Market basket analysis is based upon the identification and analysis of purchasing patterns of the customers. The problem with market basket analysis is the varying needs of the customers with respect to seasons and time and so we need to perform it time and again. Another problem that arises while doing market basket analysis is with Apriori algorithm in which we need to find candidate sets and frequent item-sets time and again. In this paper, we are suggesting the use of artificial neural network technique to overcome these problems. We have used single layer feed-forward partially connected neural network technique for this purpose.
Keywords-market basket analysis; feed-forward neural network; apriori algorithmI.
This paper presents a concept of solving the back-up node failure problem of the RDEEP (Reliable Distributed Energy Efficient Protocol). This technique will also help to make the wireless sensor network more energy efficient. The energy efficiency is one of the most important factors in case of wireless sensor networks. In this era of green computing, ERDEEP (Enhanced Reliable Distributed Energy Efficient Protocol) provides more energy efficient network and makes it more reliable as compared to the RDEEP. This scheme is based upon the principle that the cluster head will choose at least three backup nodes on the basis of best Nodial distances from each other. In case of failure of the back-up node new backup node will perform all the functions of the cluster head.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.