scholarly journals Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies

2021 ◽  
Vol 2119 (1) ◽  
pp. 012109
Author(s):  
S Abdurakipov
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Operating Time ◽  
Process Control System ◽  
Learning Technologies ◽  
Big Data Processing ◽  
Virtual Sensors ◽  
Temperature And Pressure

Abstract The current coverage of oil wells with telemetry does not allow timely determination of deviations in the operation of about 40% of electric submersible pumps. To solve this problem, a model of virtual sensors has been developed that allows the prediction of temperature and pressure growth at the pump intake in the absence of submersible sensors based on modern big data processing and machine learning technologies. The developed models of virtual sensors are embedded directly into the process control system, which allows notifying the technologists and operators about a possible reduction in the planned average pump operating time and their possible failures for various reasons.

Astronomical big data processing using machine learning: A comprehensive review

2022 ◽  
Author(s):  
Snigdha Sen ◽  
Sonali Agarwal ◽  
Pavan Chakraborty ◽  
Krishna Pratap Singh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Big Data Processing ◽  
Comprehensive Review

scholarly journals Attributes Reduction in Big Data

2020 ◽  
Vol 10 (14) ◽  
pp. 4901
Author(s):  
Waleed Albattah ◽  
Rehan Ullah Khan ◽  
Khalil Khan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Detection Efficiency ◽  
Large Data ◽  
Model Learning ◽  
Big Data Processing ◽  
Performance Loss ◽  
Points Of View

Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.

scholarly journals Influence of AI and Machine Learning in Insurance Sector

2022 ◽  
Author(s):  
Nitin Prajapati
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Quantitative Research ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Insurance Companies ◽  
Big Data Processing ◽  
Insurance Sector ◽  
Sector Analysis

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

scholarly journals Erratum to: A survey of machine learning for big data processing

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Junfei Qiu ◽  
Qihui Wu ◽  
Guoru Ding ◽  
Yuhua Xu ◽  
Shuo Feng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Big Data Processing
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