On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification

2017 ◽  
pp. 183-200 ◽  
Author(s):  
Aleksei Zhukov ◽  
Nikita Tomin ◽  
Denis Sidorov ◽  
Victor Kurbatsky ◽  
Daniil Panasetsky
Keyword(s):  
Random Forest ◽  
Power Systems ◽  
Data Stream ◽  
Security Assessment ◽  
Random Forest Algorithm ◽  
Systems Security ◽  
On Line ◽  
Using Data

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

On-Line Steady State Security Assessment of Power Systems by SMES

2005 ◽  
Vol 15 (2) ◽  
pp. 1923-1926 ◽  
Author(s):  
Y. Min ◽  
Z. Lin ◽  
J. Qiao ◽  
X. Jiang
Keyword(s):  
Steady State ◽  
Power Systems ◽  
Security Assessment ◽  
State Security ◽  
On Line

scholarly journals Ensemble methods of classification for power systems security assessment

2019 ◽  
Vol 15 (1) ◽  
pp. 45-53 ◽  
Author(s):  
A. Zhukov ◽  
N. Tomin ◽  
V. Kurbatsky ◽  
D. Sidorov ◽  
D. Panasetsky ◽  
...  
Keyword(s):  
Power Systems ◽  
Ensemble Methods ◽  
Security Assessment ◽  
Systems Security

scholarly journals A New Guideline for Security Assessment of Power Systems with a High Penetration of Wind Turbines

2020 ◽  
Vol 10 (9) ◽  
pp. 3190 ◽  
Author(s):  
Bahram Shakerighadi ◽  
Saeed Peyghami ◽  
Esmaeil Ebrahimzadeh ◽  
Frede Blaabjerg ◽  
Claus Leth Back
Keyword(s):  
Power Systems ◽  
Wind Turbines ◽  
Test System ◽  
Security Assessment ◽  
Pv Systems ◽  
Systems Security ◽  
Electrical Grids ◽  
High Penetration ◽  
Security Challenges ◽  
Do So

By the increase of the penetration of power-electronic-based (PE-based) units, such as wind turbines and PV systems, many features of those power systems, such as stability, security, and protection, have been changed. In this paper, the security of electrical grids with high wind turbines penetration is discussed. To do so, first, an overview of the power systems’ security assessment is presented. Based on that, stability and security challenges introduced by increasing the penetration of wind turbines in power systems are studied, and a new guideline for the security assessment of the PE-based power systems is proposed. Simulation results for the IEEE 39-bus test system show that the proposed security guideline is necessary for PE-based power systems, as the conventional security assessments may not be able to indicate its security status properly.

scholarly journals Crime analysis in India using data mining techniques

2018 ◽  
Vol 7 (2.6) ◽  
pp. 253 ◽  
Author(s):  
Deepika K K ◽  
Smitha Vinod
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Random Forest ◽  
Crime Analysis ◽  
Random Forest Algorithm ◽  
Indian States ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Crime Detection ◽  
Using Data

An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.  

scholarly journals Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

2021 ◽  
Vol 5 (2) ◽  
pp. 640
Author(s):  
Mulkan Azhari ◽  
Zakaria Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Svm Algorithm ◽  
Testing Data ◽  
C4.5 Algorithm ◽  
Bayes Algorithm ◽  
Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

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