21,834 research outputs found

    Big data analytics:Computational intelligence techniques and application areas

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    Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

    Banking Geography and Cross-Fertilization in the Productivity Growth of US Commercial Banks

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    The US banking industry offers a unique, natural and fertile environment to study geography's effects on banks' behavior and performance. The literature on banks' operating performance, while extensive, says little about the influence of spatial interactions on banks' performance. We compute and examine, using a physical distance-based spatio-temporal empirical model, the state-wide total factor productivity growth (TFPG) indices of US commercial banks for each state for the 1971-1995 period. We observe that the productivity growth of commercial banks in state i depends strongly, positively, and contemporaneously on the productivity growth of commercial banks located in state i's contiguous states. Further, “regulatory space” appears to induce frictions and lessen the documented spatial interactions. These findings support our plea that research on commercial banking sector's behavior need to pay a particular attention to the effects of banking geography.Spatial, Commercial Banks, Total Factor Productivity Growth, Kalman Filter

    Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

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    Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types
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