scholarly journals Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning

2021 ◽  
Author(s):  
Gael Donval ◽  
Calum Hand ◽  
James Hook ◽  
Emiko Dupont ◽  
Malena Sabate Landman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Porous Materials ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Brute Force ◽  
Screening Process ◽  
High Performing ◽  
Simulated Performance ◽  
Learning Techniques ◽  
Simple Molecules

<div>MOFs and COFs are porous materials with a large variety of applications including gas</div><div>storage and separation. Synthesised in a modular fashion from distinct building blocks, a</div><div>near in?nite number of structures can be constructed and the properties of the material can</div><div>be tailored for a speci?c application. While this modularity is a very attractive feature it also</div><div>poses a challenge. Attempting to identify the best performing material(s) for a given appli-</div><div>cation is experimentally intractable. Current research e?orts combine molecular simulations</div><div>and machine learning techniques to evaluate the simulated performance of hundreds of thou-</div><div>sands of materials to identify top performing MOFs and COFs for a given application. These</div><div>approaches typically rely on moderated brute-force screening which is still resource-intensive</div><div>as typically between 70 - 100 % of the hundreds of thousands of materials must be simulated</div><div>to create a training set for the machine learning models used, restricting screening to rela-</div><div>tively simple molecules. In this work we demonstrate our novel Bayesian mining approach</div><div>to materials screening which allows 62 - 92 % of the top 100 porous materials for a range of</div><div>applications to be readily identi?ed from large materials databases after only assessing less</div><div>than one percent of all materials. This is a stark contrast to the 0 - 1 % achieved by conven-</div><div>tional brute-force screening where porous materials are just chosen at random during a high</div><div>throughput screening. Through this accelerated virtual screening process, the identi?cation of</div><div>high performing materials can be used to more rapidly inform experimental e?orts and hence</div><div>lead to an acceleration of the entire research and development pipeline of porous materials.</div>

scholarly journals Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning

2021 ◽  
Author(s):  
Gael Donval ◽  
Calum Hand ◽  
James Hook ◽  
Emiko Dupont ◽  
Malena Sabate Landman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Porous Materials ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Brute Force ◽  
Screening Process ◽  
High Performing ◽  
Simulated Performance ◽  
Learning Techniques ◽  
Simple Molecules

<div>MOFs and COFs are porous materials with a large variety of applications including gas</div><div>storage and separation. Synthesised in a modular fashion from distinct building blocks, a</div><div>near in?nite number of structures can be constructed and the properties of the material can</div><div>be tailored for a speci?c application. While this modularity is a very attractive feature it also</div><div>poses a challenge. Attempting to identify the best performing material(s) for a given appli-</div><div>cation is experimentally intractable. Current research e?orts combine molecular simulations</div><div>and machine learning techniques to evaluate the simulated performance of hundreds of thou-</div><div>sands of materials to identify top performing MOFs and COFs for a given application. These</div><div>approaches typically rely on moderated brute-force screening which is still resource-intensive</div><div>as typically between 70 - 100 % of the hundreds of thousands of materials must be simulated</div><div>to create a training set for the machine learning models used, restricting screening to rela-</div><div>tively simple molecules. In this work we demonstrate our novel Bayesian mining approach</div><div>to materials screening which allows 62 - 92 % of the top 100 porous materials for a range of</div><div>applications to be readily identi?ed from large materials databases after only assessing less</div><div>than one percent of all materials. This is a stark contrast to the 0 - 1 % achieved by conven-</div><div>tional brute-force screening where porous materials are just chosen at random during a high</div><div>throughput screening. Through this accelerated virtual screening process, the identi?cation of</div><div>high performing materials can be used to more rapidly inform experimental e?orts and hence</div><div>lead to an acceleration of the entire research and development pipeline of porous materials.</div>

scholarly journals On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

2020 ◽  
Vol 12 (6) ◽  
pp. 2544
Author(s):  
Alice Consilvio ◽  
José Solís-Hernández ◽  
Noemi Jiménez-Redondo ◽  
Paolo Sanetti ◽  
Federico Papa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Real World ◽  
Asset Management ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
First Case ◽  
Learning Techniques ◽  
Track Circuits

The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

scholarly journals Detection of Cyber Attack in Networks using Machine Learning Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 2401-2404
Author(s):  
Meghana M
Keyword(s):  
Machine Learning ◽  
Information Security ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Brute Force ◽  
Cyber Attack ◽  
Detection Systems ◽  
Learning Techniques

