Search results for "Machine learning"

Prelogical Test: An Alternative Tool for Early Detection of Learning Difficulties

2014

Abstract Difficulties during the preschool age commonly lead to children who cannot solve problems, organize information and create meaning. It is necessary to predict factors that may affect their future learning. The aim is to develop an evaluation tool, to be applied in groups and that can easily evaluate results, to detect future learning problems in children of 3-6 years old. Computational intelligence techniques could contribute greatly to analyze results and to detect patterns that otherwise would not be apparent. Two protocols were implemented: an Indirect Variables Protocol (IVP) which captures children's personal data, and a Direct Variables Protocol (DVP) that assesses the graphi…

Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study

2021

AbstractRecent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrode’s montage. Here, we delineate how a closed-loop tES-fMRI study of …

Deformed quons and bi-coherent states

2017

We discuss how a q-mutation relation can be deformed replacing a pair of conjugate operators with two other and unrelated operators, as it is done in the construction of pseudo-fermions, pseudo-bosons and truncated pseudo-bosons. This deformation involves interesting mathematical problems and suggests possible applications to pseudo-hermitian quantum mechanics. We construct bi-coherent states associated to $\D$-pseudo-quons, and we show that they share many of their properties with ordinary coherent states. In particular, we find conditions for these states to exist, to be eigenstates of suitable annihilation operators and to give rise to a resolution of the identity. Two examples are discu…

Domain Generation Algorithm Detection Using Machine Learning Methods

2018

A botnet is a network of private computers infected with malicious software and controlled as a group without the knowledge of the owners. Botnets are used by cybercriminals for various malicious activities, such as stealing sensitive data, sending spam, launching Distributed Denial of Service (DDoS) attacks, etc. A Command and Control (C&C) server sends commands to the compromised hosts to execute those malicious activities. In order to avoid detection, recent botnets such as Conficker, Zeus, and Cryptolocker apply a technique called Domain-Fluxing or Domain Name Generation Algorithms (DGA), in which the infected bot periodically generates and tries to resolve a large number of pseudorando…

Reliability Generalization Meta-Analysis

2020

Un meta-análisis de generalización de confiabilidad (MA GF) es un método para integrar estadísticamente las estimaciones de fiabilidad obtenidas en diferentes aplicaciones de un test. El MA GF permite a los investigadores caracterizar la fiabilidad promedio de las puntuaciones obtenida en un test en múltiples estudios y situaciones y estimar el grado de variabilidad en los coeficientes de fiabilidad en diferentes tipos de medidas, muestras y contextos. Por lo tanto, sus resultados permiten ofrecer pautas a los investigadores y profesionales aplicados sobre qué escalas son más fiables para evaluar un constructo y en qué circunstancias. Así pues, los investigadores y profesionales necesitan s…

The Post-COVID-19 Functional Status scale: a tool to measure functional status over time after COVID-19

2020

Since the outbreak of the Coronavirus disease 2019 (COVID-19) pandemic, most attention has focused on containing transmission of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and addressing the surge of critically ill patients in acute care settings. Indeed, as of April 29th 2020, over 3 million confirmed cases have been accounted for globally [1]. In the coming weeks and months, emphasis will gradually involve also post-acute care of COVID-19 survivors. It is anticipated that COVID-19 may have a major impact on physical, cognitive, mental and social health status, also in patients with mild disease presentation [2]. Previous outbreaks of coronaviruses have been associate…

The EU-funded I3LUNG Project:Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

2023

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. …

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective …

2022

Item does not contain fulltext BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifie…

Is a 1-cm margin from major vessels adequate for radiofrequency ablation of pulmonary neoplasms?

2009

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces

2019

AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of su…