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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

Role-based access control (RBAC) continues to gain popularity in the management of authorization concerning access to knowledge assets in organizations. As a socio-technical concept, the notion of role in RBAC has been overemphasized, while very little attention is given to the precursors: role strain, role ambiguity, and role conflict. These constructs provide more significant insights into RBAC design in Knowledge Management Systems (KMS). KMS is the technology-based knowledge management tool used to acquire, store, share, and apply knowledge for improved collaboration and knowledge-value creation. In this paper, we propose eight propositions that require future research concerning the RBAC system for knowledge security. In addition, we propose a model that integrates these precursors and RBAC to deepen the understanding of these constructs. Further, we examine these precursory constructs in a socio-technical fashion relative to RBAC in the organizational context and the status–role relationship effects. We carried out conceptual analysis and synthesis of the relevant literature, and present a model that involves the three essential precursors that play crucial roles in role mining and engineering in RBAC design. Using an illustrative case study of two companies where 63 IT professionals participated in the study, the study established that the precursors positively and significantly increase the intractability of the RBAC system design. Our framework draws attention to both the management of organizations and RBAC system developers about the need to consider and analyze the precursors thoroughly before initiating the processes of policy engineering, role mining, and role engineering. The propositions stated in this study are important considerations for future work.

Access control has become problematic in several organizations because of the difficulty in establishing security and preventing malicious users from mimicking roles. Moreover, there is no flexibility among users in the participation in their roles, and even controlling them. Several role-based access control (RBAC) mechanisms have been proposed to alleviate these problems, but the security has not been fully realized. In this work, however, we present an RBAC model based on blockchain technology to enhance user authentication before knowledge is accessed and utilized in a knowledge management system (KMS). Our blockchain-based system model and the smart contract ensure that transparency and knowledge resource immutability are achieved. We also present smart contract algorithms and discussions about the model. As an essential part of RBAC model applied to KMS environment, trust is ensured in the network. Evaluation results show that our system is efficient.