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Hazelnut mapping detection system using optical and radar remote sensing:Benchmarking machine learning algorithms

Artificial Intelligence in Agriculture 2024年第2期12卷 97-108页

作者： Daniele Sasso Francesco Lodato Anna Sabatini Giorgio Pennazza Luca Vollero Marco Santonico Mario Merone Department of Engineering Research Unit of Computer Systems and BioinformaticsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Science and Technology for Sustainable Development and One Health Research Unit of Electronics for Sensor SystemsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Engineering Research Unit of Electronics for Sensor SystemsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Biology University of Naples FedericoⅡNaples 80138Italy

Mapping hazelnut orchards can facilitate land planning and utilization policies,supporting the development of cooperative precision farming *** present work faces the detection of hazelnut crops using optical and radar remote sensing data.A comparative study of Machine Learning techniques is *** system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud *** provide a dataset of 62,982 labeled samples,with 16,561 samples belonging to the‘hazelnut’class and 46,421 samples belonging to the‘other’class,collected in 8 heterogeneous geograph-ical areas of the Viterbo *** different comparative tests are conducted:firstly,we use a Nested 5-Fold Cross-Validation methodology to train,optimize,and compare different Machine Learning algorithms on a single *** a second experiment,the algorithms were trained on one area and tested on the remaining seven geo-graphical *** developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut *** the results,it emerges that Random Forest is the classifier with the highest generalizability,achieving the best performance in the second test with an accuracy of 96%and an F1 score of 91%for the‘hazelnut’class.

关键词： Remote sensing Crop detection Hazelnut Machine learning Classification

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Migratable AI systems for Tailoring User Experience through Multimodal Affective-Cognitive State Analysis

Migratable AI Systems for Tailoring User Experience through ...

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2023 International Conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： La Monica, Ludovica Institute of Cognitive Science and Technology National Research Council Rome00185 Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome00128 Italy

In the era of digital technologies permeating numerous aspects of society, the development of intelligent artificial agents endowed with autonomy, social capabilities, reactivity, and proactivity has become a pivotal innovation. Traditionally, these agents were confined to singular forms, known as embodiments, which dictated their capabilities and the environments in which they operated. To transcend these limitations and enable agents to expand their capabilities, the concept of agent migration has emerged, allowing them to seamlessly transition between different embodiments. My research program delves into the realm of agent migration, uncovering its challenges and potential opportunities. The central objective is to leverage this technology to deepen our understanding of human affective and cognitive states across various contexts. Specifically, my aim is to develop a migratory agent architecture capable of recognizing and characterizing individuals’ emotional experiences using different sets of capabilities tied to distinct embodiments. In summary, the intent of this research is to harness migratable AI to promote the technical advantages brought about by agent migration and gain insights into how to utilize the acquisition of additional capabilities resulting from migration to craft a more comprehensive and potentially enduring architecture, to be applied in the realm of affective computing. © 2022 Copyright for this paper by its authors.

关键词： Autonomous agents

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Application of process mining in the management of inpatient analysis

Application of process mining in the management of inpatient...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： D'Angelis, Onorato Lapadula, Valerio Iacuitto, Manuel Ivziku, Dhurata Sabatini, Anna Vollero, Luca Merone, Mario Campus Bio-Medico University of Rome Research Unit of Computer Systems and Bioinformatics Via Alvaro del Portillo Rome21 - 00128 Italy Campus Bio-Medico University of Rome Innovation and Development Unit Via Alvaro del Portillo Rome21 - 00128 Italy

ISBN: (纸本)9781665493840

The classification of the care pathway is able to define the clinical evolution of patients during hospitalization. It can also find the departments that are more stressed in terms of number of events or patients with complex clinical pictures. By analysing these aspects, it is possible to suggest improvements to the healthcare setting, making the health service provided more efficient. By applying the techniques of Process Mining it is possible to model the evolution of the complexity of inpatient management. In this paper, we present an application of some techniques of Process Mining starting from the data collected at Policlinico Universitario Campus Bio-Medico di Roma composed of anonymized records of patients in the period between 01/01/2016 and 31/12/2017. The patients and thus their hospitalization are described through a patient status grid, according to their level of autonomy, cognitive stability and clinical stability. This measure is used as an indirect measure of the complexity of inpatient management. The data were made compliant with the typical structure of an event log, and then the complexity of care of patients admitted to the facility was modelled, analyzing even the most stressed departments. The proposed approach suggests important information for the healthcare setting, ensuring an improvement of the services provided. © 2023 IEEE.

