:
The aim of this paper is to work on K-means clustering-based radio neutron star pulsar emission mechanism.
Background:
The pulsars are a rare type of neutron star that produces radio rays. Such type of rays are detectable on earth and it attracts scientists because of its concern with space-time, interstellar medium, and states of matter. During the rotation of pulsar rays, it emits the rays in the whole sky and after crossing the threshold value, the pattern of radio emission broadband detected. As rotation speed of pulsar increases then accordingly the types of the pattern produced periodically. Every pulsar emits different patterns which are a little bit different from each other which is fully depends on its rotation. The detected signals are known as a candidate. Its length of observation can determine it and it is average of all rotation of pulsar.
Objective:
The main objectives of this radio neutron star pulsar emission mechanism are: (a) Decision Tree Classifier (2) K-means Clustering (3) Neural Networks.
Method:
The Pulsar Emission Data was broken down into two sets of data: Training Data and Testing Data. The Training Data used to train the Decision Tree The algorithm, K-means clustering, and Neural Networks to allow it to identify, which attributes (Training Labels), are useful for identification of Neutron Pulsar Emissions.
Results:
The analysis is using multiple machine learning algorithms; it concluded that using neural networks is the best possible method to detect pulsar emissions from neutron stars. The best result achieved is 98% using Neural Networks.
Conclusion:
There are so many benefits of pulsar rays in different technology. Earth can detect pulsar ray from low orbit. Earth can completely absorb X-ray in the atmosphere and from these; we can say that the wavelength is limited to those who do not have an atmosphere like space. The result we got according to that we can say that the algorithm we used successfully used for detecting the pulsar signals.
Improving the Performance of a Proxy Cache Using Very Fast Decision Tree Classifier
