The article is devoted to researching the prospects and possibilities of implementing the concept of a smart city in order to restore the industrial sector of Ukraine. The authors systematically analyze the key aspects of modern smart cities, in particular the use of advanced technologies of the Internet of Things, data analysis, and automation of production processes. The article highlights the potential benefits of implementing a smart grid in Ukrainian industry, such as increasing the efficiency of production processes, optimizing resource use, and creating environmentally friendly technological solutions. In particular, the possibilities of using smart technologies in production, logistics and management, as well as their impact on increasing the competitiveness of enterprises, are considered. The study is based on the study of the experience of implementing a smart grid in other countries and takes into account the specifics of the Ukrainian industrial sector. Thanks to this analysis, the authors provide specific recommendations for the implementation of a smart grid in Ukraine in order to increase its competitiveness and create sustainable economic growth.
AI technology presents new challenges in digital development which necessitates the creation of a regulatory framework for implementation of AI technological solutions. Public discussion in European public institutions, the scientific and professional community additionally contributes to this demand. According to the areas of AI Technology application, the regulation of AI solutions should be focused on specific problems related to implementation in certain parts of digital society. A special part of implementation is regulation of AI technology in public administration. The focus of this paper will be on the implementation ability of the general principle of AI technology regulation in specific solutions in public administration, according to EU AI Act and EU Digital Strategy. The first part is implementation of AI in central government services, the second part is implementation in local government services, and the third one is implementation in interactive communication between government authorities and citizens.
Patent analysis is the process of analyzing patent documents and other information throughout the patent lifecycle to uncover innovation insights and patterns in a particular area or technology field. It provides data-driven, evidence-based insights that allow organizations to make better strategic decisions in areas such as research and development, innovation policies, licensing, and research collaboration. Patent analysis is often carried out in several steps, such as data collection and extraction, analysis and finding patterns, and reporting results. Various methods have been used to analyze patent data in many technological fields. The goal of this paper is twofold: (i) to describe basic concepts and terminology of patent analysis, and (ii) to perform a systematic literature analysis to detect the most common methodology used for conducting patent analysis and to identify the technological fields that have been the focus of patent analyses in the last twenty years. The research is carried out in three steps: first, querying scientific databases; second, analyzing relevant articles within the patent analysis research area; and third, summarizing and reporting the state-of-the-art methodology and technological fields prevalent in patent analysis for the last twenty years.
Artificial intelligence is a global topic that holds significant importance in recent years. It sparks debates about its usage, but despite the contradictions that arise, there are many benefits to applying it in various areas. In this paper, the authors investigate the possibilities of implementing machine learning algorithms to help optimize crop management for precision agriculture. Meeting current food demands becomes an increasingly challenging task as the population grows. Applying machine learning using IoT data analytics in the agricultural sector will bring forth additional advantages, increasing not only the quantity but also the quality of production from crop fields to meet the rising food demands. The main objective of the paper is to employ various machine learning algorithms for predictive modeling and subsequently develop a robust model capable of making accurate predictions on unseen agricultural data. The research methodology involves preprocessing the acquired dataset, including data cleaning and feature engineering to enhance model performance. The authors systematically apply a range of machine learning algorithms, such as Logistic regression, Decision trees, Naïve Bayes, and k-nearest Neighbor, to identify the most effective approach for crop yield prediction. For better model performance, boosting techniques of machine learning are also implemented. Cross-validation and performance metrics such as accuracy, confusion matrix, precision, recall and F1 score, are included to evaluate the accuracy of the models, aiming to highlight the strengths and weaknesses of each algorithm in the context of precision agriculture.
