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Future Internet
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Generative Artificial Intelligence in Smart Societies
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Generative Artificial Intelligence in Smart Societies

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 12388

Special Issue Editors

Dr. Filipe Portela

E-Mail Website
Guest Editor
1. Algoritmi Research Centre, University of Minho, 4800-058 Guimarães, Portugal
2. IOTECH—Innovation on Technology, 4785-588 Trofa, Portugal
Interests: knowledge discovery; data science; progressive web apps; research and development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Athanasios D. Panagopoulos

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, GR-15780 Athens-Zografou, Greece
Interests: communications; wireless communications; radio communications; communications theory; modulations and coding; satellite & space communications; vehicular technology; antennas and propagation; gigabit networking; computer communications; systems and protocols; artificial intelligence techniques in communication data and networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the integration of generative artificial intelligence (AI) technologies has progressively redefined the landscape of smart societies. These technologies harness advanced algorithms to create content, formulate predictions, and simulate human-like interactions, offering profound implications for urban development, healthcare, education, and social interactions. This Special Issue seeks to explore the frontier of generative AI applications within the context of smart societies, aiming to address both the transformative potential of this technology and the emerging challenges. The contributions to this Special Issue will address a wide range of topics, including, but not limited to, the development of generative models for urban planning, the application of AI in predictive healthcare, personalized education through machine learning, and the ethical considerations of AI deployment in public spaces. This Special Issue aims to compile research demonstrating how generative AI can be leveraged to enhance the efficiency, sustainability, and quality of life in urban environments, while also addressing critical issues such as privacy, security, and the socio-economic impacts of AI technology.

We invite authors to submit original research articles, case studies, and comprehensive reviews that enhance our understanding of generative AI in smart societies. This Special Issue welcomes studies combining theoretical frameworks with practical implementations, offering insights into the current state of AI and its potential to foster smarter, more responsive societies. Through this Special Issue, we aim to provide a platform for researchers to share innovations and stimulate further research in this dynamic and crucial field of study.

Dr. Filipe Portela
Prof. Dr. Athanasios D. Panagopoulos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart cities
  • smart societies
  • generative AI
  • artificial intelligence
  • generative models
  • predictive analytics
  • big data
  • AI governance
  • personalized learning and urban planning

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Published Papers (7 papers)

