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Using Deep Learning for Image-Based Plant Disease Detection
Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, a deep convolutional neural network is trained to identify 14 crop species and 26 diseases (or absence thereof).
An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing
These data are the beginning of an on-going, crowdsourcing effort to enable computer vision approaches to help solve the problem of yield losses in crop plants due to infectious diseases.
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
COVID-Twitter-BerT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19, shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets.
Dynamics and Control of Diseases in Networks with Community Structure
It is found that community structure has a major impact on disease dynamics, and it is shown that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals.
A high-resolution human contact network for infectious disease transmission
- M. Salathé, Maria A. Kazandjieva, Jung Woo Lee, P. Levis, M. Feldman, J. Jones
- MedicineProceedings of the National Academy of Sciences
- 23 June 2010
High-resolution data of CPIs during a typical day at an American high school is obtained, permitting the reconstruction of the social network relevant for infectious disease transmission and suggested that contact network data are required to design strategies that are significantly more effective than random immunization.
Modelling the influence of human behaviour on the spread of infectious diseases: a review
Recent efforts to incorporate human behaviour into disease models are reviewed, and it is proposed that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, andAccording to the assumed effects of such behaviour.
Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control
This work uses publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine and finds that most communities are dominated by either positive or negative sentiments towards the novel vaccine.
Early Assessment of Anxiety and Behavioral Response to Novel Swine-Origin Influenza A(H1N1)
The results from an online survey about risk perception of the Influenza A(H1N1) outbreak during the first few days of widespread media coverage find that after an initially high level of concern, levels of anxiety waned along with the Perception of the virus as an immediate threat.
The state of affairs in the kingdom of the Red Queen.
COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation.
Why the testing strategy in Switzerland should be strengthened urgently, as a core component of a combination approach to control COVID-19 is explained.