IoT Based Two Way Safety Enabled Intelligent Stove with Age Verification Using Machine Learning

Author(s):  
Mariha Afroz ◽  
Nazia Hasan ◽  
Md. Ishan Arefin Hossain
2021 ◽  
pp. tobaccocontrol-2021-056937
Author(s):  
Neal Shah ◽  
Matthew Nali ◽  
Cortni Bardier ◽  
Jiawei Li ◽  
James Maroulis ◽  
...  

BackgroundIncreased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms.ObjectivesWe used unsupervised machine learning to identify and characterise sale offers of electronic nicotine delivery systems (ENDS) and associated products on Instagram. We examined types of sellers, geographic ENDS location and use of age verification.MethodsOur methodology was composed of three phases: data collection, topic modelling and content analysis. We used data mining approaches to query hashtags related to ENDS product use among young adults to collect Instagram posts. For topic modelling, we applied an unsupervised machine learning approach to thematically categorise and identify topic clusters associated with selling activity. Content analysis was then used to characterise offers for sale of ENDS products.ResultsFrom 70 725 posts, we identified 3331 engaged in sale of ENDS products. Posts originated from 20 different countries and were roughly split between individual (46.3%) and retail sellers (43.4%), with linked online sellers (8.8%) representing a smaller volume. ENDS products most frequently offered for sale were flavoured e-liquids (53.0%) and vaping devices (20.5%). Online sellers offering flavoured e-liquids were less likely to use age verification at point of purchase (29% vs 64%) compared with other products.ConclusionsInstagram is a global venue for unregulated ENDS sales, including flavoured products, and access to websites lacking age verification. Such posts may violate Instagram’s policies and US federal and state law, necessitating more robust review and enforcement to prevent ENDS uptake and access.


2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  

Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols

Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  

2014 ◽  
Author(s):  
Francisco L. Sotelo ◽  
Anne Zhou ◽  
Loretta Hsueh ◽  
Elizabeth A. Klonoff

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang

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