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International Journal of Neutrosophic Science |

Pentapartitioned Neutrosophic Pythagorean Continuous Mappings

A Pentapartitioned neutrosophic set (PNS) is a powerful structure where we have five components Truth, Falsity, Ignorance , Contradiction and unknown . And also it generalizes the concept of fuzzy, intuitionistic and neutrosophic set. In this paper , we applying the idea of continuous function to Pentapartitioned Neutrosophic Pythagorean Sets[PNPS]. Also, we interrelate with other functions and its properties are also studied.

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Journal of Neutrosophic and Fuzzy Systems |

Optimizing Predictions of Brain Stroke Using Machine Learning

Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. In any of these cases, the brain becomes damaged or dies. Our brain controls every action in our body, like how many hormones are produced and released, breathing, memory, and everything. If the flow of blood to the brain gets occluded, then the cells in the brain start to die within a moment due to the lack of oxygen. This eventually causes strokes. Stroke is one of the most common causes for death globally. According to the World Health Organization (WHO), stroke is responsible for 11% of global deaths. So, in this paper, we propose a novel machine learning model with supervised learning techniques that can predict whether a person is likely to get a stroke or not by taking medical inputs such as medical risk factors which can cause strokes like smoking status, heart disease, glucose value, and hypertension. This paper compares various state-of-the-art machine learning algorithms, such as the Support Vector Machine (SVM), random forest, KNN algorithms, etc. Our simulation results show that the proposed scheme increases accuracy significantly (94.6%) and improves system performance.

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Journal of Neutrosophic and Fuzzy Systems |

Optimizing Predictions of Brain Stroke Using Machine Learning

Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. In any of these cases, the brain becomes damaged or dies. Our brain controls every action in our body, like how many hormones are produced and released, breathing, memory, and everything. If the flow of blood to the brain gets occluded, then the cells in the brain start to die within a moment due to the lack of oxygen. This eventually causes strokes. Stroke is one of the most common causes for death globally. According to the World Health Organization (WHO), stroke is responsible for 11% of global deaths. So, in this paper, we propose a novel machine learning model with supervised learning techniques that can predict whether a person is likely to get a stroke or not by taking medical inputs such as medical risk factors which can cause strokes like smoking status, heart disease, glucose value, and hypertension. This paper compares various state-of-the-art machine learning algorithms, such as the Support Vector Machine (SVM), random forest, KNN algorithms, etc. Our simulation results show that the proposed scheme increases accuracy significantly (94.6%) and improves system performance.

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Journal of Neutrosophic and Fuzzy Systems |

Optimizing Predictions of Brain Stroke Using Machine Learning

Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. In any of these cases, the brain becomes damaged or dies. Our brain controls every action in our body, like how many hormones are produced and released, breathing, memory, and everything. If the flow of blood to the brain gets occluded, then the cells in the brain start to die within a moment due to the lack of oxygen. This eventually causes strokes. Stroke is one of the most common causes for death globally. According to the World Health Organization (WHO), stroke is responsible for 11% of global deaths. So, in this paper, we propose a novel machine learning model with supervised learning techniques that can predict whether a person is likely to get a stroke or not by taking medical inputs such as medical risk factors which can cause strokes like smoking status, heart disease, glucose value, and hypertension. This paper compares various state-of-the-art machine learning algorithms, such as the Support Vector Machine (SVM), random forest, KNN algorithms, etc. Our simulation results show that the proposed scheme increases accuracy significantly (94.6%) and improves system performance.

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Journal of Neutrosophic and Fuzzy Systems |

Applying Machine Learning Techniques To Maximize The Performance of Loan Default Prediction

In peer-to-peer (P2P) lending, borrowers would access loans with lower interest rates than what they usually got from traditional lenders. People can directly borrow from the P2P platform with the rules that make them easy to borrow loans and invest free funds into P2P, which can benefit both borrowers and lenders. However, the easy way to borrow loans comes with risks. One of the major issues is that borrowers may default on the loan taken. In such cases, they can get loans quickly from P2P online platforms without any bank interferences. Thus, the lender can calculate his risk for loan default. In this project, we consider the P2P lending data to predict the loan default reassuring the lender to continue providing loans in the future. In our analysis, we consider the Logistic Regression, Naive Bayes, Random Forest, K Nearest Neighbour, and Decision tree to classify loan data based on their likelihood of default. The simulation result in our algorithm provides a significant accuracy of 94.6%.

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Journal of Neutrosophic and Fuzzy Systems | 2022

Optimizing Predictions of Brain Stroke Using Machine Learning

Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. In any of these cases, the brain becomes damaged or dies. Our brain controls every action in our body, like how many hormones are produced and released, breathing, memory, and everything. If the flow of blood to the brain gets occluded, then the cells in the brain start to die within a moment due to the lack of oxygen. This eventually causes strokes. Stroke is one of the most common causes for death globally. According to the World Health Organization (WHO), stroke is responsible for 11% of global deaths. So, in this paper, we propose a novel machine learning model with supervised learning techniques that can predict whether a person is likely to get a stroke or not by taking medical inputs such as medical risk factors which can cause strokes like smoking status, heart disease, glucose value, and hypertension. This paper compares various state-of-the-art machine learning algorithms, such as the Support Vector Machine (SVM), random forest, KNN algorithms, etc. Our simulation results show that the proposed scheme increases accuracy significantly (94.6%) and improves system performance.  

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Venkata Sravan Telu mail -
Vinay Padimi mail
link https://doi.org/10.54216/JNFS.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2022

Applying Machine Learning Techniques To Maximize The Performance of Loan Default Prediction

In peer-to-peer (P2P) lending, borrowers would access loans with lower interest rates than what they usually got from traditional lenders. People can directly borrow from the P2P platform with the rules that make them easy to borrow loans and invest free funds into P2P, which can benefit both borrowers and lenders. However, the easy way to borrow loans comes with risks. One of the major issues is that borrowers may default on the loan taken. In such cases, they can get loans quickly from P2P online platforms without any bank interferences. Thus, the lender can calculate his risk for loan default. In this project, we consider the P2P lending data to predict the loan default reassuring the lender to continue providing loans in the future. In our analysis, we consider the Logistic Regression, Naive Bayes, Random Forest, K Nearest Neighbour, and Decision tree to classify loan data based on their likelihood of default. The simulation result in our algorithm provides a significant accuracy of 94.6%.

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Vinay Padimi mail -
Venkata Sravan Telu mail -
Devarani Devi Ningombam mail
link https://doi.org/10.54216/JNFS.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2022

Smart City's Security Model for Management of Image Data on Cloud

In modern world of advancement, cloud technology is taking its grip. COVID-19 also enhance the use of the cloud services. The cloud concept comes with the new advancement of technology, but with that data breaches and data hacks are also increasing. In the cloud-based environment, we suggested the model of security enhancement which, make use pf role-based security model where the files are shared according to the role and the access of such file is controlled using TPA validation and apart for that the concept of authentication of user is also suggested using the Pattern Lock based graphical system for pattern formation. The pattern obtained are validated using the various of online password or pattern strength validation tools, results which are obtained proves that pattern obtained through the proposed concept performs better against the attack.

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Pooja mail -
Manish Kumar Mukhija mail -
Satish Kumar Alaria mail
link https://doi.org/10.54216/JCHCI.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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