Efficient Finger Print Image Classification and Recognition using Neural Network Data Mining

Author(s):  
K. Umamaheswari ◽  
S. Sumathi ◽  
S.N. Sivanandam ◽  
K.K.N. Anburajan
Author(s):  
Jyoti Bhola ◽  
Gaurav Dhiman ◽  
Tarun Singhal ◽  
Guna Sekhar Sajja

Over the last few years, academic institutions have conducted a number of programmes to help school boards, colleges, and schools of autism spectrum educating pupils (ASD). Autism spectrum disorder (ASD) is a complicated neurological disorder which affects many skills over a lifetime. The main aim of the chapter is to examine the topic of autism and identify autism levels with furious logic classification algorithms using the artificial neural network. Data mining has generally been recognized as a method of decision making to promote higher use of resources for autism students.


2021 ◽  
Vol 4 (2) ◽  
pp. 205-216
Author(s):  
Amri Muliawan Nur ◽  
◽  
Imam Fathurrahman ◽  
Yahya Yahya ◽  
◽  
...  

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976


2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Salisu Aliyu ◽  
Aminu S Zakari ◽  
Muhammad Ismail ◽  
Mohammed A Ahmed

Solar energy has attracted enormous attention as it plays an essential role in meeting the ever growing sustainable and environmental friendly energy demand of the world. Due to the high cost of calibration and maintenance of designated measuring instruments, solar radiation data are limited not only in Nigeria but in most parts of the world. The optimal design of solar energy systems requires accurate estimation of solar radiation. Existing studies are focused on the analysis of monthly or annual solar radiation with less attention paid to the determination of daily solar radiation. Estimating daily solar radiation envisages high quality and performance of solar systems. In this paper, an Artificial Neural Network data mining model is proposed for estimating the daily solar radiation in Kano, Kaduna and Katsina, North West Nigeria. Daily Solar radiation data for 21years collected from the Nigerian Metrological Agency were used as training/testing data while developing the model. Two statistical indicators: coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the model. An RMSE of 0.47 and 0.48 was obtained for the training and testing dataset respectively, while an R2 of 0.78 was obtained for both the training and testing dataset. The overall results showed that artificial neural network exhibits excellent performance for the estimation of daily solar radiation.Keywords— Artificial Neural Network, Data mining, Solar Radiation 


2017 ◽  
Vol 25 (1) ◽  
pp. 20-32
Author(s):  
Maryam Kazemi ◽  
Hossein Mehdizadeh ◽  
Ardeshir Shiri ◽  
◽  
◽  
...  

Sign in / Sign up

Export Citation Format

Share Document