New cinema history and the computational turn

This paper outlines how the digitisation of both the film industry and contemporary research practices bear on the work of the new cinema historian. How might the opportunities presented by an unprecedented proliferation of data for example, also challenge the unspoken assumptions and ordinary practices of conventional film studies research? And how might the 'computational turn' present opportunities (and challenges) for a revisionist cinema history at the intersection of qualitative historiographies (focussed on the social experience of the cinema) and quantitative research approaches such as data mining, empirical analysis and digital visualisations?

An introduction to computational intelligence paradigms

This chapter presents an introduction to computational intelligence (CI) paradigms. A number of CI definitions are first presented to provide a general concept of this new, innovative computing field. The main constituents of CI, which include artificial neural networks, fuzzy systems, and evolutionary algorithms, are explained. In addition, different hybrid CI models arisen from synergy of neural, fuzzy, and evolutionary computational paradigms are discussed.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

Computational simulation of the influence of inert particles on incomplete combustion of methane at a low air factor

It is well known that the gas–solid system plays a significant role in many industrial processes. It is a complex physical and chemical process, generally consisting of heat transfer, mass transfer, species diffusion, and chemical reactions. In this paper, the reaction of methane with air at a low air factor and the gas flow in a fluidized bed with 0.1 mm solid particles are computationally simulated to enable the study of the effect of the inert particles on the species diffusion and the chemical reactions. The reaction of methane and air is modeled by a two-step reaction mechanism that produces a continuous fluid phase composed of six gases (CH₄, CO, O₂, CO₂, H₂O, and N₂) and discrete solid particles in the reactor. The simulation results are compared with experiment and show that the finite rate model and the eddy dissipation model can well describe the reactions of gases in high-density gas–solid systems. The distribution of each gas and the particle behaviors are analyzed for incomplete combustion at different concentrations of loaded solid particles. The inert particles change the reactions by enhancing both the chemical kinetics and the species diffusion dynamics.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.