No abstract

Microarray data are becoming a more essential source of gene expression data for interpretation and analysis. To improve the detection accuracy of tumors, the researchers try to use the lowest feasible collection of the most gene expression studies, and relevant gene expression patterns are found. The purpose of this article is to use a data mining strategy and an optimized feature selection method focused on a limited dense tree forest classifier to evaluate and forecast colon cancer data. More specifically, merging the “gain information” and “Grey wolf optimization” was incorporated as a feature selection approach into the random forest classifier, to improve the prediction model’s accuracy. Our suggested technique can decrease the load of high-dimensional data, and it allows quicker computations. In this research, we provided a comparison of the analysis model with feature selection accuracy over model analysis without feature selection accuracy. The extensive experimental findings have shown that the suggested method with selecting features is beneficial, outperforming the good classification performances.

The term "big data" refers to an information processing system that combines different conventional data techniques. Big data also includes a large amount of personally identifiable and authenticated data, making privacy a major concern. Various techniques have been developed to provide security and efficient data processing. Machine learning is a form of data technology that deals with one of the most important and least understood aspects of the data collected. Deep learning algorithms, similar to machine learning algorithms, learn programmers automatically from data and are thought to improve the efficiency and security of large data sets. The efficiency of machine learning and deep learning in a sensitive environment was evaluated in this paper, which reviewed security problems in big data. This paper begins by providing an overview of machine learning and deep learning algorithms. The research then moves on to machine learning problems and challenges, as well as potential solutions. The investigation into deep learning principles of big data continues after that. Finally, the report examines approaches used in recent research developments and concludes with recommendations for the future.