Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Academic Data Modelling based on Fuzzy-Genetic Algorithm


Affiliations
1 Department of Computer Science, Kalinga University, Raipur, Chhattisgarh, 492101, India
2 Department of Chemistry, Government Engineering College, Raipur, Chhattisgarh, 492015, India
3 Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology University, Vellore, Tamil Nadu, 632014, India
     

   Subscribe/Renew Journal


The data of student's academic performance is studied as the application of fuzzy-genetic(FG) approach. Discrete and continuous data interpretation are overviewed by the linguistic classes and then its representation over the fuzzy-genetic algorithm in this chapter. This dual method has the advantages are noticed as classvariable transformation, language-number generalisation and characteristic-operation simulation. Fuzzy-genetic algorithm is a tool to study the data over the representation of randomness to weight. Hence the data represents as the discrete classes by itself as the self-organized operator under the fuzzy-genetic technique.

Keywords

Data Analysis, FG.
Subscription Login to verify subscription
User
Notifications
Font Size


Abstract Views: 320

PDF Views: 7




  • Academic Data Modelling based on Fuzzy-Genetic Algorithm

Abstract Views: 320  |  PDF Views: 7

Authors

Swati Jain
Department of Computer Science, Kalinga University, Raipur, Chhattisgarh, 492101, India
Vikas Kumar Jain
Department of Chemistry, Government Engineering College, Raipur, Chhattisgarh, 492015, India
Sunil Kumar Kashyap
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology University, Vellore, Tamil Nadu, 632014, India
Sanjay Kumar
Department of Computer Science, Kalinga University, Raipur, Chhattisgarh, 492101, India

Abstract


The data of student's academic performance is studied as the application of fuzzy-genetic(FG) approach. Discrete and continuous data interpretation are overviewed by the linguistic classes and then its representation over the fuzzy-genetic algorithm in this chapter. This dual method has the advantages are noticed as classvariable transformation, language-number generalisation and characteristic-operation simulation. Fuzzy-genetic algorithm is a tool to study the data over the representation of randomness to weight. Hence the data represents as the discrete classes by itself as the self-organized operator under the fuzzy-genetic technique.

Keywords


Data Analysis, FG.