scholarly journals Machine Learning Methods for Content - Classification and Categorization

2021 ◽  
pp. 184-189
Author(s):  
Prabhat Kubal ◽  
Prof. Surabhi Thorat ◽  
Prof. Swati Maurya
Keyword(s):  
Machine Learning ◽  
Everyday Life ◽  
Adverse Consequence ◽  
Learning Methods ◽  
Web Based ◽  
Machine Learning Methods ◽  
Content Classification

These days online gatherings and web-based media stages have furnished people with the necessary resources to advance their contemplations and put themselves out there free paying little heed to the kind of language used to communicate those thoughts, in certain examples these internet based remarks contain express language which might hurt the peruser. We likewise evaluate the class irregularity issues related with the dataset by utilizing inspecting procedures and misfortune. Models we applied yield high in general exactness with moderately minimal expense. To diminish the adverse consequence of poisonous remark in everyday life we have endeavored to plan a Toxic Language detector.

scholarly journals Machine Learning Methods for Seismic Hazards Forecast

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 308 ◽  
Author(s):  
Valeri G. Gitis ◽  
Alexander B. Derendyaev
Keyword(s):  
Machine Learning ◽  
Training Sample ◽  
Time Interval ◽  
Minimum Area ◽  
Learning Methods ◽  
Web Based ◽  
Strong Earthquakes ◽  
Machine Learning Methods ◽  
Regression Approach ◽  
Spatio Temporal

In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.

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