scholarly journals Real time scalable data acquisition of COVID-19 in six continents through PySpark - a big data tool

2021 ◽  
Author(s):  
Tanvi S Patel ◽  
Daxesh P Patel ◽  
Chirag N Patel
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
World Health ◽  
Learning Approach ◽  
Time Intervals ◽  
Processor Time ◽  
Global Pandemic ◽  
Machine Learning Approach ◽  
Health Organization ◽  
Python Programming

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared as a global emergency in January 2020 due to its pandemic outbreak. To examine this Coronavirus disease 2019 (COVID-19) effects various data are being generated through different platforms. This study was focused on the clinical data of COVID-19 which relied on python programming. Here, we proposed a machine learning approach to provide a insights into the COVID-19 information. PySpark is a machine learning approach which also known as Apache spark an accurate tool for the searching of results with minimum time intervals as compare to Hadoop and other tools. World Health Organization (WHO) started gathering corona patients data from last week of the February 2020. On March 11, 2020, the WHO declared COVID-19 a global pandemic. The cases became more evident and common after mid-March. This paper used the live owid (our world in data) dataset and will analyse and find out the following details on the live COVID-19 dataset. (1) The daily Corona virus scenario on various continents using PySpark in microseconds of Processor time. (2) After the various antibodies have been implemented, how they impact new cases on a regular basis utilizing various graphs. (3) Tabular representation of COVID-19 new cases in all the continents.

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

scholarly journals Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

2015 ◽  
Vol 11 (5) ◽  
pp. 2087-2096 ◽  
Author(s):  
Raghunathan Ramakrishnan ◽  
Pavlo O. Dral ◽  
Matthias Rupp ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Quantum Chemistry ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Su-In Lee ◽  
Safiye Celik ◽  
Benjamin A. Logsdon ◽  
Scott M. Lundberg ◽  
Timothy J. Martins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Myeloid Leukemia ◽  
Big Data ◽  
Precision Medicine ◽  
Myeloid Leukemia ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Acute Myeloid

scholarly journals Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

2016 ◽  
Vol 23 (3) ◽  
pp. 269-278 ◽  
Author(s):  
R. Andrew Taylor ◽  
Joseph R. Pare ◽  
Arjun K. Venkatesh ◽  
Hani Mowafi ◽  
Edward R. Melnick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Big Data ◽  
Hospital Mortality ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Emergency Department Patients
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