关键词： Hospitals

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Identification of Optimal Training for Prediction of Glucose Levels in Type-1-Diabetes Using Edge Computing

Identification of Optimal Training for Prediction of Glucose...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： D'Antoni, Federico Petrosino, Lorenzo Velieri, Andrea Sasso, Daniele D'Angelis, Onorato Boscarino, Tamara Vollero, Luca Merone, Mario Piemonte, Vincenzo Università Campus Bio-Medico di Roma Research Unit of Computer Systems and Bioinformatics Department of Engineering Rome Italy Università Campus Bio-Medico di Roma Chemical-physics Fundamentals in Chemical Engineering Department of Engineering Rome Italy

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Type 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease involving high blood glucose levels. It is caused by the destruction of pancreatic β-cells, which are responsible for insulin production. Survival of subjects with T1DM is dependent on exogenous insulin administrations [1]. In order to calculate and then inject an appropriate insulin dose, subjects must frequently monitor their glucose values, taking into account not only the exact amount of carbohydrates ingested, but also physical activity [2], [3], disease states [4], and stress episodes [5]. The main goal of glycemic control is to avoid or at least mitigate events such as hypoglycemia and hyperglycemia [6]. Hypoglycemia, defined as a blood glucose level lower than 70 mg/dL, can result in serious immediate complications such as seizure or coma [7], whereas hyperglycemia, defined as a blood glucose level greater than 180 mg/dL, leads to several microvascular and macrovascular complications in the long term [7]. The advent of Continuous Glucose Monitoring (CGM) devices, i.e., sensors capable of measuring interstitial glucose concentration at 1-to 5-minute intervals, has improved the quality of life of individuals with T1DM, allowing a decrease in the occurrence of hypo/hyperglycemic events [8]. Despite the widespread use of these devices, adverse glycemic events still occur. Providing patients with a prediction of blood glucose levels can further improve their quality of life enabling proactive interventions and reducing their daily cognitive load [9]. Indeed, research is focusing on the development of data-driven models that can predict future glycemic levels as accurately as possible on both short-term [10] and long-term predictions [11], leveraging past CGM values alone [12] or in combination with other features such as insulin injections or carbohydrate intake [13]. © 2022 IEEE.

关键词： Insulin

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Exploring Negated Entites for Named Entity Recognition in Italian Lung Cancer Clinical Reports

Exploring Negated Entites for Named Entity Recognition in It...

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20th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2024

作者： Paolo, Domenico Bria, Alessandro Greco, Carlo Russano, Marco Ramella, Sara Soda, Paolo Sicilia, Rosa Unit of Computer Systems and Bioinformatics Università Campus Bio-Medico di Roma Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy Operative Research Unit of Medical Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy

ISBN: (纸本)9781643685182

This paper explores the potential of leveraging electronic health records (EHRs) for personalized health research through the application of artificial intelligence (AI) techniques, specifically Named Entity Recognition (NER). By extracting crucial patient information from clinical texts, including diagnoses, medications, symptoms, and lab tests, AI facilitates the rapid identification of relevant data, paving the way for future care paradigms. The study focuses on Non-small cell lung cancer (NSCLC) in Italian clinical notes, introducing a novel set of 29 clinical entities that include both presence or absence (negation) of relevant information associated with NSCLC. Using a state-of-the-art model pretrained on Italian biomedical texts, we achieve promising results (average F1-score of 80.8%), demonstrating the feasibility of employing AI for extracting biomedical information in the Italian language. © 2024 The Authors.

关键词： Diseases

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Postural Data Analysis using AI-powered Classification Models 6

Postural Data Analysis using AI-powered Classification Model...

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6th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Bacco, Luca Coletta, Matteo Zaltieri, Martina Presti, Daniela Lo Massaroni, Carlo Schena, Emiliano Merone, Mario Università Campus Bio-Medico di RomaVia Research Unit of Computer Systems and Bioinformatics Department of Engineering Alvaro del Portillo 21 Rome00128 Italy Università Campus Bio-Medico di RomaVia Research Unit of Measurements and Biomedical Instrumentation Department of Engineering Alvaro del Portillo 21 Rome00128 Italy

ISBN: (纸本)9798350396577

Nowadays, more and more people are working remotely or in professions that require them to sit for long periods of time. Unfortunately, spending too much time in a seated position can lead to a range of physical and mental health problems, such as musculoskeletal discomfort, headaches, and respiratory issues. These problems are often exacerbated by poor posture, which is common when sitting for extended periods of time. To address this issue, we have developed a system for classifying sitting postures using sensors and machine learning algorithms, achieving 100% of accuracy with a set of seven fiber Bragg grating sensors. We have further optimized the multisensor system by studying the optimal number of sensors and their positioning on the spine, achieving over 95% accuracy in classifying upright, kyphotic, and lordotic positions with as little as only two devices. © 2023 IEEE.

