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This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

This study aims to explore the possibility that securities markets in selected African countries of Egypt, Kenya, Nigeria and South Africa play a significant role in capital accumulation using panel data analysis. This is done by exploring the relationship between gross fixed capital formation on the one hand and financial market development indicators on the other hand. Thus, the study aims to examine if stock market size and liquidity are determinants of capital accumulation.

The analysis is based on annual times series from 1991 through 2017 spanning four African stock markets. The analysis utilizes the fixed-effect and random-effect econometric models. The Durbin–Wu–Hausman test is used to choose between the two models.

The key results indicate that stock market capitalization is a positive determinant of gross fixed capital formation. The market value traded and turnover have no relationship with capital formation. Therefore, the role of stock African stock markets in promoting capital accumulation and, subsequently, industrial growth in Africa is seriously questioned.

Only a handful of studies have examined the role of the African securities market in promoting capital accumulation. This study is unique in which it focuses on the leading stock markets in the four corners of Africa. The markets are from Egypt in the north, South Africa from the south, Nigeria from the west and Kenya from the east. These four markets account for a significant segment of all African markets.