Recent Applications of Artificial Intelligence in Economy and Society

Abstract submission deadline

30 September 2025

Manuscript submission deadline

30 December 2025

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Topic Information

Dear Colleagues,

Artificial intelligence (AI) is a transformative force reshaping industries, societies, and economies. From advancing decision-making processes to personalizing user experiences, AI enables innovation and addresses global challenges. Its applications span critical sectors, including healthcare, education, agriculture, environmental protection, finance, urban planning, and more. These advancements redefine efficiency, creativity, and problem-solving capabilities, positioning AI as a key driver of sustainable development.

However, this rapid growth raises concerns about data privacy, ethical use, governance, and broader societal impacts. To ensure that AI contributes positively to humanity's future, balancing its opportunities with critical examinations of its risks and implications is crucial.

We invite submissions of high-quality original research and review articles that offer novel perspectives, rigorous analyses, and actionable insights for this Special Issue. The Special Issue will also explore AI's methodological advancements, theoretical breakthroughs, and real-world applications. Contributions should emphasize AI's potential to address pressing global issues while critically engaging with ethical, societal, and governance challenges.

Authors are encouraged to provide empirical evidence, comparative studies, or robust theoretical insights. Papers should include methodologies, evaluation criteria, and discussions of the societal impact. Case studies must demonstrate real-world relevance and measurable outcomes. Submissions addressing ethical and governance challenges are welcome in order to foster critical discourse on the responsible use of AI.

Topics of interest include but are not limited to the following:

• Comparative analyses with state-of-the-art methodologies;
• AI applications in healthcare, education, environmental management, and urban planning;
• Strategies for leveraging AI to achieve the SDGs;
• Practical case studies demonstrating socio-economic and environmental benefits;
• Frameworks for ethical AI development and deployment;
• Data privacy, accountability, and transparency in AI systems;
• Collaborative approaches to bridge technical and social science disciplines;
• AI's influence on workforce dynamics, employment trends, and labor markets;
• Economic effects on consumer behavior, trade, and accounting practices;
• Opportunities and challenges posed by AI in financial systems and management.

Prof. Dr. Claudiu George Bocean
Dr. Anca Antoaneta Vărzaru
Prof. Dr. Domenico Ursino
Prof. Dr. Kah Phooi Seng
Dr. Dorel Berceanu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Smart Cities smartcities	7.0	14.7	2018	28.4 Days	CHF 2000	Submit
Systems systems	2.3	4.1	2013	19.6 Days	CHF 2400	Submit
Economies economies	2.1	4.7	2013	21.9 Days	CHF 1800	Submit

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Published Papers (3 papers)

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All Electronics Smart Cities Systems Economies

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27 pages, 2291 KiB  

Open AccessArticle

Tech Trend Analysis System: Using Large Language Models and Finite State Chain Machines

by Dragoş Florin Sburlan, Cristina Sburlan and Alexandru Bobe

Electronics 2025, 14(11), 2191; https://doi.org/10.3390/electronics14112191 - 28 May 2025

Viewed by 265

Abstract

In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and [...] Read more.

In today’s fast-paced technological environment, spotting emerging trends and anticipating future developments are important tasks in strategic planning and business decision-making. However, the volume and complexity of unstructured data containing relevant information make it very difficult for humans to effectively monitor, analyze, and identify inflection points by themselves. In this paper, we aim to prove the potential of integrating large language models (LLMs) with a novel finite state chain machine (FSCM) with output and graph databases to extract insights from unstructured data, specifically from earnings call transcripts of 40 top Technology Sector companies. The FSCM provides a modular, state-based approach for processing texts, enabling entity and relationship recognition. The extracted information is stored in a knowledge graph, further enabling semantic search and entity clustering. By leveraging this approach, we identified over 20,000 hidden (overlapping) trends and topics across various types. Our experiment on real-world datasets confirms the scalability and effectiveness of the method in extracting valuable knowledge from large datasets. The present work contributes to the field of Natural Language Processing (NLP) by showcasing the proposed method in addressing real-world business problems. The findings shed new light on current trends and challenges faced by tech companies, highlighting the potential for further integration with other NLP methods, leading to more robust and effective outcomes. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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38 pages, 6018 KiB  

Open AccessArticle

Artificial Intelligence Adoption in the European Union: A Data-Driven Cluster Analysis (2021–2024)

by Costel Marian Ionașcu

Economies 2025, 13(5), 145; https://doi.org/10.3390/economies13050145 - 21 May 2025

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Abstract

The adoption of artificial intelligence by enterprises in the EU countries increased significantly between 2021 and 2024, but the recorded values were uneven and very small. This study analyzed the main characteristics of the artificial intelligence adoption process, its dynamics and patterns using [...] Read more.

The adoption of artificial intelligence by enterprises in the EU countries increased significantly between 2021 and 2024, but the recorded values were uneven and very small. This study analyzed the main characteristics of the artificial intelligence adoption process, its dynamics and patterns using principal component analysis and K-means clustering. The results highlighted a shift from using technologies for process automation to more advanced ones like natural language generation. The process was extended and gradually covered almost all business areas. The lack of relevant expertise, high costs and gaps in regulation of the development and use of artificial intelligence are the important barriers identified by 2024. The cluster analysis of EU countries highlighted the existence of two permanent clusters, one containing the leading countries and one containing the countries lagging behind, showing a large gap between them. The increasing dependence on externally developed solutions has characterized a maturing market for artificial intelligence. The equitable adoption of artificial intelligence at the level of EU countries must be based on specific workforce training, investments in infrastructure, financial incentives and, last but not least, on clear regulations. Only in this way can the gap in this area at the EU level be reduced. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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45 pages, 4361 KiB  

Open AccessArticle

Engineering Sustainable Data Architectures for Modern Financial Institutions

by Sergiu-Alexandru Ionescu, Vlad Diaconita and Andreea-Oana Radu

Electronics 2025, 14(8), 1650; https://doi.org/10.3390/electronics14081650 - 19 Apr 2025

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Abstract

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a [...] Read more.

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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