• Manpreet kaur
• Amit Chhabra

Abstract-The clustering mechanisms are devised to minimize the execution time associated with the sensors. To this category, chain based mechanisms such as stable election protocol is created. In this protocol entire network is divided into three parts. First part includes normal transaction. These transactions participate in detection of frauds if score attribute exceeds threshold value. Second transactions are known as least fraudulent if score attribute is within range. There are special node analyzer. This node monitor the execution time of conducted transaction. The proposed system integrated neural network based mechanism to eliminate execution time during the fraud detection phase. To accomplish the simulation is desired manner the rule base is defined. It has been analyzed about algorithm for without neural network based approach protocol became complex due to fuzzy technique involvement and thus further delay has been increased for fraud detection. To overcome this problem the algorithm can be divided in small problems using neural network techniques and the entire process can be enhanced by using NFBFD protocol in which neuro-fuzzy system involvement.

Keywords: Fuzzy system, Social media, Clustering, NFBFD

"Enhanced Neuro Fuzzy Approach for Detection of Credit Card Frauds", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.174-181, May-2019, Available :http://www.jetir.org/papers/JETIR1905930.pdf

"Enhanced Neuro Fuzzy Approach for Detection of Credit Card Frauds", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp174-181, May-2019, Available at : http://www.jetir.org/papers/JETIR1905930.pdf