BCI signals. Analysis of incoming brain signals and techniques for processing and classifying information are actively researched. Investigating these signals is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and more. This research focuses on the classification of brain-computer interface (BCI) data using Bayesian analysis. The aim of the paper is to study the classification of brain signals and the possibilities of reducing the number of channels. Experimental data were obtained using Emotiv Epoc 14+. The R programming language was used to process the data. The data were classified using Bayesian analysis in R.

This article is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and examines the study of practical solutions implemented using applied artificial intelligence. The research was done by preparing an online survey containing a total of 7 questions, open and closed. The purpose of the study is to find real working applications of applied artificial intelligence projects, describe their application in what field, and record the name of the projects found to describe their activity. The study was done by looking at cases all over the world. The analysis of the data provides insight in several directions:-in which countries are more real cases of artificial intelligence solutions used-what is the distribution of realized cases – depending on whether the country is a member of the EU or not EU.-In what category is the real case developed.-whether the country of the real case works in collaboration with other countries or implements the real case only the country.The research and analysis done provide a clear picture of the developed projects using artificial intelligence. The obtained results will guide in what areas to organize the practical training. Also, the research would help future AI application developers.