An ensemble of intelligent water drop algorithms and its application to optimization problems

Crown Copyright © 2015 Published by Elsevier Inc. All rights reserved. The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.

Emergent behaviour: learning from an artificially intelligent performing software agent

This research investigates the possibility for emergent choreographic behaviour to arise from the interactions between a human dancer and a learning, digital performing agent. The cognitive framework is extended through theories of distributed cognition to take into account the two interacting agents rather than a single agent and its environment. The Artificial Neural Network based performing agent demonstrated emergent dance behaviour when performing live with the human dancer. The agent was able to follow the dancer, create movement phrases based on what the dancer was performing and recognize short movement phrases, as a result of the interaction of the dancer’s motion captured movement data and the agent’s artificial neural network. This emergent behaviour was not explicitly programmed, but emerged as a result of the learning process and the interactions with the human dancer.

Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. © 2006 IEEE.

Intelligent risk detection in healthcare contexts of hip and knee athroplasty and paediatric congenital heart disease

Rapid increase of service demands in healthcare contexts today requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Contemporaneous with the challenges facing healthcare, we are witnessing the development of very sophisticated intelligent tools and technologies such as Business Analytics techniques. Therefore, it would appear to be prudent to investigate the possibility of applying such tools and technologies into various healthcare contexts to facilitate better risk detection and support superior decision making. The following serves to do this in the context of Total Hip and Knee Arthroplasty and Congenital Heart Disease.

'All intelligent subjects' – Presenting scientific collecting and identity at the Hawke's Bay Philosophical Institute Museum, 1865-1899

This paper traces a shift in New Zealand’s Scientific Heritage, and the performance and presentation of scientific knowledge and identity through collecting and museum practice – based on a case study of the Hawke’s Bay Philosophical Institute Museum, during the period 1865 -1899. Two very well-known figures of New Zealand science, Museums and collecting were central in Hawke’s Bay: William Colenso FLS FRS; and Augustus Hamilton, who later became Director of the Colonial Museum in Wellington. Through them, can be traced how scientific collections and identities evolved, from the work of gentlemen of science to that of a newly professionalised vocation, realised within the spaces of Museums.

Intelligent internet computing: applications and security systems

Intelligent Internet Computing (IIC) is emerging rapidly as an exciting new paradigm including pervasive, grid, and peer-to-peer computing to provide computing and communication services any time and anywhere. IIC paradigm foresees seamless integration of communicating and computational devices and applications embedded in all parts of our environment, from our physical selves, to our homes, our offices, our streets and so on. Although IIC presents exciting enabling opportunities, the benefits will only be realized if application and security issues can be appropriately addressed. This special issue is intended to foster the dissemination of state-of-the-art research in the area of IIC, including novel applications associated with its utilization, security systems and services, security models. We plan to publish high quality manuscripts, which cover the various practical applications and related security theories of IIC. The papers should not be submitted simultaneously for publication elsewhere. Submissions of high quality papers describing mature results or on-going work are invited. Selected high-quality papers from “the Eleventh IEEE International Conference on High Performance Computing and Communications (HPCC-09) and the Third International Conference on Information Security and Assurance (ISA-09),” will be published in this special issue of Journal of Internet Technology on "Intelligent Internet Computing".