scholarly journals Synergistic Action of Immunotherapy and Nanotherapy against Cancer Patients Infected with SARS-CoV-2 and the Use of Artificial Intelligence

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Tanvi Gupta ◽  
Tilahun Ayane Debele ◽  
Yu-Feng Wei ◽  
Anish Gupta ◽  
Mohd Murtaza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Synergistic Action ◽  
Peak Time ◽  
Time Data ◽  
Major Threat ◽  
Vulnerable Patients ◽  
The World ◽  
Real Time Data

Since 2019, the SARS-CoV-2 pandemic has caused a huge chaos throughout the world and the major threat has been possessed by the immune-compromised individuals involving the cancer patients; their weakened immune response makes them vulnerable and susceptible to the virus. The oncologists as well as their patients are facing many problems for their treatment sessions as they need to postpone their surgery, chemotherapy, or radiotherapy. The approach that could be adopted especially for the cancer patients is the amalgamation of immunotherapy and nanotherapy which can reduce the burden on the healthcare at this peak time of the infection. There is also a need to predict or analyze the data of cancer patients who are at a severe risk of being exposed to an infection in order to reduce the mortality rate. The use of artificial intelligence (AI) could be incorporated where the real time data will be available to the physicians according to the different patient’s clinical characteristics and their past treatments. With this data, it will become easier for them to modify or replace the treatment to increase the efficacy against the infection. The combination of an immunotherapy and nanotherapy will be targeted to treat the cancer patients diagnosed with SARS-CoV-2 and the AI will act as icing on the cake to monitor, predict and analyze the data of the patients to improve the treatment regime for the most vulnerable patients.

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 Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

2020 ◽  
Vol 17 (12) ◽  
pp. 4568 ◽  
Author(s):  
Dabiah Alboaneen ◽  
Bernardi Pranggono ◽  
Dhahi Alshammari ◽  
Nourah Alqahtani ◽  
Raja Alyaffer
Keyword(s):  
Saudi Arabia ◽  
Real Time ◽  
World Health ◽  
Logistic Growth ◽  
Final Phase ◽  
Time Data ◽  
Local Prediction ◽  
The World ◽  
Real Time Data ◽  
Health Organization

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

scholarly journals Artificial Intelligence assisted Prediction of COVID-19 Hotspots in Third Wave using EHTERAZ

2021 ◽  
Author(s):  
Mustafa Ahmed ◽  
Mohammed Houkan ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Mobile Technology ◽  
Hot Spots ◽  
Mobile Application ◽  
Local State ◽  
Third Wave ◽  
Time Data ◽  
Policy Makers ◽  
Real Time Data

The present study conceptualizes fusion of artificial intelligence and contract tracking mobile application for predicting the COVID-19 hot spots. The findings suggest that mobile technology can be used to provide real-time data on the national and local state of the pandemic, enabling policy makers to make informed decisions in a quickly moving pandemic.

scholarly journals Frontiers of Real-Time Data Analysis

2011 ◽  
Vol 49 (1) ◽  
pp. 72-100 ◽  
Author(s):  
Dean Croushore
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Data Sets ◽  
Current Analysis ◽  
Time Data ◽  
The Past ◽  
Data Revisions ◽  
The World ◽  
Real Time Data ◽  
The Impact

In the past ten years, researchers have explored the impact of data revisions in many different contexts. Researchers have examined the properties of data revisions, how structural modeling is affected by data revisions, how data revisions affect forecasting, the impact of data revisions on monetary policy analysis, and the use of real-time data in current analysis. This paper summarizes many of the questions for which real-time data analysis has provided answers. In addition, researchers and institutions have developed better real-time data sets around the world. Still, additional research is needed in key areas and research to date has uncovered even more fruitful areas worth exploring. (JEL C52, C53, C80, E01)

scholarly journals Doctor at Your Fingertips: An Exploration of Digital Visits from Stakeholders’ Perspectives

Life ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Selina Demi ◽  
Samir Hilmy ◽  
Christina Keller
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Empirical Data ◽  
Mobile Technologies ◽  
Structured Interviews ◽  
Time Data ◽  
Technological Advances ◽  
Real Time Data

Technological advances and the evolution of mobile technologies enable patients to meet their doctors through their smartphones. While offering the opportunity of digital visits to patients, there are also challenges for this development. The purpose of this study is to enhance the understanding of digital visits, as perceived by experts working in telemedicine companies. To serve this purpose, the authors conducted semi-structured interviews with managers and employees in eight telemedicine companies. The analysis of the empirical data confirmed the importance of digital visits and their efficiency. The potential of digital visits is expected to increase significantly, should they make use of remote devices to transfer real-time data from patients to physicians. In such a case, digital visits are expected to cover approximately 70–75% of medical cases. However, the use of remote devices must be taken with caution and specific conditions need to be taken into account. We encourage researchers to perform research on promising technologies such as artificial intelligence and remote diagnostic devices, which could make more diagnoses and conditions possible to be treated by digital visits. This is even more important in light of the ongoing Covid-19 pandemic.

scholarly journals AN IMPROVED SAFETY ALGORITHM FOR ARTIFICIAL INTELLIGENCE ENABLED PROCESSORS IN SELF DRIVING CARS

2019 ◽  
Vol 2019 (2) ◽  
pp. 95-104 ◽  
Author(s):  
Dr. Samuel Manoharan
Keyword(s):  
Artificial Intelligence ◽  
Road Safety ◽  
Human Population ◽  
Environmental Issues ◽  
The Self ◽  
Driving Ability ◽  
Time Data ◽  
The Road ◽  
The World ◽  
Self Driving Cars

The innovation in the automobiles have undergone a most noteworthy leap from the operation of the vehicles that was human centered to vehicles with the self –driving ability. The self-driving cars being a major level of attraction today, needs to concentrate also on the privacy, energy, traffic flow, environmental issues and the road safety. The perfect road safety is a goal that can be never achieved, due to the growing number of vehicles and the human population all over the world. Measures to improve the probability of the safety is put forward in the paper for the cars with the capability of the self-driving built with the artificial intelligence enabled processors. The performance evaluation of the proposed algorithm for using real time data ensures the competence of the safety algorithm in the self-driving cars.

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