An approach of electric power demand forecasting using data-mining method: a case study of application of data-mining technique to improve decision making

Author(s):  
Toshio Sugihara
2021 ◽  
Vol 9 (211) ◽  
pp. 1-27
Author(s):  
SHEILA DE SOUZA CORREA DE MELO ◽  
ALEXANDRA DE SOUZA CORREA DE MELO

This research focuses on the registration of trademarks made by açaí exporting companies in the State of Pará with the National Institute of Industrial Property (INPI). The research was methodologically oriented as a case study and was performed using data mining technique in official databases such as INPI e-marcas and DataScience Brasil. The results obtained in the research point to the low percentage of protection of intangible assets of the brand type by this group of companies and the result shows all the distinctive signs used by companies in their products and/or services. Our indication is that brand registration is included in the companies' business plan as a priority for better positioning of their products and/or services and that there is an incentive from the public administration of Pará through educational campaigns regarding the importance of brand registration.


2015 ◽  
Vol 21 (2) ◽  
pp. 95
Author(s):  
Hyo Soung Cha ◽  
Tae Sik Yoon ◽  
Ki Chung Ryu ◽  
Il Won Shin ◽  
Yang Hyo Choe ◽  
...  

2016 ◽  
Vol 139 (6) ◽  
pp. 46-47
Author(s):  
M. Ashrafa ◽  
D. Asha ◽  
D. Radha ◽  
M. Sangeetha ◽  
R. Jayaparvathy

Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.


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