Prognostics and Health Management of Wind Energy Infrastructure Systems

2022 ◽  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Data Augmentation ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Quality Of Data ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
The Impact

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

The Role of Data Analytics and Machine Learning in Resurrecting Inductive-Based Accounting Research

2021 ◽  
Vol 9 (2) ◽  
pp. 1-19
Author(s):  
Lawrence A. Gordon
Keyword(s):  
Machine Learning ◽  
Empirical Evidence ◽  
Data Analytics ◽  
Accounting Research ◽  
Recent Developments ◽  
Important Trend ◽  
The Impact ◽  
The Way

The objective of this paper is to assess the impact of data analytics (DA) and machine learning (ML) on accounting research.[1] As discussed in the paper, the inherent inductive nature of DA and ML is creating an important trend in the way accounting research is being conducted. That trend is the increasing utilization of inductive-based research among accounting researchers. Indeed, as a result of the recent developments with DA and ML, a rebalancing is taking place between inductive-based and deductive-based research in accounting.[2] In essence, we are witnessing the resurrection of inductive-based accounting research. A brief review of some empirical evidence to support the above argument is also provided in the paper.   

scholarly journals The Role of the Pathologist in Population Health

2018 ◽  
Vol 143 (5) ◽  
pp. 610-620 ◽  
Author(s):  
David J. Gross ◽  
Mary Kennedy ◽  
Tarush Kothari ◽  
David O. Scamurra ◽  
Myra L. Wilkerson ◽  
...  
Keyword(s):  
Population Health ◽  
Health Management ◽  
Laboratory Data ◽  
Value Added ◽  
Medical Laboratory ◽  
Population Health Management ◽  
Semistructured Interviews ◽  
Value Based Care ◽  
The Impact

Context.— As part of its value-based care initiative, the College of American Pathologists has pursued research to better understand the role pathologists can have in population health. Objectives.— To answer the following questions: (1) what is the impact of population health and population health management on pathologists; (2) what roles are pathologists playing in population health management; (3) is population health something that pathologists in both larger and smaller settings can engage in; (4) are pathologists in a position to analyze laboratory data for population health, and, if so, what are the key information sources those pathologists must access; and (5) what steps can a pathologist take to become involved in population health? Design.— We conducted 10 semistructured interviews with pathologists and other medical laboratory leaders who have been active in population health. These interviews were supplemented with a review of the medical literature. Results.— Pathologists have demonstrated that laboratory data can provide unique value-added contributions to improving the health of populations. These contributions are not limited to pathologists in large, integrated settings. However, pathologists need to be proactive to contribute to health systems' population health efforts and may need to both enhance their own skills and the quality of their data to maximize the value of their contributions. Conclusions.— Although not necessarily a definitive summary of the roles that pathologists are playing in population health, this article identifies some of the promising and innovative activities occurring among pathologists and laboratorians.

scholarly journals Machine Learning for the Educational Sciences

2021 ◽  
Author(s):  
Sven Hilbert ◽  
Stefan Coors ◽  
Elisabeth Barbara Kraus ◽  
Bernd Bischl ◽  
Mario Frei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Decisive Role ◽  
Quality Of Data ◽  
Practical Applications ◽  
Educational Sciences ◽  
Complex Relationships ◽  
The Impact ◽  
Analytical Approaches

Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals Multitask Support Vector Regression for Solar and Wind Energy Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6308
Author(s):  
Carlos Ruiz ◽  
Carlos M. Alaíz ◽  
José R. Dorronsoro
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Support Vector Regression ◽  
Energy Production ◽  
Multitask Learning ◽  
Support Vector ◽  
Prediction Problem ◽  
Energy Prediction ◽  
The Common ◽  
The Impact

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

scholarly journals Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6279
Author(s):  
Alessio Ragno ◽  
Anna Baldisserotto ◽  
Lorenzo Antonini ◽  
Manuela Sabatino ◽  
Filippo Sapienza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Essential Oils ◽  
Data Augmentation ◽  
Biological Data ◽  
Chemical Components ◽  
Quantitative Composition ◽  
Chemical Compositions ◽  
Learning Models ◽  
Machine Learning Models

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition–activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils’ chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils’ constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Sign in / Sign up

Export Citation Format

Share Document