AI4EIoT 2025 Abstracts

Area 1 - Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives

Full Papers

Paper Nr:	8
Title:	eXplainable Artificial Intelligence Framework for Structure's Limit Load Extimation
Authors:	Habib Imani, Renato Zona, Armando Arcieri, Luigi Piero Di Bonito, Simone Palladino and Vincenzo Minutolo
Abstract:	The recent advancements in machine learning (ML) and deep learning (DL) have significantly expanded opportunities across various fields. While ML is a powerful tool applicable to numerous disciplines, its direct implementation in civil engineering poses challenges. ML models often fail to perform reliably in real-world scenarios due to lack of transparency and explainability during the decision-making process of the algorithm. To address this, physics-based ML models integrate data obtained through a finite element procedure based on the lower bound theorem of limit analysis, ensuring compliance with physical laws described by general nonlinear equations. These models are designed to handle supervised learning tasks while mitigating the effects of data shift. Widely recognized for their applications in disciplines such as fluid dynamics, quantum mechanics, computational resources, and data storage, physics-based ML is increasingly being explored in civil engineering. In this work, a novel methodology that combines machine learning and computational mechanics to evaluate the seismic vulnerability of existing buildings is proposed. Interesting and affordable results are reported in the paper concerning the predictability of limit load of structure through ML approaches. The aim is to provide a practical tool for professionals, enabling efficient maintenance of the built environment and facilitating the organization of interventions in response to natural disasters such as earthquakes.
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Paper Nr:	9
Title:	Towards a Digital Twin of the Cardiovascular System
Authors:	Ciro Nespolino, Roberta De Fazio, Laura Verde and Stefano Marrone
Abstract:	As medicine aims to become smarter, more pervasive, and more personalised, the concept of the Digital Twin has become a cornerstone of the entire base and applied research. The advantages of having Digital Twins to understand, predict and communicate complex mechanisms and functionalities have become of paramount importance in modern and future medicine. This paper presents an approach for the construction of a Digital Twin for the cardiovascular system. The approach, with the objective of being as lightweight and explainable as possible, is based on the integration of partial differential equation models and of realistic data. This integration can overcome both the rigidity of traditional model-based methods and the computational demands of modern deep learning approaches. A technical integration of a smart backend with a frontend based on virtual reality visor is presented in the paper.
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Short Papers

Paper Nr:	6
Title:	Enhancing Accuracy and Efficiency in Physical Count Processes: Leveraging AI, IoT, and Automation for Real-Time Inventory Management in Supply Chain
Authors:	Prabhakaran Rajendran, Nirmal Kumar Balaraman and Hareesh Viswanathan
Abstract:	This paper aims at studying how much AI, IoT, and automation play a crucial role in improving the calibration and effectiveness of physical inventory count exercises. As supply chain networks become enhanced, companies are using these technologies to counter issues that come with the use of enhanced inventory control including but not limited to errors, slowness among others. About this, the present paper examines two different case studies one, based on a well-known logistics company in Finland, and the other, Amazon’s fulfillment centers exploring how the application of AI, IoT and automation enhance real-time inventory management. The study informs that the adoption of these technologies greatly improves both the integrity and efficiency of inventory data, accurate real-time monitoring, and less reliance on manual adjustments, and streamlines warehouse logistics. This paper fills the existing literature gap in understanding technological advancements in inventory management and provides valuable recommendations to companies that wish to transform in the context of the Fourth Industrial Revolution.
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Paper Nr:	7
Title:	An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact
Authors:	Lelio Campanile, Renato Zona, Antonio Perfetti and Franco Rosatelli
Abstract:	The rapid expansion of the Internet of Things has led to a surge in patent filings, creating challenges in evaluating their relevance and potential impact. Traditional patent assessment methods, relying on manual review and keyword-based searches, are increasingly inadequate for analyzing the complexity of emerging IoT technologies. In this paper, we propose an AI-driven methodology for patent evaluation that leverages Large Language Models and machine learning techniques to assess patent relevance and estimate future impact. Our framework integrates advanced Natural Language Processing techniques with structured patent metadata to establish a systematic approach to patent analysis. The methodology consists of three key components: (1) feature extraction from patent text using LLM embeddings and conventional NLP methods, (2) relevance classification and clustering to identify emerging technological trends, and (3) an initial formulation of impact estimation based on semantic similarity and citation patterns. While this study focuses primarily on defining the methodology, we include a minimal validation on a sample dataset to illustrate its feasibility and potential. The proposed approach lays the groundwork for a scalable, automated patent evaluation system, with future research directions aimed at refining impact prediction models and expanding empirical validation.
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Paper Nr:	11
Title:	Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges
Authors:	Fabio Napoli, Lelio Campanile, Giovanni De Gregorio and Stefano Marrone
Abstract:	Quantum Computing is becoming a central point of discussion in both academic and industrial communities. Quantum Machine Learning is one of the most promising subfields of this technology, in particular for image classification. In this paper, the model of Quantum Convolutional Neural Networks and some related implementations are explored in their potential for a non-trivial task of image classification. The paper presents some experimentations and discusses the limitations and the strengths of these approaches when compared with classical Convolutional Neural Networks. Furthermore, an analysis of the impact of the noise level on the quality of the classification task has been performed. This paper reports a substantial equivalence of the perfomance of the model with respect the level of noise.
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