The use of recent innovations provides unimaginable blessings to individuals, organizations, and governments, be that because it might, messes some up against them. for example, the protection of serious information, security of place away data stages, accessibility of knowledge so forth. Digital concern, that created an excellent deal of problems individuals and institutions, has received A level that might undermine open and nation security by totally different gatherings, as an example, criminal association, good individuals and digital activists. the foremost common risk to a network’s security is an intrusion like brute force, denial of service or maybe an infiltration from inside a network. this can be wherever machine learning comes into play. Intrusion Detection Systems (IDS) has been created to take care of a strategic distance from digital assaults.

scholarly journals Prediction of Severity of COVID-19-Infected Patients Using Machine Learning Techniques

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 31
Author(s):  
Aziz Alotaibi ◽  
Mohammad Shiblee ◽  
Adel Alshahrani
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Initial Screening ◽  
Patient History ◽  
Screening Process ◽  
Laboratory Findings ◽  
Risk Of Death ◽  
Learning Techniques

Precisely assessing the severity of persons with COVID-19 at an early stage is an effective way to increase the survival rate of patients. Based on the initial screening, to identify and triage the people at highest risk of complications that can result in mortality risk in patients is a challenging problem, especially in developing nations around the world. This problem is further aggravated due to the shortage of specialists. Using machine learning (ML) techniques to predict the severity of persons with COVID-19 in the initial screening process can be an effective method which would enable patients to be sorted and treated and accordingly receive appropriate clinical management with optimum use of medical facilities. In this study, we applied and evaluated the effectiveness of three types of Artificial Neural Network (ANN), Support Vector Machine and Random forest regression using a variety of learning methods, for early prediction of severity using patient history and laboratory findings. The performance of different machine learning techniques to predict severity with clinical features shows that it can be successfully applied to precisely and quickly assess the severity of the patient and the risk of death by using patient history and laboratory findings that can be an effective method for patients to be triaged and treated accordingly.

Physics Informed Machine Learning of Rainfall-Runoff Processes

2020 ◽  
Author(s):  
Vladan Babovic ◽  
Jayashree Chadalawada ◽  
Herath Mudiyanselage Viraj Vidura Herath
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Hydrological Model ◽  
Building Blocks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Learning Techniques ◽  
Highly Nonlinear ◽  
Wide Range

&lt;p&gt;Modelling of rainfall-runoff phenomenon continues to be a challenging task at hand of hydrologists as the underlying processes are highly nonlinear, dynamic and interdependent. Numerous modelling strategies like empirical, conceptual, physically based, data driven, are used to develop rainfall-runoff models as no model type can be considered to be universally pertinent for a wide range of problems. Latest literature review emphasizes that the crucial step of hydrological model selection is often subjective and is based on legacy. As the research outcome depends on model choice, there is a necessity to automate the process of model evolution, evaluation and selection based on research objectives, temporal and spatial characteristics of available data and catchment properties. Therefore, this study proposes a novel automated model building algorithm relying on machine learning technique Genetic Programming (GP).&lt;/p&gt;&lt;p&gt;State of art GP applications in rainfall-runoff modelling as yet used the algorithm as a short-term forecasting tool which produces an expected future time series very much alike to neural networks application. Such simplistic applications of data driven black-box machine learning techniques may lead to development of accurate yet meaningless models which do not satisfy basic hydrological insights and may have severe difficulties with interpretation. Concurrently, it should be admitted that there is a vast amount of knowledge and understanding of physical processes that should not just be thrown away. Thus, we strongly believe that the most suitable way forward is to couple the already existing body of knowledge with machine learning techniques in a guided manner to enhance the meaningfulness and interpretability of the induced models.&lt;/p&gt;&lt;p&gt;In this suggested algorithm the domain knowledge is introduced through the incorporation of process knowledge by adding model building blocks from prevailing rainfall-runoff modelling frameworks into the GP function set. Presently, the function set library consists with Sugawara TANK model functions, generic components of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX) and model equations of 46 existing hydrological models (MARRMoT). Nevertheless, perhaps more importantly, the algorithm is readily integratable with any other internal coherence building blocks. This approach contrasts from rest of machine learning applications in rainfall-runoff modelling as it not only produces the runoff predictions but develops a physically meaningful hydrological model which helps the hydrologist to better understand the catchment dynamics. The proposed algorithm considers the model space and automatically identifies the appropriate model configurations for a catchment of interest by optimizing user-defined learning objectives in a multi-objective optimization framework. The model induction capabilities of the proposed algorithm have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model-building algorithm are compatible with the fieldwork investigations and previously reported research findings.&lt;/p&gt;

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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