scholarly journals Machine Learning for Healthcare Diagnostics

2019 ◽  
Vol 8 (6S2) ◽  
pp. 999-1001
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Genetic Disease ◽  
World Wide ◽  
Early Stage ◽  
Human Life ◽  
The Real ◽  
Initial Stage

Presently machine learning and artificial intelligence is playing one of the most important role in diagnose many genetic and non genetic disease. So that the rapid inventions in machine learning can save thousands of life’s as it can diagnose the early stage of many serious diseases. In this research the datasets for such diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life. The problem with such methodology is that it is not possible to get accurate results in the initial stage of research. The reason is every human have different immunity power and stamina. There are many diagnostics center who are fully dependent on the equipments which are fully based on machine learning. In order to boost this process it is necessary to collect the real time patient’s data from different hospitals, states and countries. So that it will be beneficial for world wide.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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.

Artificially Intelligent Physiotherapy

2021 ◽  
Vol 10 (1) ◽  
pp. 77-88
Author(s):  
Sachin Pandurang Godse ◽  
Shalini Singh ◽  
Sonal Khule ◽  
Shubham Chandrakant Wakhare ◽  
Vedant Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Range Of Motion ◽  
Real Time ◽  
Severe Pain ◽  
User Performance ◽  
Computer Aided ◽  
To Come ◽  
And Performance ◽  
At Home

Physiotherapy is the trending medication for curing bone-related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. The aim here is to build a framework using artificial intelligence and machine learning for providing patients with a digitalized system for physiotherapy. Even though various computer-aided assessment of physiotherapy rehabilitation exist, recent approaches for computer-aided monitoring and performance lack versatility and robustness. In the authors' approach is to come up with proposition of an application which will record patient physiotherapy exercises and also provide personalized advice based on user performance for refinement of therapy. By using OpenPose Library, the system will detect angle between the joints, and depending upon the range of motion, it will guide patients in accomplishing physiotherapy at home. It will also suggest to patients different physio-exercises. With the help of OpenPose, it is possible to render patient images or real-time video.

Real-Time Water Injection Monitoring with Distributed Fiber Optics Using Physics-Informed Machine Learning

2021 ◽  
Author(s):  
Teymur Sadigov ◽  
Cagri Cerrahoglu ◽  
James Ramsay ◽  
Laurence Burchell ◽  
Sean Cavalero ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Real Time ◽  
Fiber Optics ◽  
Water Injection ◽  
The Real ◽  
Start Up ◽  
Naturally Fractured ◽  
Monitoring Application ◽  
Time Injection

Abstract This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to maximise recovery from the field. In addition to their default limitations, such as providing a snapshot measurement and disturbing the natural well flow with up and down flowing passes, wireline-conveyed production logs (PL) are also unable to provide a high-resolution profile of the water injection along the reservoir due to the completion type. DFOS offers unique surveillance capability when permanently installed along the reservoir interface and continuously providing injection profiles with full visibility along the reservoir section without the need for an intervention. The real-time injection monitoring application uses both distributed acoustic and temperature sensing (DAS & DTS) and is based on physics-informed machine learning models. It is now running and available to all asset users on the cloud. So far, the application has generated high-resolution injection profiles over a dozen multi-rate injection periods automatically and the results are cross-checked against the profiles from the warmback analyses that were also generated automatically as part of the same application. The real-time monitoring insights have been effectively applied to provide significant business value using the capability for start-up optimization to manage and improve injection conformance, monitor fractured formations and caprock monitoring.

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

2019 ◽  
pp. 84-104 ◽  
Author(s):  
Mamata Rath ◽  
Sushruta Mishra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Learning Approaches ◽  
Advanced Level ◽  
Real Time Applications

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

Tie me to the mast: artificial intelligence & reputation risk management

2018 ◽  
Vol 39 (1) ◽  
pp. 61-64 ◽  
Author(s):  
Peter Buell Hirsch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Thought Experiment ◽  
Early Stage ◽  
Stress Test ◽  
Risky Behaviors ◽  
Content Type ◽  
Early Signs ◽  
Reputation Risk

Purpose Artificial intelligence and machine learning have spread rapidly across every aspect of business and social activity. The purpose of this paper is to examine how this rapidly growing field of analytics might be put to use in the area of reputation risk management. Design/methodology/approach The approach taken was to examine in detail the primary and emerging applications of artificial intelligence to determine how they could be applied to preventing and mitigating reputation risk by using machine learning to identify early signs of behaviors that could lead to reputation damage. Findings This review confirmed that there were at least two areas in which artificial intelligence could be applied to reputation risk management – the use of machine learning to analyze employee emails in real time to detect early signs of aberrant behavior and the use of algorithmic game theory to stress test business decisions to determine whether they contained perverse incentives leading to potential fraud. Research limitations/implications Because of the fact that this viewpoint is by its nature a thought experiment, the authors have not yet tested the practicality or feasibility of the uses of artificial intelligence it describes. Practical implications Should the concepts described be viable in real-world application, they would create extraordinarily powerful tools for companies to identify risky behaviors in development long before they had run far enough to create major reputation risk. Social implications By identifying risky behaviors at an early stage and preventing them from turning into reputation risks, the methods described could help restore and maintain trust in the relationship between companies and their stakeholders. Originality/value To the best of the author’s knowledge, artificial intelligence has never been described as a potential tool in reputation risk management.

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