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 rada...
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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.
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 ...
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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 Recogniti...
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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 levera...
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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 abil...
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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.
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...
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Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce "Not ...
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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 m...
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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 ...
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.
作者:
Bacco, LucaDell'Orletta, FeliceMerone, MarioDepartment of Engineering
Unit of Computer Systems and Bioinformatics Campus Bio-Medico University of Rome Via Alvaro del Portillo 21 Rome00128 Italy ItaliaNLP Lab
National Research Council Istituto di Linguistica Computazionale "antonio Zampolli" Via Giuseppe Moruzzi 1 Pisa56124 Italy
The healthcare industry is experiencing an unprecedented era of transformation, driven by the proliferation of Electronic Health Records (EHRs) and the emergence of vast amounts of natural language data from sources l...
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