K-means Clustering-based Radio Neutron Star Pulsar Emission Mechanism

2020 ◽  
Vol 13 ◽  
Author(s):  
Shubham Shrimali ◽  
Amritanshu Pandey ◽  
Chiranji Lal Chowdhary
Keyword(s):  
Neural Networks ◽  
Neutron Star ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Emission Mechanism ◽  
Decision Tree Classifier ◽  
Emission Data ◽  
Pulsar Emission ◽  
Tree Classifier

: 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.

scholarly journals QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1734
Author(s):  
Cosmin Alexandru Bugeac ◽  
Robert Ancuceanu ◽  
Mihaela Dinu
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Logistic Regression ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Disk Diffusion ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Disk Diffusion Test ◽  
Tree Classifier

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

scholarly journals Landslide Vulnerability Assessment in Devikulam Taluk, Idukki District, Kerala Using Gis and Machine Learning Algorithms

2021 ◽  
Author(s):  
Bhargavi Gururajan ◽  
Arun nehru Jawaharlal
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Spatial Database ◽  
Machine Learning Algorithms ◽  
Kappa Statistics ◽  
Mountainous Area ◽  
Decision Tree Classifier ◽  
Linear Discriminant ◽  
Hilly Region ◽  
Tree Classifier

Abstract Landslide is a chronic problem that causes severe geographical hazard due to development activities and exploitation of the hilly region and it occurs due to heavy and prolongs rain flow in the mountainous area. Initially, a total of 726 locations were identified at devikulam taluk, Idukki district (India). These landslide potential points utilised to construct a spatial database. Then, the geo spatial database is then split randomly into 70% for training the models and 30% for the model validation. This work considers Seven landslide triggering factors for landslide susceptibility mapping. The susceptibility maps were verified using various evaluation metrics. The metrics are sensitivity, specificity, accuracy, precision, Recall, Matthews correlation efficient (MCE), Area Under the Curve (AUC), Kappa statistics, Mean Absolute Error (MAE), Mean Square Error (MSE).The proposed work with 5 advanced machine learning approaches assess the landslide vulnerability.It includes Logistic Regression (LR), K Nearest Neighbor (KNN), Decision tree classifier, Linear Discriminant Analysis (LDA) and Gaussian Naïve Bayes modelling and comparing their performance for the spatial forecast of landslide possibilities in the Devikulam taluk. In experimental results, Decision tree classifier performs the most reliable performance with an overall accuracy rate of 99.21%.

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