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Future Internet
Special Issues
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: 30 November 2024 | Viewed by 385

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

Published Papers (1 paper)

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Research

17 pages, 2939 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
Viewed by 216
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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