Gene expression profiling has been successfully use to identity potential cancer diagnosis and therapeutic target in the past few years. Since there are often large number of potential genes, an important issue is to find a small subset of genes that are differentially expressed between different clinical outcomes (or) related subset to cancer patients survival time, and thus can be used to build prognosis and predictors.

Colon Rectum Cancer is one of the leading cause of cancer deaths worldwide. In this paper, a comparative study is made for the prediction of Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). For more than twenty decades, Box Jenkin's Auto Regressive Integrated Moving Average (ARIMA) technique is one of the most sophisticated extrapolation method for prediction. It predicts the values in a time series as a linear combination of its own past values, past errors and current and past values by using the concept of time Series. Artificial Neural Network (ANN) is a modern Non Linear Technique used for prediction that involve learning and pattern recognition. Based on the data the model is was modeled is designed by using two techniques for a period of 50 years (from 1960 to 2010) and the Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error(RMSE) are obtained to evaluate the accuracy of the models. Results show that ANN model perform much better than the traditional ARIMA model. Since early detection of cancer is the key to improve survival rate, prediction of Colon Rectum Cancer will greatly facilitate the doctors in the diagnosis of the disease.

Aluminum alloys are currently used in a wide variety of industries, and strong aluminum alloys are required for the creation of new components. As a result, multiple scientists are experimenting with various compositions of hybrid aluminum metal matrix composites. The purpose of this experiment was to generate hybridization on aluminum alloy 7076 using stir-casting and nano zirconium dioxide and BN reinforcements. Taguchi’s approach was used to optimize the stir-casting process criteria in this investigation. The parameters employed in this investigation were agitation speed, agitation time, and temperature. The chosen constraints are the percentage of reinforcement (0–12%), the agitation speed, the agitation time, and the molten state temperature. We used a wear tester and a Vickers hardness tester to determine the wear and microhardness of the produced stir casting materials. By optimizing wear parameters, the least wear rate is determined.

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