Publications by authors named "Marina Paolanti"

Article Synopsis
  • The study aimed to develop a machine-learning model to predict the risk of sepsis following retrograde intrarenal surgery (RIRS) for kidney stones, including data from 1,552 patients across 16 centers.* -
  • Inclusion criteria for patients were specified, and certain conditions like concomitant ureteral stones were excluded, with antibiotic treatments used where applicable.* -
  • The Random Forest algorithm outperformed other machine-learning models, achieving high precision, recall, and accuracy, and a web-based version of the predictive tool is available for use in clinical settings.*
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The illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods.

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Article Synopsis
  • In deep learning, models need a lot of data to train effectively, leading to the development of zero-shot learning (ZSL), which tests models on "unseen" classes after training on "seen" ones.
  • This paper investigates how the way data is split influences the performance of ZSL models, specifically looking at the attributes and classes used during training.
  • Experiments show that ZSL models can be improved in terms of generalizability and robustness, particularly on different types of datasets, with dimensionality reduction techniques being effective in enhancing performance for fine-grained datasets.
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Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life.

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Vehicles' trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works.

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Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs' Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure.

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In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data.

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Today, e-health has entered the everyday work flow in the form of a variety of healthcare providers. General practitioners (GPs) are the largest category in the public sanitary service, with about 60,000 GPs throughout Italy. Here, we present the Nu.

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In increasingly hyper-connected societies, where individuals rely on short and fast online communications to consume information, museums face a significant survival challenge. Collaborations between scientists and museums suggest that the use of the technological framework known as Internet of Things (IoT) will be a key player in tackling this challenge. IoT can be used to gather and analyse visitor generated data, leading to data-driven insights that can fuel novel, adaptive and engaging museum experiences.

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Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving.

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