Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network

Fatriansyah, Jaka Fajar; Surip, Siti Norasmah; Hartoyo, Fernanda

doi:10.5109/4774229

Cited by 8 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is a better way of classification compared to conventional machine learning predictive models 43) succeeded in predicting the mechanical properties of structural information 44) and overcome the problem of uncertainty 45) . However, this research has limitations in data processing which is still done manually, so it still cannot be implemented practically in health facilities.…”

Section: Resultsmentioning

confidence: 99%

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Sumarti,

Sheilla Rully Anggita,

Hartono

et al. 2023

Evergreen

View full text Add to dashboard Cite

Breast cancer is the most common cancer in Indonesia. One way to detect it early is by screening using mammography. Previous trials showed that mammography screening in women aged 40-49 years could reduce breast cancer mortality by 25%. However, misdiagnosis may occur abaout breast density and the patient's physical size due to machines. In addition, human reader errors can occur concerning the reader's experience and perception. Therefore, diagnostic aids are needed to distinguish benign and malignant cases and receive appropriate treatment. The methodology in this research consists of three stages: preprocessing, texture feature extraction, and data classification. Preprocessing consists of filtering, contrast, cropping, and resizing, while texture feature extraction consist of Histogram and (Gray Level Co-occurrence Matrix) GLCM. Data classification using Support Vector Machines (SVM), Naive Bayes, Multi-Layer Perceptron (MLP), Multiclass classifier, and Random Forest methods with Weka Machine Learning software. It produces an accuracy of 62.00%, 62.00%, 88.00%, 82.00%, and 100.00%, respectively. The results of data classification using the Random Forest method show that the accuracy, specifications, and specificity reach 100%. Random forest can be used as the most optimal classification method to distinguish benign and malignant cases based on texture features in mammography images using Weka Machine Learning software. This can help radiologists and medical professionals to diagnose cases and take further steps, such as therapy.

show abstract

Section: Resultsmentioning

confidence: 99%

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Sumarti,

Sheilla Rully Anggita,

Hartono

et al. 2023

Evergreen

View full text Add to dashboard Cite

show abstract

“…Meanwhile, to find the correlation between egg production in tons per capita and the prevalence of malnutrition under five, the independent variable is egg production in tons per capita and the dependent variable is the prevalence of malnutrition under five. The correlation coefficient was calculated using the Pearson correlation analysis method of two variables (Fatriansyah, 2022). The standard deviation is calculated using the general standard deviation equation.…”

Section: Implementation Methodsmentioning

confidence: 99%

Untitled

Fatriansyah¹,

Pradana²,

Jonatan³

et al. 2022

ABDIDOS

View full text Add to dashboard Cite

Indonesia is a country with low consumption of chicken eggs and this has implications for the high prevalence of malnutrition among children under five in Indonesia. In this community service survey research, an analysis of the correlation between chicken egg production and calorie consumption will be carried out as the prevalence of under-five malnutrition (0-59 months). The analysis carried out is Pearson correlation analysis. The study found that there was a positive correlation between egg production per capita and egg consumption per province with a correlation coefficient of 2.067 x 103 tons/kcal. In addition, there is also a negative correlation between egg consumption and the prevalence of malnutrition in children under five (0-59 months). This study obtained a recommendation that it is important to increase egg production by increasing the productivity of chicken egg SMEs in the community service scheme.

show abstract

“…Nowadays, machine learning and other approaches is widely utilized to solve a variety of difficult problems, such as making predictions and performing pattern recognition [5][6][7][8][9][10][11][12][13][14][15] . In modern era, there are others optimization techniques which is used in antenna structures like heuristic optimization techniques like genetic algorithms and particle swarm optimization [16][17][18][19][20] , but these algorithms examine for solution which is optimal by analyzing the output on individual data points and generating new and possibly improved search directions until a global maxima or minima is identified.…”

Section: Prerequisites For the Publicationmentioning

confidence: 99%

Design and Development of Machine Learning Assisted Cylindrical Dielectric Resonator Antenna

Ranjan¹,

Yadav²,

Gupta³

et al. 2023

Evergreen

View full text Add to dashboard Cite

The design of a Dielectric Resonator Antenna (DRA) utilizing machine learning approaches is presented in this study. Antennas are an essential part of a wireless network. An antenna with a decent design will reduce system requirements and improve overall system performance. Full-wave electromagnetic simulation is exact and necessary in antenna design. Still, it takes more time to perform, resulting in enormous challenges in designing, optimizing, and performing sensitivity analysis. The data in this study is subjected to several Machine Learning (ML) algorithms for optimizing antenna efficiency and S-parameter value. To expedite antenna design, machine learning-assisted optimization (MLAO) has proven to be the most effective method. A wide range of regression techniques, including as Support Vector Machine (SVM), Decision Tree Regression (DTR), and Random Forest Regression (RFR), have been used in machine learning (ML) techniques to develop antenna models, enabling quick response prediction and optimum S11 value. Multiple MLAO algorithms have been implemented using these machine-learning techniques for various applications out of which Decision Tree Regression and Random Forest Regression outperforms among all the above-mentioned algorithms with 99.98% and 99.99% of accuracy. First, a broad overview of recent advancements in ML approaches for antenna modelling is presented.

show abstract

Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network

Cited by 8 publications

References 0 publications

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Untitled

Design and Development of Machine Learning Assisted Cylindrical Dielectric Resonator Antenna

Contact Info

Product

Resources

About