Journalism robot by using a computer algorithm, while maintaining the precision and reliability of the existing media refers to an article which is automatically created. In this paper, we introduce 'stock robot' of robot journalism which writes securities articles and describe artificial intelligence algorithms in stages. Key steps of stock robot implemented artificial intelligence algorithm through four steps of data collection and storage, key event extraction, article content production, and article production. This research has developed a stock robot that collects and analyzes data on social issues and stock indexes for the last 2 years. In the future, as the algorithm is further developed, it becomes possible to write securities articles quickly and accurately through social issues. It will also provide customized information tailored to the user's preferences.

The clustering of gene expression data is used to analyze the results of microarray studies. This method is often useful in understanding how a particular class of genes functions together during a biological process. In this study, we attempted to perform clustering using the Markov cluster (MCL) algorithm, a clustering method for graphs based on the simulation of stochastic flow. It is a fast and efficient algorithm that clusters nodes in a graph through simulation by computing probability. First, we converted the raw matrix into a sample matrix using the Euclidean distance of the genes between the samples. Second, we applied the MCL algorithm to the new matrix of Euclidean distance and considered 2 factors, namely, the inflation and diagonal terms of the matrix. We have turned to set the proper factors through massive experiments. In addition, distance thresholds, i.e., the average of each column data elements, were used to clearly distinguish between groups. Our experimental result shows about 70% accuracy in average compared to the class that is known before. We also compared the MCL algorithm with the self-organizing map (SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms.

In a cloud computing environment, a cryptographic service allows an information owner to encrypt the information and send it to a cloud server as well as to receive and decode encrypted data from the server which guarantees the confidentiality of shared information. However, if an attacker gains a coded data and has access to an encryption key via cloud server, then the server will be unable to prevent data leaks by a cloud service provider. In this paper, we proposed a key management server which does not allow an attacker to access to a coded key of the owners and prevents data leaks by a cloud service provider. A key management server provides a service where a server receives a coded public key of an information user from an owner and delivers a coded key to a user. Using a key management server proposed in this paper, we validated that the server can secure the confidentiality of an encryption key of data owners and efficiently distribute keys to data users

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