1,720,990 research outputs found
Wetting and coalescence prevention of drops in a liquid matrix. Ground and parabolic flight results
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Wetting and coalescence prevention of drops in a liquid matrix. Ground and parabolic flight results
No full textContent-Based Fake News Detection With Machine and Deep Learning: a Systematic Review
No full textFake news, which can be defined as intentionally and verifiably false news, has a strong influence on critical aspects of our society. Manual fact-checking is a widely adopted approach used to counteract the negative effects of fake news spreading. However, manual fact-checking is not sufficient when analysing the huge volume of newly created information. Moreover, the number of labeled datasets is limited, humans are not particularly reliable labelers and databases are mostly in English and focused on political news. To solve these issues state-of-the-art machine learning models have been used to automatically identify fake news. However, the high amount of models and the heterogeneity of features used in literature often represents a boundary for researchers trying to improve model performances. For this reason, in this systematic review, a taxonomy of machine learning and deep learning models and features adopted in Content-Based Fake News Detection is proposed and their performance is compared over the analysed works. To our knowledge, our contribution is the first attempt at identifying, on average, the best-performing models and features over multiple datasets/topics tested in all the reviewed works. Finally, challenges and opportunities in this research field are described with the aim of indicating areas where further research is needed
Emotion-Aware Monitoring of Users’ Reaction With a Multi-Perspective Analysis of Long-and Short-Term Topics on Twitter
No full textA framework for unsupervised learning and predictive maintenance in Industry 4.0
No full textIn recent decades, the economic importance of maintaining machines, equipment, and production facilities has prompted many scholars to examine various aspects of the maintenance of physical assets. However, the industry continues to face the recurring problem of improving product and equipment maintenance processes. New opportunities for improving these processes arise from Industry 4.0 technologies because they make it possible to realize better solutions to the problem of predictive maintenance. Starting from a Big Data and Internet of Things (IoT) architecture as a reference, this paper proposes an abstract framework for predictive maintenance using unsupervised learning models to support decision-making in maintenance programs. From the abstract framework, a predictive maintenance system was developed to enable effective just-in-time maintenance strategies. An unsupervised machine learning algorithm, based on the Gauxian mixtures model, allows us to study the influence on a machine's behavior of a single variable, a group of variables of the same type, and combined variables of different types. The algorithm provides experts with information on which part of the machine they need to focus on to find potential causes of future failures
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
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