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Research

20 pages, 6941 KiB  
Article
EmoSDS: Unified Emotionally Adaptive Spoken Dialogue System Using Self-Supervised Speech Representations
by Jaehwan Lee, Youngjun Sim, Jinyou Kim and Young-Joo Suh
Future Internet 2025, 17(4), 143; https://doi.org/10.3390/fi17040143 - 25 Mar 2025
Viewed by 415
Abstract
In recent years, advancements in artificial intelligence, speech, and natural language processing technology have enhanced spoken dialogue systems (SDSs), enabling natural, voice-based human–computer interaction. However, discrete, token-based LLMs in emotionally adaptive SDSs focus on lexical content while overlooking essential paralinguistic cues for emotion [...] Read more.
In recent years, advancements in artificial intelligence, speech, and natural language processing technology have enhanced spoken dialogue systems (SDSs), enabling natural, voice-based human–computer interaction. However, discrete, token-based LLMs in emotionally adaptive SDSs focus on lexical content while overlooking essential paralinguistic cues for emotion expression. Existing methods use external emotion predictors to compensate for this but introduce computational overhead and fail to fully integrate paralinguistic features with linguistic context. Moreover, the lack of high-quality emotional speech datasets limits models’ ability to learn expressive emotional cues. To address these challenges, we propose EmoSDS, a unified SDS framework that integrates speech and emotion recognition by leveraging self-supervised learning (SSL) features. Our three-stage training pipeline enables the LLM to learn both discrete linguistic content and continuous paralinguistic features, improving emotional expressiveness and response naturalness. Additionally, we construct EmoSC, a dataset combining GPT-generated dialogues with emotional voice conversion data, ensuring greater emotional diversity and a balanced sample distribution across emotion categories. The experimental results show that EmoSDS outperforms existing models in emotional alignment and response generation, achieving a minimum 2.9% increase in text generation metrics, enhancing the LLM’s ability to interpret emotional and textual cues for more expressive and contextually appropriate responses. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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20 pages, 2995 KiB  
Article
Explainable Identification of Similarities Between Entities for Discovery in Large Text
by Akhil Joshi, Sai Teja Erukude and Lior Shamir
Future Internet 2025, 17(4), 135; https://doi.org/10.3390/fi17040135 - 22 Mar 2025
Viewed by 374
Abstract
With the availability of a virtually infinite number of text documents in digital format, automatic comparison of textual data is essential for extracting meaningful insights that are difficult to identify manually. Many existing tools, including AI and large language models, struggle to provide [...] Read more.
With the availability of a virtually infinite number of text documents in digital format, automatic comparison of textual data is essential for extracting meaningful insights that are difficult to identify manually. Many existing tools, including AI and large language models, struggle to provide precise and explainable insights into textual similarities. In many cases, they determine the similarity between documents as reflected by the text, rather than the similarities between the subjects being discussed in these documents. This study addresses these limitations by developing an n-gram analysis framework designed to compare documents automatically and uncover explainable similarities. A scoring formula is applied to assigns each of the n-grams with a weight, where the weight is higher when the n-grams are more frequent in both documents, but is penalized when the n-grams are more frequent in the English language. Visualization tools like word clouds enhance the representation of these patterns, providing clearer insights. The findings demonstrate that this framework effectively uncovers similarities between text documents, offering explainable insights that are often difficult to identify manually. This non-parametric approach provides a deterministic solution for identifying similarities across various fields, including biographies, scientific literature, historical texts, and more. Code for the method is publicly available. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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28 pages, 16471 KiB  
Article
An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design
by J. de Curtò, I. de Zarzà, Leandro Sebastián Fervier, Victoria Sanagustín-Fons and Carlos T. Calafate
Future Internet 2025, 17(3), 96; https://doi.org/10.3390/fi17030096 - 20 Feb 2025
Cited by 1 | Viewed by 887
Abstract
This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of [...] Read more.
This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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27 pages, 1091 KiB  
Article
Explainable Security Requirements Classification Through Transformer Models
by Luca Petrillo, Fabio Martinelli, Antonella Santone and Francesco Mercaldo
Future Internet 2025, 17(1), 15; https://doi.org/10.3390/fi17010015 - 3 Jan 2025
Cited by 2 | Viewed by 1126
Abstract
Security and non-security requirements are two critical issues in software development. Classifying requirements is crucial as it aids in recalling security needs during the early stages of development, ultimately leading to enhanced security in the final software solution. However, it remains a challenging [...] Read more.
Security and non-security requirements are two critical issues in software development. Classifying requirements is crucial as it aids in recalling security needs during the early stages of development, ultimately leading to enhanced security in the final software solution. However, it remains a challenging task to classify requirements into security and non-security categories automatically. In this work, we propose a novel method for automatically classifying software requirements using transformer models to address these challenges. In this work, we fine-tuned four pre-trained transformers using four datasets (the original one and the three augmented versions). In addition, we employ few-shot learning techniques by leveraging transfer learning models, explicitly utilizing pre-trained architectures. The study demonstrates that these models can effectively classify security requirements with reasonable accuracy, precision, recall, and F1-score, demonstrating that the fine-tuning and SetFit can help smaller models generalize, making them suitable for enhancing security processes in the Software Development Cycle. Finally, we introduced the explainability of fine-tuned models to elucidate how each model extracts and interprets critical information from input sequences through attention visualization heatmaps. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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26 pages, 1559 KiB  
Article
Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
by Markus Hornsteiner, Michael Kreussel, Christoph Steindl, Fabian Ebner, Philip Empl and Stefan Schönig
Future Internet 2024, 16(12), 438; https://doi.org/10.3390/fi16120438 - 22 Nov 2024
Cited by 2 | Viewed by 2905
Abstract
Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap [...] Read more.
Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap in field of Text-to-Cypher queries, focusing on Neo4j. Using the Design Science Research Methodology, we developed a Natural Language Interface which can receive user queries in real time, convert them into Cypher Query Language (CQL), and perform targeted queries, allowing users to choose from different graph databases. In addition, the user interaction is expanded by an additional chat function based on the chat history, as well as an error correction module, which elevates the precision of the generated Cypher statements. Our findings show that the chatbot is able to accurately and efficiently solve the tasks of database selection, chat history referencing, and CQL query generation. The developed system therefore makes an important contribution to enhanced interaction with graph databases, and provides a basis for the integration of further and multiple database technologies and LLMs, due to its modular pipeline architecture. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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25 pages, 2612 KiB  
Article
Measuring the Effectiveness of Carbon-Aware AI Training Strategies in Cloud Instances: A Confirmation Study
by Roberto Vergallo and Luca Mainetti
Future Internet 2024, 16(9), 334; https://doi.org/10.3390/fi16090334 - 13 Sep 2024
Cited by 1 | Viewed by 1909
Abstract
While the massive adoption of Artificial Intelligence (AI) is threatening the environment, new research efforts begin to be employed to measure and mitigate the carbon footprint of both training and inference phases. In this domain, two carbon-aware training strategies have been proposed in [...] Read more.
While the massive adoption of Artificial Intelligence (AI) is threatening the environment, new research efforts begin to be employed to measure and mitigate the carbon footprint of both training and inference phases. In this domain, two carbon-aware training strategies have been proposed in the literature: Flexible Start and Pause & Resume. Such strategies—natively Cloud-based—use the time resource to postpone or pause the training algorithm when the carbon intensity reaches a threshold. While such strategies have proved to achieve interesting results on a benchmark of modern models covering Natural Language Processing (NLP) and computer vision applications and a wide range of model sizes (up to 6.1B parameters), it is still unclear whether such results may hold also with different algorithms and in different geographical regions. In this confirmation study, we use the same methodology as the state-of-the-art strategies to recompute the saving in carbon emissions of Flexible Start and Pause & Resume in the Anomaly Detection (AD) domain. Results confirm their effectiveness in two specific conditions, but the percentage reduction behaves differently compared with what is stated in the existing literature. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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17 pages, 2937 KiB  
Article
Emotion Recognition from Videos Using Multimodal Large Language Models
by Lorenzo Vaiani, Luca Cagliero and Paolo Garza
Future Internet 2024, 16(7), 247; https://doi.org/10.3390/fi16070247 - 13 Jul 2024
Cited by 2 | Viewed by 3820
Abstract
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data [...] Read more.
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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