Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results

2019 ◽  
Author(s):  
Joana Cardoso-Fernandes ◽  
Ana Claudia Moreira Teodoro ◽  
Alexandre Lima ◽  
E. Roda-Robles
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Support Vector ◽  
Preliminary Results ◽  
Vector Machines

scholarly journals Prediction of Stock Prices using Random Forest and Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 473-477
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Stock Prices ◽  
Support Vector ◽  
Future Cost ◽  
Financial Instrument ◽  
Vector Machines ◽  
The Future ◽  
Future Value

Prediction of stock markets is the act of attempting to determine the future value of an inventory of a business or other financial instrument traded on an economic exchange.Effectively foreseeing the future cost of a stock will amplify the benefits of the financial specialist.This article suggests a model of machine learning to forecast the price of the stock market.During the way toward considering various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures. In this article, we will present and audit an increasingly suitable strategy for anticipating more prominent exactness stock oscillations.The primary thing we thought about was the securities exchange estimating informational index from yahoo stocks. We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource for financial exchange associations and will give genuine options in contrast to the difficulties confronting the stock speculator.

scholarly journals Detecting Fake News Tweets from Twitter

2021 ◽  
Vol 23 (08) ◽  
pp. 532-537
Author(s):  
Cherlakola Abhinav Reddy ◽  
◽  
Sai Nitesh Gadiraju ◽  
Dr. Samala Nagaraj ◽  
◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Online Media ◽  
Fake News ◽  
Breaking News ◽  
Learning Techniques ◽  
Vector Machines

Online media has progressively obtained integral to the route billions of individuals experience news and occasions, frequently bypassing writers—the conventional guardians of breaking news. Occasions,in reality, make a relating spike of posts (tweets) on Twitter. This projects a great deal of significance on the validity of data found via online media stages like Twitter. We have utilized different managed learning techniques like Naïve Bayes, Decision Trees, and Support Vector Machines on the information to separate tweets among genuine and counterfeit news. For our AI models, we have utilized tweet and client highlights as our indicators. We accomplished a precision of 88% utilizing the Random Forest classifier and 88% utilizing the Decision tree. Notwithstanding, we accept that breaking down client records would build the accuracy of our models.

Prediction of S-nitrosylation sites by integrating support vector machines and random forest

2019 ◽  
Vol 15 (6) ◽  
pp. 451-458 ◽  
Author(s):  
Md. Mehedi Hasan ◽  
Balachandran Manavalan ◽  
Mst. Shamima Khatun ◽  
Hiroyuki Kurata
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Support Vector ◽  
Biological Processes ◽  
Post Translational Modification ◽  
Vector Machines

Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes.

The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

2010 ◽  
Vol 31 (11) ◽  
pp. 2885-2909 ◽  
Author(s):  
Steven E. Sesnie ◽  
Bryan Finegan ◽  
Paul E. Gessler ◽  
Sirpa Thessler ◽  
Zayra Ramos Bendana ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Decision Trees ◽  
Costa Rican ◽  
Support Vector ◽  
Vector Machines
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