Classification ofABO3perovskite solids: a machine learning study

We explored the use of machine learning methods for classifying whether a particularABO3chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, theAandBionic radii relative to the radius of O, and the bond valence distances between theAandBions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2–3 percentage points over using any one pair. We also included the Mendeleev numbers of theAandBatoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.

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Harvesting Brain Signal using Machine Learning Methods

Journal of Engineering and Science in Medical Diagnostics and Therapy ◽

10.1115/1.4053064 ◽

2021 ◽

Author(s):

Kevin Matsuno ◽

Vidya Nandikolla

Keyword(s):

Machine Learning ◽

Error Rate ◽

Learning Algorithm ◽

Relevance Vector Machine ◽

Event Related Potential ◽

Machine Learning Algorithm ◽

Learning Methods ◽

Linear Discriminant ◽

Machine Learning Methods ◽

Physical Task

Abstract Brain computer interface (BCI) systems are developed in biomedical fields to increase the quality of life. The development of a six class BCI controller to operate a semi-autonomous robotic arm is presented. The controller uses the following mental tasks: imagined left/right hand squeeze, imagined left/right foot tap, rest, one physical task, and jaw clench. To design a controller, the locations of active electrodes are verified and an appropriate machine learning algorithm is determined. Three subjects, ages ranging between 22-27, participated in five sessions of motor imagery experiments to record their brainwaves. These recordings were analyzed using event related potential plots and topographical maps to determine active electrodes. BCILAB was used to train two, three, five, and six class BCI controllers using linear discriminant analysis (LDA) and relevance vector machine (RVM) machine learning methods. The subjects' data was used to compare the two-method's performance in terms of error rate percentage. While a two class BCI controller showed the same accuracy for both methods, the three and five class BCI controllers showed the RVM approach having a higher accuracy than the LDA approach. For the five-class controller, error rate percentage was 33.3% for LDA and 29.2% for RVM. The six class BCI controller error rate percentage for both LDA and RVM was 34.5%. While the percentage values are the same, RVM was chosen as the desired machine learning algorithm based on the trend seen in the three and five class controller performances.

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EDR signatures observed by MMS : a statistical study of dayside events found with machine learning

10.5194/egusphere-egu21-2381 ◽

2021 ◽

Author(s):

Quentin Lenouvel ◽

Vincent Génot ◽

Philippe Garnier ◽

Benoit Lavraud ◽

Sergio Toledo

Keyword(s):

Machine Learning ◽

Statistical Study ◽

Learning Algorithm ◽

Small Scale ◽

Diffusion Region ◽

Machine Learning Algorithm ◽

Physical Processes ◽

The Core ◽

Machine Learning Methods ◽

Guide Field

<p>The understanding of magnetic reconnection's physical processes has considerably been improved thanks to the data of the Magnetopsheric Multiscale mission (MMS). However, a lot of work still has to be done to better characterize the core of the reconnection process : the electron diffusion region (EDR). We previously developed a machine learning algorithm to automatically detect EDR candidates, in order to increase the available list of events identified in the literature. However, identifying the parameters that are the most relevant to describe EDRs is complex, all the more that some of the small scale plasma/fields parameters show limitations in some configurations such as for low particle densities or large guide fields cases. In this study, we perform a statistical study of previously reported dayside EDRs as well as newly reported EDR candidates found using machine learning methods. We also show different single and multi-spacecraft parameters that can be used to better identify dayside EDRs in time series from MMS data recorded at the magnetopause. And finally we show an analysis of the link between the guide field and the strength of the energy conversion around each EDR.</p>

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P4622Prediction of in-hospital bleeding for AMI patients undergoing PCI using machine learning method

European Heart Journal ◽

10.1093/eurheartj/ehz745.1004 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

X Y Zhao ◽

J G Yang ◽

T G Chen ◽

J M Wang ◽

X Li ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Prediction Model ◽

Clinical Decision Making ◽

Learning Algorithm ◽

Area Under The Curve ◽

Learning Methods ◽

Machine Learning Methods ◽

Validation Set

Abstract Background Prediction of in-hospital bleeding is critical for clinical decision making for acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI). Machine learning methods can automatically select the combination of the important features and learn their underlying relationship with the outcome. Objective We aim to evaluate the predictive value of machine learning methods to predict in-hospital bleeding for AMI patients. Methods We used data from the multicenter China Acute Myocardial Infarction (CAMI) registry. We randomly partitioned the cohort into derivation set (75%) and validation set (25%). Using data from the derivation set, we applied a state-of-art machine learning algorithm, XGBoost, to automatically select features from 106 candidate variables and train a risk prediction model to predict in-hospital bleeding (BARC 3, 5 definition). Results 16736 AMI patients who underwent PCI were consecutively included in the analysis, while 70 (0.42%) patients had in-hospital bleeding followed the BARC 3,5 definition of bleeding. Fifty-nine features were automatically selected from the candidate features and were used to construct the prediction model. The area under the curve (AUC) of the XGBoost model was 0.816 (95% CI: 0.745–0.887) on the validation set, while AUC of the CRUSADE risk score was 0.723 (95% CI: 0.619–0.828). Relative contribution of the 12 most important features Feature Relative Importance Direct bilirubin 0.078 Heart rate 0.077 CKMB 0.076 Creatinine 0.064 GPT 0.052 Age 0.048 SBP 0.036 TG 0.035 Glucose 0.035 HCT 0.031 Total bilirubin 0.030 Neutrophil 0.030 ROC of the XGBoost model and CRUSADE Conclusion The XGBoost model derived from the CAMI cohort accurately predicts in-hospital bleeding among Chinese AMI patients undergoing PCI. Acknowledgement/Funding the CAMS innovation Fund for Medical Sciences (CIFMS) (2016-12M-1-009); the Twelfth Five-year Planning Project of China (2011BAI11B02)

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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