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Introduction:The current study investigates the utilization and performance of machine learning (ML) algorithms in the cognitive task of finding the correlation between numerical parameters of the human brain activation during gaming. We hypothesize that our integrated feature extraction platform is able to distinguish between different psychosomatic conditions in the gaming process as measured by the functional near-infrared brain imaging technique.Methods: For demonstration, the decision-making process was constructed in the experiment environment that combined gaming simulator, such as the Iowa Gaming Task (IGT), with functional near-infrared spectroscopy (fNIRS) as the neuroimaging technique. Features of fNIRS levels were extracted, averaged, and synchronized by time with the IGT dataset to predict the task score inside ML algorithms, such as multiple regression, classification and regression trees, support vector machine, artificial neural network, and random forest. For findings validation, the experiment data were resampled by training and testing sets. Further, a training dataset was used to train the ML algorithms, and prediction accuracy was estimated by repeated cross-validation methods and compared by R squared and root mean square error (RMSE). The model with the best accuracy was used with the testing dataset and finalized the experiment.Results: During the experiment, the highest correlation was identified in the fourth block between the oxy-hemoglobin signal and IGT score in average value (0.24) and signal feature (0.57). Such relationship is due to block 4 characterization as "conceptual" period when participants task experience reaches the maximum, and rewards raise accordingly. Simultaneously, ML algorithms, constructed based on training data set, demonstrate acceptable performance, and RMSE as the primary performance metric dynamically increases from block 1 to block 5, from the state of uncertainty and unknown to the certainty and risky. In contrast, R squared decreases during the same transition. In most IGT blocks, the best fitted model was determined as support vectorThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Identity and Access Management (IAM) and Single Sign on (SSO) are two security concepts that are related to each other. IAM governs the user access in an organization whereas SSO facilitates the user by authenticating to one centralized application and not having to re-authenticate when trying to access other applications. This paper addresses the different benefits that an IAM and SSO tool can provide to reduce the security risk within an organization. Since, authentication and authorization are one of the major concerns in the cyber security; this paper analyzes common problems that are faced during authentication and authorization. We have also analyzed prior researches that have been done in the IAM and SSO space along with conducting a survey to understand the different issues and benefits of IAM and SSO. From the survey that has been conducted, we have addressed different issues when implementing IAM and SSO solutions along with understanding the architecture, in which these solutions have been deployed into. The surveys conducted have been compared with prior researches done in IAM and SSO space to understand the benefits that the solution provides. The results from the survey have been analyzed to provide the best practices when implementing IAM and SSO solutions along with the benefits provided by the solution.

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