Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

2020 ◽  
Vol 34 (13) ◽  
pp. 4113-4131 ◽  
Author(s):  
Peiman Parisouj ◽  
Hamid Mohebzadeh ◽  
Taesam Lee
Keyword(s):  
United States ◽  
Machine Learning ◽  
Learning Algorithms ◽  
River Basins ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Streamflow Prediction ◽  
Climatic Zones

Collusive Algorithms as Mere Tools, Super-tools or Legal Persons

2019 ◽  
Author(s):  
Guan Zheng ◽  
Hong Wu
Keyword(s):  
United States ◽  
Machine Learning ◽  
Learning Algorithms ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Tacit Collusion ◽  
Antitrust Law ◽  
Strong Argument ◽  
Market Pricing ◽  
Distinct Features

Abstract The widespread use of algorithmic technologies makes rules on tacit collusion, which are already controversial in antitrust law, more complicated. These rules have obvious limitations in effectively regulating algorithmic collusion. Although some scholars and practitioners within antitrust circles in the United States, Europe and beyond have taken notice of this problem, they have failed to a large extent to make clear its specific manifestations, root causes, and effective legal solutions. In this article, the authors make a strong argument that it is no longer appropriate to regard algorithms as mere tools of firms, and that the distinct features of machine learning algorithms as super-tools and as legal persons may inevitably bring about two new cracks in antitrust law. This article clarifies the root causes why these rules are inapplicable to a large extent to algorithmic collusion particularly in the case of machine learning algorithms, classifies the new legal cracks, and provides sound legal criteria for the courts and competition authorities to assess the legality of algorithmic collusion much more accurately. More importantly, this article proposes an efficacious solution to revive the market pricing mechanism for the purposes of resolving the two new cracks identified in antitrust law.

Analyzing the occurrence of environmental indicator minerals using clustering techniques and mineral networks

2021 ◽  
Author(s):  
Jason Williams ◽  
Sally Potter-McIntyre ◽  
Justin Filiberto ◽  
Shaunna Morrison ◽  
Daniel Hummer
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
The United States ◽  
Hydrothermal Systems ◽  
Machine Learning Algorithms ◽  
Environmental Indicator ◽  
Clustering Techniques ◽  
Geological Processes ◽  
Indicator Mineral ◽  
Indicator Minerals

<p>Indicator minerals have special physical and chemical properties that can be analyzed to glean information concerning the composition of host rocks and formational (or altering) fluids. Clay, zeolite, and tourmaline mineral groups are all ubiquitous at the Earth’s surface and shallow crust and distributed through a wide variety of sedimentary, igneous, metamorphic, and hydrothermal systems. Traditional studies of indicator mineral-bearing deposits have provided a wealth of data that could be integral to discovering new insights into the formation and evolution of naturally occurring systems. This study evaluates the relationships that exist between different environmental indicator mineral groups through the implementation of machine learning algorithms and network diagrams. Mineral occurrence data for thousands of localities hosting clay, zeolite, and tourmaline minerals were retrieved from mineral databases. Clustering techniques (e.g., agglomerative hierarchical clustering and density based spatial clustering of applications with noise) combined with network analyses were used to analyze the compiled dataset in an effort to characterize and identify geological processes operating at different localities across the United States. Ultimately, this study evaluates the ability of machine learning algorithms to act as supplementary diagnostic and interpretive tools in geoscientific studies.</p>

scholarly journals Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach

10.2196/18401 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18401
Author(s):  
Jane M Zhu ◽  
Abeed Sarker ◽  
Sarah Gollust ◽  
Raina Merchant ◽  
David Grande
Keyword(s):  
Public Health ◽  
United States ◽  
Machine Learning ◽  
Public Health Education ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Care Organization ◽  
The Public ◽  
The Mean

Background Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. Objective We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. Methods Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. Results We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. Conclusions Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

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