Neural Network Based Heart Disease Prediction

Shivappriya, S. N.; Navaneethakrishnan, R.; Raj, Kiran S.; Abirami, M.; Chidhambaram, S.

doi:10.1109/icaeca52838.2021.9675637

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…As mentioned earlier, in some articles, the system has five membership functions for each input and output [29]. The purpose of this article is to simplify the controller as much as possible.…”

Section: Optimization Of Flcmentioning

confidence: 99%

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

Samavat

Nazari

Ghalehnoie

et al. 2023

International Journal of Energy Research

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With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

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Section: Optimization Of Flcmentioning

confidence: 99%

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

Samavat

Nazari

Ghalehnoie

et al. 2023

International Journal of Energy Research

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ANN Based Heart Disease Prognosis

Alur,

Kopparad,

Ravikumar

2023

JoAII

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Heart disease is a major health concern which is responsible for cause of death worldwide. The different factors that cause heart disease includes inadequate blood flow due to fatty plaques in the arteries, stress, family history etc. Early and accurate identification of heart disease can help people take appropriate preventative action, by which the death rate can be reduced. Artificial neural networks (ANNs), have shown good results in different medical applications as they are able to learn complex patterns from the large datasets. An ANN-based models have the capacity to learn intricate patterns from tremendous datasets, thus it is possible to early detection and medication. In this paper, we introduce a machine learning method that makes use of ANNs to predict heart disease utilizing a variety of risk indicators, including age, sex, blood pressure, cholesterol levels, and other important clinical features. The model is trained on this dataset.

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Neural Network Based Heart Disease Prediction

Cited by 2 publications

References 4 publications

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

ANN Based Heart Disease Prognosis

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