Deep Convolutional Generative Modeling for Artificial Microstructure Development of
Aluminum-Silicon Alloy

Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures.

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Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy

Indian Journal of Data Mining ◽

10.54105/ijdm.a1603.051121 ◽

2021 ◽

pp. 1-6

Author(s):

Akshansh Mishra ◽

◽

Tarushi Pathak ◽

Keyword(s):

Machine Learning ◽

Generative Adversarial Networks ◽

Microstructure Development ◽

Machine Learning Technique ◽

Silicon Alloy ◽

Aluminum Silicon Alloy ◽

Adversarial Networks ◽

Generative Modeling ◽

Learning Technique ◽

The Given

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Detection of Parkinson Diseases with More Accuracy using Machine Learning Technique

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1014.09811s19 ◽

2019 ◽

Vol 8 (11S) ◽

pp. 67-70

Keyword(s):

Machine Learning ◽

Calculation Procedure ◽

Medical Procedure ◽

Single Test ◽

Machine Learning Technique ◽

The World ◽

Learning Technique ◽

The Given ◽

Parkinson Diseases

Parkinson's malady is the most common neurodegenerative confusion influencing more than 10 million individuals around the world. There is no single test which can be regulated for diagnosing Parkinson's illness. In light of these challenges, to explore a machine learning way to deal with precisely analyze Parkinson's, utilizing a given dataset. To keep this issue in medicinal parts need to anticipate the malady influenced or not by discovering exactness figuring utilizing AI strategies. The point is to examine AI based methods for Parkinson sickness by expectation results in best precision with finding arrangement reportIn the beginning times of Parkinson ailment, your face may appear practically zero articulation. Your arms may not swing when you walk.. At times, your specialist may recommend medical procedure to manage certain locales of your cerebrum and improve your indications.To propose, an AI based strategy to precisely foresee the illness by discourse and tremor manifestations by expectation results as best exactness from looking at administer grouping AI calculations. Also, to look at furthermore, talk about the execution of different AI calculations from the given transport traffic division dataset with assessment arrangement report, distinguish the outcome demonstrates that the viability of the proposed AI calculation procedure can be thought about with best exactness with accuracy, Recall and F1 Score.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

2017 ◽

Author(s):

Benjamin Sanchez-Lengeling ◽

Carlos Outeiral ◽

Gabriel L. Guimaraes ◽

Alan Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Learning Community ◽

Chemical Species ◽

Material Design ◽

Organic Photovoltaic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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