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The impact of imbalanced training data on machine learning for author name disambiguation
In supervised machine learning for author name disambiguation, negative ...
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Measuring the unmeasurable - a project of domestic violence risk prediction and management
The prevention of domestic violence (DV) have aroused serious concerns i...
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Using News Articles and Financial Data to predict the likelihood of bankruptcy
Over the past decade, millions of companies have filed for bankruptcy. T...
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Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors
Life events can dramatically affect our psychological state and work per...
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Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data
All groups of people felt the impact of the COVID-19 pandemic. This situ...
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The fiscal response to revenue shocks
We study the impact of fiscal revenue shocks on local fiscal policy. We ...
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Classification of Existing Virtualization Methods Used in Telecommunication Networks
This article studies the existing methods of virtualization of different...
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An analysis of non-immigrant work visas in the USA using Machine Learning
High-skilled immigrants are a very important factor in US innovation and entrepreneurship, accounting for roughly a quarter of US workers in fields such as computer science and delivering in terms of patents or firm starts. Their contributions to the US is rapidly increasing in the past three decades and are found to be well trained and skilled on average than their native counterparts. While the impact of these high-skilled workers is signified, the way in which they compete to enter a tech hub like the US is rather not fair. H-1B, the work visa to import high-skilled workers, is not used for high skilled anymore but rather used to import cheap labor to displace native workers in many cases. Many billionaires, experts, pundits and even the government are looking for many amendments in H-1B to abolish this by bringing in a merit system or increasing the minimum wages to awarding these visas. We attempt to analyze the petitions filed by 2011-16 and classify the petitions filed as positive or negative, indicating whether the petition is highly skilled or not. After classifying, we build a model using Random Forest to predict any visa petition in any state of the US as positive or negative. Experimental results show the companies that are classified as abusing these visas (negative) are well consistent with the ones shown in reports and news articles
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