The energy efficiency problem is addressed using the Cluster Head (CH) formation, data aggregation, and routing techniques. Therefore,an energy-aware routing algorithm named as protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC) and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures such as energy, alive nodes and throughput.
Knowledge discovery process is to provide effective rules from the existing data. The process of finding knowledge is a complex task when the data source is in the form of data streams and in fact the data source of class imbalance nature. To find the opt solutions for those problems research proposals are formulated by many researchers. The some of the unsolved problems in the literature for the above said problem is for very large data sources of data streams with class imbalance nature. In this paper, a novel proposal for class imbalance large data streams is presented with novel techniques of oversampling and a unique multi modal filtering technique and oversampling strategy as Multimodal Increment Over Sampling for Data Streams (MIOSDS) for efficient implementation of proposal for a better solution. The experimental simulations are conducted on three large datasets with different domains. The results generated are good compared with benchmark evaluation metrics.
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