Covid-19 Pandemic: Data Analysis and Forecasting using Machine Learning Algorithms (Preprint)

2020 ◽  
Author(s):  
Sohini Sengupta ◽  
Sareeta Mugde
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Logarithmic Scale ◽  
Time Data ◽  
Transfer Data ◽  
Real Time Data ◽  
Current Scenario

BACKGROUND India reported its first Covid-19 case on 30th Jan 2020 with no practically no significant rise noticed in the number of cases in the month of February but March2020 onwards there has been a huge escalation as has been the case with like many other countries the world over. This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India. Data is gathered from multiple data sources- several authentic government websites. Variables such as gender, geographical location, age etc. have been represented using Python and Data Visualization techniques. Getting insights on Trend pattern and time series analysis will bring more clarity to the current scenario as analysis is totally on real-time data(till 19th June). Time Series Analysis and other pattern-recognition techniques are deployed to bring more clarity to the current scenario as analysis is totally based on real-time data(till 19th June,2020) Finally we will use some machine learning algorithms and perform predictive analytics for the near future scenario. We are using a sigmoid model to give an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten. Strength of Sigmoid model lies in providing a count of date –this is unique feature of analysis in this paper. We are also using certain feature engineering techniques to transfer data into logarithmic scale for better comparison removing any data extremities or outliers. Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers. Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals. Needless to mention there are a lot of factors responsible for the cases to come in the upcoming days. One factor being extent of adherence to the rules and restriction imposed by the Government by the citizens of the country. OBJECTIVE Prediction of the number of positive covid cases in the next few months . METHODS Machine Learning Model - Clustering Sigmoid Model RESULTS The model predicts maximum active cases at 258846. The curve flattens by day 154 i.e. 25th September and after that the curve goes down and the number of active cases eventually will decrease. CONCLUSIONS There are a lot of research works going on with respect to vaccines, economic dealings, precautions and reduction of Covid-19 cases. However currently we are at a mid-Covid situation. India along with many other countries are still witnessing upsurge in the number of cases at alarming rates on a daily basis. We have not yet reached the peak. Therefore cuff learning and downward growth are also yet to happen. Each day comes out with fresh information and large amount of data. Also there are many other predictive models using machine learning that beyond the scope of this paper. However at the end of the day it is only the precautionary measures we as responsible citizens can take that will help to flatten the curve. We can all join hands together and maintain all rules and regulations strictly. Maintaining social distancing, taking the lockdown seriously is the only key. This study is based on real time data and will be useful for certain key stakeholders like government officials, healthcare workers to prepare a combat plan along with stringent measures. Also the study will help mathematicians and statisticians to predict outbreak numbers more accurately.

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

Emergent Advancements in M.tb Detection: An Artificial Intelligence Approach (Preprint)

2021 ◽  
Author(s):  
Jasleen Kaur ◽  
Shruti Kapoor ◽  
Maninder Singh ◽  
Parvinderjit Singh Kohli ◽  
Urvinder Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Real Time ◽  
Automatic Detection ◽  
Machine Learning Algorithms ◽  
Real Time Monitoring ◽  
Time Data ◽  
The Real ◽  
Handheld Device ◽  
Real Time Data

BACKGROUND Infectious diseases are the major cause of mortality across the globe. Tuberculosis is one such infectious disease which is in the top 10 deaths causing diseases in developing as well as developed countries. The biosensors have emerged as a promising approach to attain the early detection of the pathogenic infection with accuracy and precision. However, the main challenge with biosensors is real time data monitoring preferentially reversible and label free measurements of certain analytes. Integration of biosensor and Artificial Intelligence (AI) approach would enable better acquisition of patient’s data in real time manner enabling automatic detection and monitoring of Mycobacterium tuberculosis (M.tb.) at an early stage. Here we propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. The collected data would be continuously transferred to the connected cloud integrated with AI based clinical decision support systems (CDSS) which may consist of the machine learning based analysis model useful in studying the patterns of disease infestation, progression, early detection and treatment. The proposed system may get deployed in different collaborating centres for validation and collecting the real time data. OBJECTIVE To propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. METHODS The Major challenges for control and early detection of the Mycobacterium tuberculosis were studied based upon the literature survey. Based upon the observed challenges, the biosensor based smart handheld device has been proposed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. RESULTS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly. CONCLUSIONS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly.

Machine Learning for Real-time Data Reduction in Cloud of Things

2020 ◽  
Author(s):  
Atheer Alahmed ◽  
Amal Alrasheedi ◽  
Maha Alharbi ◽  
Norah Alrebdi ◽  
Marwan Aleasa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data

scholarly journals Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

2018 ◽  
Author(s):  
Xiaojia Guo ◽  
Yael Grushka-Cockayne ◽  
Bert De Reyck
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Data ◽  
Passenger Flow ◽  
Real Time Data
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