1,720,999 research outputs found
A neural network approach to classify carotid disorders from Heart Rate Variability analysis
No full textBackground: Atherosclerosis is a progressive process responsible for most heart diseases and ischemic stroke. It constitutes, in fact, the most common cause of stroke in middle-aged people. To avoid or, at least, limit the disabling deficits that may derive from a carotid disease, a prompt and early diagnosis is necessary. The diagnostic technique used to detect a carotid disease is the eco-color Doppler. Unfortunately, this method is not free from errors, due to manufacturer mistakes or its operator dependence.Methods: In this study, we propose an automated methodology capable of identifying the presence of a carotid disease from the Heart Rate Variability analysis of electrocardiographic signals. A Correlation-based Feature Selector for data reduction and Artificial Neural Networks are used to distinguish between pathological and healthy subjects.Results: A series of tests has been realized to evaluate the proposed approach by using electrocardiographic signals selected from an available database in order to analyse the classification ability in comparison with other algorithms existing in literature. The results obtained show that the proposed approach provides values of accuracy, sensitivity, specificity, precision, F-measure and ROC area, respectively equal to 90.5%, 97.7%, 72.9%, 89.7%, 93.5% and 0.957, better than those achieved by other algorithms.Conclusions: Considering the achieved accuracy, our methodology is more effective than any of the main algorithm existing in literature. It is important to note that this approach is proposed as a support for the diagnosis of a carotid disorder through a non-invasive approach
Artificial Intelligence Techniques for the Non-invasive Detection of COVID-19 Through the Analysis of Voice Signals
No full textHealthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making, all tasks vital in a critical situation such as that of the COVID-19 pandemic. This paper presents a study about the possibility to support the early and non-invasive detection of COVID-19 through the analysis of voice signals by means of the main machine learning algorithms. If demonstrated, this detection capacity could be embedded in a powerful mobile screening application. To perform this important study, the Coswara dataset is considered. The aim of this investigation is not only to evaluate which machine learning technique best distinguishes a healthy voice from a pathological one, but also to identify which vowel sound is most seriously affected by COVID-19 and is, therefore, most reliable in detecting the pathology. The results show that Random Forest is the technique that classifies most accurately healthy and pathological voices. Moreover, the evaluation of the vowel /e/ allows the detection of the effects of COVID-19 on voice quality with a better accuracy than the other vowels
Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study
No full textConventional modern Machine Learning (ML) applications involve training models in the cloud and then transferring them back to the edge, especially in an Internet of Things (IoT) enabled environment. However, privacy-related limitations on data transfer from the edge to the cloud raise challenges: among various solutions, Federated Learning (FL) could satisfy privacy related concerns and accommodate power and energy issues of edge devices. This paper proposes a novel approach that combines FL and Ensemble Learning (EL) to improve both security and privacy challenges. The presented methodology introduces an extra layer, the Federation Layer, to enhance security. It uses Bayesian Networks (BNs) to dynamically filter untrusted/unsecure federation clients. This approach presents a solution for increasing the security and robustness of FL systems, considering also privacy and performance aspects
Inferring Failure Processes via Causality Analysis: from Event Logs to Predictive Fault Trees
No full textIn the current Artificial Intelligence era, the integration of the Industry 4.0 paradigm in real-world
settings requires robust and scientific methods and tools. Two concrete aims are the exploitation of
large datasets [1] and the guarantee of a proper level of explainability, demanded by critical systems
and applications [2]. Focusing on the predictive maintenance problem, this work leverages causality
analysis to elicit knowledge about system failure processes. The result is a model expressed according
to a newly introduced formalism: the Predictive Fault Trees [3]. This model is enriched by causal
relationships inferred from dependability-related event logs. The proposed approach considers both
fault-error-failure chains between system components and the impact of environmental variables (e.g.,
temperature, pressure) on the health status of the components. A proof of concept shows the effectiveness
of the methodology, leveraging an event-based simulator [4
Inferring Failure Processes via Causality Analysis: from Event Logs to Predictive Fault Trees
No full textIn the current Artificial Intelligence era, the integration of the Industry 4.0 paradigm in real-world
settings requires robust and scientific methods and tools. Two concrete aims are the exploitation of
large datasets [1] and the guarantee of a proper level of explainability, demanded by critical systems
and applications [2]. Focusing on the predictive maintenance problem, this work leverages causality
analysis to elicit knowledge about system failure processes. The result is a model expressed according
to a newly introduced formalism: the Predictive Fault Trees [3]. This model is enriched by causal
relationships inferred from dependability-related event logs. The proposed approach considers both
fault-error-failure chains between system components and the impact of environmental variables (e.g.,
temperature, pressure) on the health status of the components. A proof of concept shows the effectiveness
of the methodology, leveraging an event-based simulator [4
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain
Full text linkMachine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
