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Predicting the outcome of a graft transplant with high level of accuracy is a challenging task. To answer the challenge, data mining can play a significant role. The goal of this study is to compare the performances and features of an Artificially Intelligent (AI)-based data mining technique namely Artificial Neural Network with Logistic Regression as a standard statistical data mining method to predict the outcome of kidney transplants over a 2-year horizon. The methodology employed utilizes a dataset made available to us from a kidney transplant database. The dataset embodies a number of important properties, which make it a good starting point for the purpose of this research. Results reveal that in most cases, the neural network technique outperforms logistic regression. This study highlights that in some situations, different techniques can potentially be integrated to improve the accuracy of predictions.

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Abstract-Scalar multiplication is time consuming operation of ECC when implemented on wireless sensor nodes. The wireless sensor node consists of 8 bit micro controller and limited memory for the storage. The scalar multiplication process can be accelerated with the sliding window method which has two stages namely pre computation and an evaluation stage. Points for use in the evaluation stage are computed in the pre computation stage. The scalar multiplication is carried out in the evaluation stage with the addition of pre computed points. The number of pre computations depends on the window size of sliding window method. More is the window size, more are the pre computations and more is the memory required for the storage .This is the well-known draw-back of the sliding window method when implemented on WSN Nodes. This research paper proposes sliding window method with flexible window size for scalar multiplication on wireless sensor nodes. The flexible window size will prevent sensor node failures due to stack overflow.

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