Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. Methods: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. Result: The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF , GUCA2A , CA7 , and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA , USP20 , and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4 , ANKHD1 - EIF4EBP3 , PLXNC1 , and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. Conclusion: The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.

House price predictions are a crucial reflection of the economy; sometimes house prices include the land prices and demand of the place and location. The house price and land price are two different things, but both are important for both buyers and sellers. This chapter introduced the combination of ML and DL approaches to predict the house price with the updated regression algorithm. The algorithm named as ‘Mopuri algorithm' reads the 14 attributes like crime rate, population density, rooms, etc. and produces the cost estimation result as a prediction. The proposed model accurately estimates the worth of the house as per the given features. The results of the model tested with the different datasets existing in the Kaggle data source using Python libraries with the Jupyter platform and continuation of the model using the Android OS to develop the smart home web-based application.

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