scholarly journals Intelligent Machine Learning: Tailor-Making Macromolecules

Polymers ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 579 ◽  
Author(s):  
Yousef Mohammadi ◽  
Mohammad Saeb ◽  
Alexander Penlidis ◽  
Esmaiel Jabbari ◽  
Florian J. Stadler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kinetic Monte Carlo ◽  
Engineering Problem ◽  
Optimization Techniques ◽  
Machine Learning Techniques ◽  
Coordination Polymerization ◽  
Intelligent Modeling ◽  
Learning Techniques ◽  
Input Variables ◽  
Architectural Characteristics

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important ‘inverse’ engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.

scholarly journals An Exploration of ECG Signal Feature Selection and Classification using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 797-804 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Optimization Techniques ◽  
Signal Classification ◽  
Machine Learning Techniques ◽  
Ecg Signal ◽  
Vital Part ◽  
Learning Techniques ◽  
Cardiac Illness ◽  
Feature Selection Techniques

This effort examines and likens a collection of active methods to dimensionally reduction and select salient features since the electrocardiogram database. ECG signal classification and feature selection plays a vital part in identifies of cardiac illness. An accurate ECG classification could be a difficult drawback. This effort also examines of ECG classification into arrhythmia kinds. This effort discusses the problems concerned in Classification ECG signal and exploration of ECG databases (MIT-BIH), pre-processing, dimensionally reduction, Feature selection techniques, classification and optimization techniques. Machine learning techniques give offers developed classification accurateness with imprecation of dimensionality.

Habitat mapping in coastal dunes using Random Forest classification of UAV images

2021 ◽  
Author(s):  
Charmaine Cruz ◽  
Kevin McGuinness ◽  
Jerome O'Connell ◽  
James Martin ◽  
Philip Perrin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sand Dune ◽  
Coastal Dunes ◽  
Machine Learning Techniques ◽  
Habitats Directive ◽  
Actual State ◽  
Preliminary Results ◽  
Learning Techniques ◽  
Input Variables

<p>The EU Habitats Directive (HD) requires that natural habitats are monitored every six years to assess habitat condition, extent and range. In Ireland, reporting for the HD is based on ecological field surveys. This field-based mapping and assessment methodology, while desirable, can be time-consuming, difficult, and expensive. It also only covers a sub-sample of sites due to cost. Thus, more efficient mapping approaches, such as remote sensing, should be considered to supplement these monitoring techniques.  </p><p>Here we present some preliminary results from the iHabiMap Project. The overall aim of iHabiMap is to develop and assess analytical approaches that use machine learning techniques to derive habitat maps from imagery acquired by Unmanned Aerial Vehicle (UAV). The project started in 2019 and to date twelve UAV surveys have been conducted acquiring very high-resolution (6 cm) multispectral imagery for five selected study sites. Ecological data were collected concurrently with each UAV survey to obtain the actual state of the recorded vegetation. The project focuses on assessing imagery from three habitat types: upland blanket bog, coastal dunes, and grassland in Ireland. In this abstract we focus on the coastal dunes.</p><p>The Random Forest (RF) machine learning algorithm using the python Scikit-learn library was utilized to identify and map the habitats. The pixel-based RF model was calibrated using a combination of ground truth data and several colour, band ratios, and topographic variables derived from the UAV data. Six separate models were generated to compare how classification accuracies change based on combinations of input variables. The methodology was initially implemented to classify four sand dune Annex I habitats: 2120 - Marram dunes; 2130 - Fixed dunes; 2170 – Dunes with creeping willow; 2190 – Dune slacks, in the Maharees site in Ireland. The results were analyzed using the standard confusion matrix to calculate overall and class-specific accuracies. Preliminary results suggest that RF can classify sand dune Annex I habitats 2120, 2130, 2170, and 2190, with overall accuracies ranging from 0.80 to 0.93, depending on the input variables. The highest accuracy was achieved using the combined spectral and topographic information. Feature importance metrics calculated from RF showed that the surface elevation and Green Normalized Vegetation Index (GNDVI) were the key input variables in the classification. The results obtained from the presented workflow demonstrate the potential of using UAV, machine learning techniques, and field data in characterizing coastal dune environments. The classification will be further expanded to explore phenological differences of the vegetation by including the temporal dimension of the data and will be tested on the upland and grassland habitats. Moreover, an upscaling methodology will be implemented to assess UAV data usability on a broader scale mapping.</p>

scholarly journals Machine Learning Techniques for Fluid Flows at the Nanoscale

Fluids ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 96
Author(s):  
Filippos Sofos ◽  
Theodoros E. Karakasidis
Keyword(s):  
Machine Learning ◽  
Fluid Flows ◽  
Md Simulations ◽  
Machine Learning Techniques ◽  
Lennard Jones ◽  
Learning Techniques ◽  
Data Production ◽  
Data Points ◽  
Input Variables ◽  
Predicted Values

Simulations of fluid flows at the nanoscale feature massive data production and machine learning (ML) techniques have been developed during recent years to leverage them, presenting unique results. This work facilitates ML tools to provide an insight on properties among molecular dynamics (MD) simulations, covering missing data points and predicting states not previously located by the simulation. Taking the fluid flow of a simple Lennard-Jones liquid in nanoscale slits as a basis, ML regression-based algorithms are exploited to provide an alternative for the calculation of transport properties of fluids, e.g., the diffusion coefficient, shear viscosity and thermal conductivity and the average velocity across the nanochannels. Through appropriate training and testing, ML-predicted values can be extracted for various input variables, such as the geometrical characteristics of the slits, the interaction parameters between particles and the flow driving force. The proposed technique could act in parallel to simulation as a means of enriching the database of material properties, assisting in coupling between scales, and accelerating data-based scientific computations.

scholarly journals Memory Utilization and Machine Learning Techniques for Compiler Optimization

2021 ◽  
Vol 37 ◽  
pp. 01021
Author(s):  
A V Shreyas Madhav ◽  
Siddarth Singaravel ◽  
A Karmel
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Compiler Optimization ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Machine Learning Techniques ◽  
Peak Performance ◽  
Learning Techniques ◽  
Overall Performance ◽  
Memory Utilization

Compiler optimization techniques allow developers to achieve peak performance with low-cost hardware and are of prime importance in the field of efficient computing strategies. The realm of compiler suites that possess and apply efficient optimization methods provide a wide array of beneficial attributes that help programs execute efficiently with low execution time and minimal memory utilization. Different compilers provide a certain degree of optimization possibilities and applying the appropriate optimization strategies to complex programs can have a significant impact on the overall performance of the system. This paper discusses methods of compiler optimization and covers significant advances in compiler optimization techniques that have been established over the years. This article aims to provide an overall survey of the cache optimization methods, multi memory allocation features and explore the scope of machine learning in compiler optimization to attain a sustainable computing experience for the developer and user.

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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