Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As the time advances, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. Cancer research is one of the major research areas in the medical field. Accurate prediction of different tumor types has great value in providing better treatment and toxicity minimization on the patients. To minimize it, the data mining algorithms are important tool and the most extensively used approach to classify gene expression data and plays an important role for cancer classification. One of the major challenges is to discover how to extract useful information from datasets. This research is based on recent advances in the machine learning based microarray gene expression data analysis with three feature selection algorithms.
A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.
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