In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise ...
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The novel coronavirus or officially known as SARS-CoV 2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused a severe pandemic over the world affecting not only the economy of the countries but also the lifest...
The novel coronavirus or officially known as SARS-CoV 2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused a severe pandemic over the world affecting not only the economy of the countries but also the lifestyle of the people worldwide. As on 31.12.2020, Covid-19 (coronavirus disease) has infecting more than 10266674 people and causing about 148738 deaths in India. It has been seen through various statistics of various countries that the number of Covid-19 cases grows exponentially as the number of test increases then after some period, the rate of new cases decreases. In this research paper, researchers have created deep learning-based model to predict the curve of the new Covid-19 cases vs the total number of tests conducted in India. There is still lockdown in some part of the country while some states have partially relaxed the rules and some states totally lifted the lockdown. Predicting the number of new cases and their trend can help in deciding what is the optimal time to release the lockdown. It will also help in determining when the coronavirus will loosen its grip from India.
The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, ...
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This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline...
This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bott...
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Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, whil...
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The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard ha...
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