A comparative assessment of measures of similarity of fuzzy values
Fuzzy Sets and Systems
A note on the value similarity of fuzzy systems variables
Fuzzy Sets and Systems
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mining e-mail content for author identification forensics
ACM SIGMOD Record
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In a country like India, the growth rate of the number of academic institutions is at par with the lost student rate. Hence when a lost student is found we need to identify the student on the basis of information such as name of the student, institution name where he studies, class or branch of the student, etc. But the fact is that in most of the cases one never gets complete and precise information to identify a lost student. Hence, in such environment a soft computing model can be an attractive alternative to identify a lost student on the basis of imprecise or partial information. This paper presents a soft computing model for identifying lost student on the basis of imprecise and partial information. In this model student information is represented as a symbolic student object. Symbolic student object is further processed using a fuzzy symbolic model for identifying the lost student. The authors have devised a symbolic knowledge base which acts as a repository of information pertaining to student of different institutions that assist in creating student object and identifying the lost student. A fuzzy technique "symbolic similarity measure" is devised for generating symbolic student object and mapping the symbolic student object with student information present in knowledge base. This system has been tested scrupulously and an efficiency of above 90% has been achieved in identifying the lost student.