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.

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.

scholarly journals Earth remote sensing imagery classification using a multi-sensor super-resolution fusion algorithm

2020 ◽  
Vol 44 (4) ◽  
pp. 627-635
Author(s):  
A.M. Belov ◽  
A.Y. Denisova
Keyword(s):  
Remote Sensing ◽  
Support Vector Machines ◽  
Random Forest ◽  
Classification Accuracy ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Earth Remote Sensing ◽  
Vector Machines ◽  
Fused Image ◽  
Better Than

Earth remote sensing data fusion is intended to produce images of higher quality than the original ones. However, the fusion impact on further thematic processing remains an open question because fusion methods are mostly used to improve the visual data representation. This article addresses an issue of the effect of fusion with increasing spatial and spectral resolution of data on thematic classification of images using various state-of-the-art classifiers and features extraction methods. In this paper, we use our own algorithm to perform multi-frame image fusion over optical remote sensing images with different spatial and spectral resolutions. For classification, we applied support vector machines and Random Forest algorithms. For features, we used spectral channels, extended attribute profiles and local feature attribute profiles. An experimental study was carried out using model images of four imaging systems. The resulting image had a spatial resolution of 2, 3, 4 and 5 times better than for the original images of each imaging system, respectively. As a result of our studies, it was revealed that for the support vector machines method, fusion was inexpedient since excessive spatial details had a negative effect on the classification. For the Random Forest algorithm, the classification results of a fused image were more accurate than for the original low-resolution images in 90% of cases. For example, for images with the smallest difference in spatial resolution (2 times) from the fusion result, the classification accuracy of the fused image was on average 4% higher. In addition, the results obtained for the Random Forest algorithm with fusion were better than the results for the support vector machines method without fusion. Additionally, it was shown that the classification accuracy of a fused image using the Random Forest method could be increased by an average of 9% due to the use of extended attribute profiles as features. Thus, when using data fusion, it is better to use the Random Forest classifier, whereas using fusion with the support vector machines method is not recommended.

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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