1,721,125 research outputs found
Population dynamics and habitat selection of the European hare (Lepus europaeus P.) on an area of monocultures of northern Italy
No full textVarianti politiche d'autore. Da Verri a Manzoni
No full textRecensione al volume Varianti politiche d'autore. Da Verri a Manzoni, a cura di Beatrice Nava, Bologna Pàtron, 201
Improving Activity Recognition while Reducing Misclassification of Unknown Activities
No full textAutomated recognition of Activities of daily living is a significant task since it allows monitoring patients remotely using wearable devices. It could also help doctors and specialists to easily track the health status of elderly people and of patients suffering from a variety of geriatric diseases. Most of the literature regarding activity recognition does not face the problem of classifying activities of interest performed among activities that are not, i.e., unknown activities for the classifier. In this paper, we propose a novel method, based on the acceleration data recorded by wearable smartwatches, aimed at improving the activity recognition accuracy in presence of unknown activities (i.e., in real-world settings). The approach is based on an ensemble of different classifiers, a filtering procedure, and a final voting mechanism. The empirical evaluation, carried out on 8 different subjects, shows that our approach improves the overall F1 score by 5.5%, increases the recognition of unknown activities by 11.1%, and decreases the amount of data wrongly classified as an unknown activity by 15.5%. The first two observed differences are respectively statistically significant (Wilcoxon test p-value<0.01)
Reproducibility in Activity Recognition Based on Wearable Devices: a Focus on Used Datasets
No full textDaily living activity recognition using wearable devices: A features-rich dataset and a novel approach
No full textAutomated daily living activity recognition is a relevant task since it allows to assess the health status of a subject both objectively and remotely. Having a reliable measure is important since it gives precise indications to doctors and researchers interested in evaluating the effectiveness of treatments or drugs (e.g., in the context of clinical stud-ies). The possibility to perform this task remotely is more convenient for the patients and acquired increasing importance not only due to the current pandemic, but also because of the regularly growing population of elderly people that could benefit from remote monitoring. In this paper, first, we describe a novel wearable-device-based dataset that contains data (1) of a high number of daily life activities, coming from a real-life scenario, (2) recorded by applying multiple devices on different parts of the body, and (3) recorded with medical-grade devices at a high sampling frequency. Then, second, we describe a machine learning-based method for activity recognition. Our approach takes in input a dataset and through multiple phases allows to recognise the activities performed by the subjects with a good degree of accuracy (up to 0.92 expressed as F1 score depending on the location)
Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices
No full textIntroduction: Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance. Methods: We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately. Results: The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set. Discussions: Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth-often overlooked factors- can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies
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
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
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