Detection of Parkinson Diseases with More Accuracy using Machine Learning Technique

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

10.35940/ijdm.a1603.051121 ◽

2021 ◽

Vol 1 (1) ◽

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

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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Human Behavioral Analyzer using Machine Learning Technique

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8531.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 5150-5154

Keyword(s):

Mental Health ◽

Machine Learning ◽

Health Issues ◽

Machine Learning Technique ◽

Behavioral Traits ◽

Low Level ◽

Mental Health Issues ◽

The World ◽

Learning Technique ◽

The Status

This research paper aims to the variety of people suffering from medium or low level of mental agitation i.e. being stress, depression etc. As countries like India in which more than 65% of the population is under the age of 35 [1] are continuously falling down the rank in the World Happiness Report, In 2018, India ranked on 133rd [2] position, and it can be concluded that the majority of population is facing mental health issues and does not have proper methods to analyze their mental health and take appropriate precautions and also to provide automated solutions to the Industry for hiring a productive group of people those are cool minded and sensible, the purpose of this research is to analyze the mental health of a person using behavioral traits of the person that are entered by the person or chosen from a list of given options throughout the analyses procedure of the application in which surveyed data is tested through Machine Learning to determine the status of mental health of a person and associated stress levels and suggesting the user with appropriate recommendations

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Neuro-Based Prognosticative Analytics for Parkinson Disease using Random Forest Approach

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

10.35940/ijitee.j7434.0991120 ◽

2020 ◽

Vol 9 (11) ◽

pp. 11-15

Keyword(s):

Machine Learning ◽

Random Forest ◽

Parkinson Disease ◽

Neurodegenerative Disorder ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Single Test ◽

Specificity And Sensitivity ◽

The World ◽

The Given

Parkinson’s malady is the most current neurodegenerative disorder poignant quite ten million folks across the world. There's no single test at which may be administered for diagnosis Parkinson’s malady. Our aim is to analyze machine learning based mostly techniques for Parkinson malady identification in patients. Our machine learning-based technique is employed to accurately predict the malady by speech and handwriting patterns of humans and by predicting leads to the shape of best accuracy and in addition compare the performance of assorted machine learning algorithms from the given hospital dataset with analysis and classification report and additionally determine the result and prove against with best accuracy and exactness, Recall ,F1 Score specificity and sensitivity.

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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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Silkworm Yield Prediction in Attibele Region using Machine Learning Technique

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1587.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1172-1177

Keyword(s):

Machine Learning ◽

Climatic Change ◽

Research Work ◽

Climatic Changes ◽

Climatic Conditions ◽

Yield Prediction ◽

Machine Learning Technique ◽

Karnataka State ◽

The World ◽

Learning Technique

Sericulture is the processes of cultivation of silkworms to produce cocoons which are used for the production of silk or to produce eggs. This research work is carried out with respect to the Attibele region (Karnataka State in India). There are various species of silkworms that are grown in the world, and the yield of silk varies with climatic change. Why climatic changes important for rearing of silkworms? Because they are very sensitive for temperature and humidity fluctuations. For example if the temperature is high and humidity is low or the temperature is low and humidity is high, the silkworms become unhealthy. In this paper we have calculated the climatic conditions that is to be maintained in the future for obtaining the optimal yield of the silkworms. The work also aims to provide the remedies to be taken for the betterment of the production, both in terms of farm-land and cocoons.

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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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