A novel method of trans-esophageal Doppler cardiac output monitoring utilizing peripheral arterial pulse contour with/without machine learning approach

2021 ◽  
Author(s):  
Kazunori Uemura ◽  
Takuya Nishikawa ◽  
Toru Kawada ◽  
Can Zheng ◽  
Meihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulse Contour ◽  
Cardiac Output Monitoring ◽  
Learning Approach ◽  
Esophageal Doppler ◽  
Arterial Pulse ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Output Monitoring ◽  
Peripheral Arterial

scholarly journals Managing Churn to Maximize Profits

2020 ◽  
Vol 39 (5) ◽  
pp. 956-973 ◽  
Author(s):  
Aurélie Lemmens ◽  
Sunil Gupta
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Novel Method

This article provides a novel method to optimize the profits of proactive retention campaigns using a machine learning approach to causal inference.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

scholarly journals Naïve Bayes classifier predicts functional microRNA target interactions in colorectal cancer

2015 ◽  
Vol 11 (8) ◽  
pp. 2126-2134 ◽  
Author(s):  
Raheleh Amirkhah ◽  
Ali Farazmand ◽  
Shailendra K. Gupta ◽  
Hamed Ahmadi ◽  
Olaf Wolkenhauer ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Mirna Target ◽  
Naive Bayes ◽  
Learning Approach ◽  
Bayes Classifier ◽  
Microrna Target ◽  
Machine Learning Approach ◽  
Specific Prediction ◽  
Novel Method

The article describes a novel method (CRCmiRTar) for a CRC-specific prediction of functional miRNA-target interactions based on a machine learning approach.

scholarly journals Twitter Sentiment Analysis and Visualization Using Apache Spark and Elasticsearch

2018 ◽  
Vol 7 (3.12) ◽  
pp. 314 ◽  
Author(s):  
Maragatham G ◽  
Shobana Devi A
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Rich Information ◽  
Space Data ◽  
User Friendly ◽  
The Impact

Sentiment analysis on Twitter data has paying more attention recently. The system’s key feature, is the immediate communication with other users in an easy, fast way and user-friendly too. Sentiment analysis is the process of identifying and classifying opinions or sentiments expressed in source text. There is a huge volume of data present in the web for internet users and a lot of data is generated per second due to the growth and advancement of web technology. Nowadays, Internet has become best platform to share everyone's opinion, to exchange ideas and to learn online. People are using social network sites like facebook, twitter and it has gained more popularity among them to share their views and pass messages about some topics around the world. As tweets, notices and blog entries, the online networking is producing a tremendous measure of conclusion rich information. This client produced assumption examination information is extremely helpful in knowing the supposition of the general population swarm. At the point when contrasted with general supposition investigation the twitter assumption examination is much troublesome because of its slang words and incorrect spellings. Twitter permits 140 as the most extreme cutoff of characters per message. The two procedures that are mostly utilized for content examination is information base approach and machine learning approach. In this paper, we investigated the twitter created posts utilizing Machine Learning approach. Performing assumption examination in a particular area, is to distinguish the impact of space data in notion grouping. we ordered the tweets as constructive, pessimistic and separate diverse people groups' data about that specific space. In this paper, we developed a novel method for sentiment learning using the Spark coreNLP framework. Our method exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner.

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