Topic: LLM
Published:
2025
- DetailsCampanile, L., Zona, R., Perfetti, A., & Rosatelli, F. (2025). An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 501–508. https://doi.org/10.5220/0013519700003944
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. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
2024
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2021
- DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.
2025
- DetailsCampanile, L., Zona, R., Perfetti, A., & Rosatelli, F. (2025). An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 501–508. https://doi.org/10.5220/0013519700003944
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. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
2024
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2021
- DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.