关键词： Fiber Bragg gratings

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Identification of the Optimal Meal Detection Strategy for Adults, Adolescents, and Children with Type 1 Diabetes: an in Silico Validation

Identification of the Optimal Meal Detection Strategy for Ad...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Federico D’Antoni Martina Bertazzoni Luca Vollero Mario Merone Department of Engineering Research Unit of Computer Systems and Bioinformatics Università Campus Bio-Medico di Roma Rome Italy

Current management of Type 1 Diabetes mellitus (T1D) resorts to manual meal announcements from the patient to manage postprandial glycemia; nevertheless, suboptimal glycemic control is observed in real data, with the presence of many hypoglycemic and hyperglycemic events. The utilization of Continuous Glucose Monitoring (CGM) sensors and Artificial Intelligence (AI) is paving the way for improved and automated glycemic control. A step in this direction is represented by the automation of meal detection, which would not require patients to perform tasks such as carbohydrate estimation and meal announcement that are error-prone, especially for children and elderly *** this work, we investigate several AI models for meal detection from in silico data of 10 adults, 10 adolescents, and 10 children with T1D using only CGM data, and compare them to the standard detection method based on the glycemic threshold. We generate 30 days of data per patient that include 5 meals per day and introduce human error on carbohydrate estimation to make data more similar to the real ones. The AI models can detect more than 81% of meals from any cohort of patients while producing a relatively small amount of false positives. The feedforward neural network, the support vector machine, and the threshold method are the most promising meal detection strategies for adult, adolescent, and child populations, respectively, and may improve patients’ health and disease management.

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Virtual Scanner: Leveraging Resilient Generative AI for Radiological Imaging in the Era of Medical Digital Twins

Virtual Scanner: Leveraging Resilient Generative AI for Radi...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Adornato, Carolina Assolito, Cecilia Cordelli, Ermanno Di Feola, Francesco Guarrasi, Valerio Iannello, Giulio Marcoccia, Lorenzo Ayllon, Elena Mulero Restivo, Rebecca Rofena, Aurora Sicilia, Rosa Soda, Paolo Tortora, Matteo Tronchin, Lorenzo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Àlvaro del Portillo 21 Rome00128 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå90187 Sweden

Advancements in generative artificial intelligence (AI) are setting the stage for transformative changes in medical imaging, particularly through the development of the Virtual Scanner. This innovative approach leverages resilient generative AI to synthesize radiological images, addressing critical challenges in the field such as data scarcity, patient exposure to radiation, and the limitations of current imaging technologies. By harnessing the power of Generative Adversarial Networks (GANs) and focusing on the resilience of these algorithms, the Virtual Scanner aims to enhance diagnostic accuracy, improve patient care, and fill gaps in multimodal datasets. Our research explores both unimodal and multimodal techniques, including GAN ensembles, latent augmentation, and advanced texture synthesis, to create robust and adaptable generative models. Through extensive experimentation and analysis, we demonstrate the potential of the Virtual Scanner to revolutionize medical diagnostics by providing a safer, more efficient, and comprehensive imaging solution. The implications of this work extend beyond immediate medical applications, offering insights into the development of AI technologies capable of navigating the complexities of real-world data. © 2024 Copyright for this paper by its authors.

关键词： Generative adversarial networks

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Impact of Interfering Factors on a Glucose Sensor Model

Impact of Interfering Factors on a Glucose Sensor Model

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IEEE Instrumentation and Measurement Technology Conference

作者： Anna Sabatini Costanza Cenerini Luca Vollero Danilo Pau Research Unit of Electronics for Sensor Systems University Campus Bio-Medico of Rome Rome Research Unit of Computer Systems and Bioinformatics University Campus Bio-Medico of Rome Rome STMictroelectronics Agrate Brianza Italy

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Management of diabetes often involves monitoring blood glucose levels through Blood Glucose Monitoring (BGM) or Continuous Glucose Monitoring (CGM) systems. CGM systems, preferred for their noninvasive nature and ability to track glucose trends, face challenges in accuracy due to sensor drift. In fact, it is well known that all sensor systems are subject to different types of change that alter the measurement and increase the inaccuracy in detecting analytes. This paper proposes a model that integrates environmental conditions, physiological parameters, and sensor characteristics to model their impact on the accuracy of CGM systems. The study examines the impact of various interfering variables, including physiological changes, such as sweating or physical exertion; environmental conditions, such as high temperatures; and sensor-specific changes, such as adhesive properties and aging. Each factor is modeled and analyzed to understand its influence on the sensor's accuracy and reliability. This model seeks to enhance the repeatability and reliability of the system by offering a method for predicting interferences and their effect on measurements. Consequently, it enables the development of reliable countermeasures.

关键词： Temperature measurement Temperature sensors Accuracy Temperature Mathematical models Glucose Reliability

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