In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods.
The research aimed to study the financial markets liquidity and returns of common stocks , Take the research the theoretical concepts associated with each of the liquidity of financial markets and returns of common stocks , As well as the use of mathematical methods in the practical side to measure market liquidity and Stocks Return, the community of research in listed companies in Iraqi stock exchange that have been trading on its stock and number 85 joint-stock company, The research was based to one premise, there is a statistically significant effect for the liquidity of the Iraqi stock exchange on returns of common stocks to traded companies in which , Using th
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