Abstract: Educational Data Mining (EDM) is the application of data mining methods in the educational domain. In the EDM field, we see mixed data (i.e., text and number data types). Grouping or ...
US retail sales on Black Friday, the busiest shopping day of the year, climbed 4.1% compared with last year, according to data released Saturday by Mastercard SpendingPulse. Online shoppers alone ...
Landlords could no longer rely on rent-pricing software to quietly track each other's moves and push rents higher using confidential data, under a settlement between RealPage Inc. and federal ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. The discovery of functional small molecules, chemical matter ...
To understand how Americans are faring economically these days, it's helpful to consider the eleventh letter of the alphabet. Experts describe the current U.S. economy as "K-shaped," a reference to ...
Coeur Mining produces and sells gold, silver, zinc, and lead concentrates from wholly owned mining operations in the United States, Canada, and Mexico. Generates revenue primarily through the ...
SAN FRANCISCO, Oct 22 (Reuters) - Google said it has developed a computer algorithm that points the way to practical applications for quantum computing and will be able to generate unique data for use ...
The change is part of a deal to bring TikTok under U.S. ownership to avert a looming ban. By Emmett Lindner and Lauren Hirsch The software giant Oracle will oversee the security of Americans’ data and ...
White House press secretary Karoline Leavitt on Saturday revealed further details of a deal reached between the U.S. and China over control of the popular social media platform TikTok, sharing that ...
From core shacks to cloud systems, mining is entering a digital era where innovation could shape how projects move forward. In mining, billion-dollar bets depend on how someone reads a rock.
The integration of distributed generations (DGs) and time-varying loads introduces significant uncertainties in distribution network planning. Existing methods often rely on simplified scenarios (e.g.
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