scholarly journals Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine

2016 ◽  
Vol 87 (1-4) ◽  
pp. 1077-1093 ◽  
Author(s):  
Dorota Wilk-Kolodziejczyk ◽  
Krzysztof Regulski ◽  
Grzegorz Gumienny
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cast Iron ◽  
Comparative Analysis ◽  
Ductile Iron ◽  
Nodular Cast Iron ◽  
Austempered Ductile Iron ◽  
Support Vector

scholarly journals Comparative Analysis of Intellectual Methods for Muscular Contraction Interpretation for Gesture Interface Implementation

2021 ◽  
Vol 2096 (1) ◽  
pp. 012190
Author(s):  
E V Bunyaeva ◽  
I V Kuznetsov ◽  
Y V Ponomarchuk ◽  
P S Timosh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Single Channel ◽  
Muscular Contraction ◽  
Support Vector ◽  
Machine Learning Methods

Abstract The paper considers comparative analysis results of the machine learning methods used for the gesture recognition based on the surface single-channel electromyography (sEMG) data. The data were processed using multilayer perceptron, support vector machine, decision tree ensemble (Random Forest) and logistic regression for the chosen four gesture types. The conclusion was derived on the analysis efficiency of these methods using commonly recommended accuracy metrics.

scholarly journals Abrasive wear of ploughshare blades made of Austempered Ductile Iron

2007 ◽  
Vol 55 (1) ◽  
pp. 173-182 ◽  
Author(s):  
Jiří Votava ◽  
Michal Černý ◽  
J. Filípek
Keyword(s):  
Cast Iron ◽  
Abrasive Wear ◽  
Ductile Iron ◽  
Base Material ◽  
Field Tests ◽  
Nodular Cast Iron ◽  
Austempered Ductile Iron ◽  
Spheroidal Graphite ◽  
Cast State ◽  
Heat Treated

The objective of this article consists in exhaustive monitoring of abrasive wear and subsequent evaluation of nodular cast iron with spheroidal graphite used as the base material for production of ploughshare blades. Nodular cast iron has a lot of convenient properties for production of these components. We have tested this material in field tests. For these tests there were manufactured ploughshare blades (directly according to original). These ploughshare blades were tested in practical conditions of agricultural company Farma Nedvězí Ltd. Three ploughshare blades were used in natural cast state and the others were heat-treated. During the test there were measured decreases of weights of the ploughshare blades and subsequently compared with decreases of original ploughshare blades, produced by company Opall Agri which are ordinarily available at our market.

scholarly journals AUSTEMPERING HEAT TREATMENT STUDY OF CAST DUCTILE IRON: ANALYSIS OF MECHANICAL AND MICROSTRUCTURAL PROPERTIES, ACCORDING TO THE A897M STANDARD SPECIFICATIONS FOR AUSTEMPERED DUCTILE IRON CASTINGS

2018 ◽  
Vol 15 (29) ◽  
pp. 64-74
Author(s):  
A. R. M. SCHIFINO ◽  
F. R. SANTANNA ◽  
A. P. TRINDADE
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Cast Iron ◽  
Ductile Iron ◽  
Great Influence ◽  
Metallic Matrix ◽  
Nodular Cast Iron ◽  
Austempered Ductile Iron ◽  
Mechanical Resistance ◽  
Spring Support

The objective of this work was to develop heat treatment parameters of an austempered cast iron alloy ASTM 897 / A 897M - 1400/1100/1, aiming at the production of a truck spring support. The austempered nodular cast iron, known by the acronym ADI - Austempered Ductile Iron - is a class of nodular cast iron that, after austempered thermal treatment, increases significantly its mechanical properties and tenacity (Machado, 2007). Mechanical and metallographic tests demonstrated the great influence that the level of microshrinkage has on the elongation and mechanical resistance of the material. Generally, tensile tests demonstrate high elongation due to minimal presence of microshrinkage and segregations in the metallic matrix of the material, as well as to the presence of austenite with high carbon retained in the ADI matrix. Analyzes were performed to determine if the mechanical properties required by ASTM 897 / A897M were achieved. Within this standard, four degrees can be obtained. The degree of interest in this study was 1400/1100/1, which is the grade requested by the company, so that the truck spring support can be put into service. Tensile, Charpy and optical microscopy tests were carried out.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

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