Sensor networks generally consist of a very great number of sensor nodes which will be spread into a vast environment and aggregate data out of it. The sensor nodes are afflicted with some limitations as follows memory, reception, communication as well as calculation capability, and battery power. The transmission of a great amount of extra data increases data transmission and proportionally increases the amount of energy and bandwidth for the data transmission. One solution for this issue is data aggregation. The results of aggregated data influence the accuracy and precision of the final result already gleaned from the base station. The main challenge in such networks is how to further elongate the network lifetime and among the factors doing so is the energy consumption or energy optimization. The clustering is one apt method in place for furthering the network life span. Respectively the clustering protocols have come up with a suitable method for the so called challenge or more simply put increasing the lifetime. In this paper the researchers attempt to bring forth yet another efficient protocol for data aggregation hinging around clustering which uses maximum residual energy and minimum distance for selecting the cluster-head to reduce the consumption of energy. The experimental results point to this very fact that Energy-Efficient Clustering Algorithm through Residual Energy and Average Distance (EECA-READ) attains very good performance.

Introduction: The major issue for the future of bioinformatics is the design of tools to determine the functions and all products of single-cell genes. This requires the integration of different biological disciplines as well as sophisticated mathematical and statistical tools. This study revealed that data mining techniques can be used to develop models for diagnosing high-risk or low-risk lifestyles for colorectal cancer. Method: In this retrospective study, a dataset consisting of information relevant to 84 patients and 225 healthy individuals with 25 attributes was collected. This information was on patients diagnosed from 2006 to the first quarter of 2014. The most widely used techniques in the medical informatics literature including support vector machine, Naive Bayes, decision tree, and k-nearest neighbor were used to develop the models. Results: The developed models are able to distinguish people's lifestyles efficiently. A welldeveloped non-technical measure can properly determine the true value of individual predictions, whether true or false, at actual costs, and indicate a true measure of the cost savings in the health system by each model. Among the developed models, only two models were able to meet the criteria set for use in the real world. Conclusion: The developed models should not only be technically evaluated, but should also be examined in terms of metrics accepted for the medical field as well as feasibility for real problem solving.