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Health care costs and ageing population are two factors which are of major concern to western governments in the 21st century. Existing work in affective health care is primary focused on developing avatars in the tele-health space. This paper reports on the modeling of emotions (anxiety level) of patients in pre-operative stage using communication robots to assist nurses in providing personalized care and determining the sedation level of patients. The research is based on the need for better management of IT resources for social innovation as well as need to improve operational efficiency and quality of care.

In a health care context, the use of RFID (Radio Frequency Identification) technology can be employed for not only bringing down health care costs but also to facilitate automatic streamlining patient identification processes in health centers and assist medical practitioners in quick and accurate diagnosis and treatments. In this paper, we outline a describe design and application of RFIDbased Real-time Intelligent Clinical Diagnosis and Treatment Support System (ICDTS) in health care.

This paper proposes a segmentation method of SAR (Synthetic Aperture Radar) images based on a SOM (Self-Organizing Map) neural network. SAR images are obtained by observation using microwave sensor. For teacher data generation, they are segmented into the drift ice (thick and thin), and sea regions manually, and then their features are extracted from partitioned data. However they are not necessarily effective for neural network learning because they might include incorrectly segmented data. Therefore, in particular, a multi-step SOM is used as a learning method to improve reliability of teacher data, and carry out classification. This process enable us to fix all mistook data and segment the SAR image data using just data. The validity of this method was demonstrated by means of computer simulations using the actual SAR images.

Emotions form an important component of human behaviour and decision making. In this paper we employ image processing and soft computing techniques to design and implement emotional state profiling of a sales candidate while they provide psychological inputs related to their selling behaviour to an intelligent sales recruitment and benchmarking system. The work reported has implications in not only recruitment and benchmarking of sales persons but also web personalisation and internet based system in general.

In this paper the authors model green behaviour by predicting consumers' purchase intention using Kohonen's LVQ technique. It is envisaged that such a model may facilitate better understanding of green consumers' market segments. The model employs cognitive, affective, and situational attributes of consumers to predict their purchase intention. The model can, potentially, provide a more direct method for companies to gauge consumers' intention to purchase green products. The results indicate that consumers are more strongly resistant to lower quality than higher prices of green products in comparison to other alternative non-green products.

Soft computing agents today are being applied in a range of areas including image processing, engineering, process control, data mining, internet and others. In the process of applying soft computing agents to complex real world problems three phenomena have emerged. Firstly, the application of soft computing agents in distributed environments has resulted in merger of techniques from soft computing area with those in distributed artificial intelligence. Secondly, in order to facilitate better understanding and frequent use of the soft computing agent technology, humanization of these agents has become an important design issue (Takagi, 2001). Thirdly, given the approximate and imprecise nature of the solutions provided by soft computing agents, optimization has become an important design issue in an effort to improve the quality of solution provided by soft computing agents. Finally, from a user’s perspective, as the soft computing agent technologies have moved out of laboratories into the internet and the real world the need to model and express these technologies in the problem solving context of user. In this chapter we model these four phenomenons as part of multi-layered multi-agent architecture. The layered architec­ture is also motivated by the human-centered approach and criteria outlined in the 1997 NSF workshop on human-centered systems.

Identifying profiles of customers based on their history trails of transactions and further providing personalization services to them are the best strategy to retain customers for online businesses. Currently profiling techniques have the defects of static offline operation, inexactness, and slow response, which do not meet the ever-changing demand of online transactions for e-commerce, e-banking, etc. This paper proposes a dynamic intelligent profiling architecture with the help of high performance computing. The architecture is tested and applied in an e-banking application.

The provision of web cameras has opened possibilities of user behaviour profiling and web personalisation based on their emotional state. In this paper we report on a neural network model for emotional state profiling and its implications for behaviour profiling and personalisation.

Existing approaches for salesperson recruitment primarily rely on interviews. Some organizations also include personality testing based on psychometric techniques. The high turnover of salesperson in the industry suggests limited success of these procedures. Additionally, existing approaches lack benchmarking methods. In this paper we describe a Salesperson Recruitment and Benchmarking System (SRBS) for recruitment and benchmarking of salespersons. SRBS integrates psychology based selling behaviour model with artificial intelligence techniques and soft computing Kohonen’s LVQ Prediction technique for selling behaviour profiling and benchmarking.

A problem for the illegal copy of the digital image, which overflows on WWW, and the copyright protection of the image information becomes serious. Electronic watermark technology is being watched as that strong countermeasure. However, there is a problem that the problem that quality of output deteriorates, and the codes put inside are analyzed. In this paper, a neuro-color compression ID system with no quality of output deterioration, which sets up security for the image, is proposed. The position information of pixel chosen at random from the color image and RGB value are compressed with a proposal system by using sandglass type neural network (SNN). The information that it is compressed and the combination weight of SNN are called neuro-color compression ID. Moreover, neuro-color compression ID can be restored by using the position information of the number of pixel of SNN chosen at random. It is possible that the possible system of the prevention of an illegal copy and the copyright protection is built by making either of the position information of neuro-color compression ID, the number of pixel that it is chosen, or pixels base an image license. Furthermore, because it doesn’t need to put a code inside like an electronic watermark, even the field that a demand for the information secret is severe can be used as a new watermark technology with no quality of output deterioration. Furthermore, in order to show the effectiveness of the proposed method, simulations are done. This proposed method was recognized as the patent.

Personal identification provides are important means for realizing a man-machine interface and security products. Recently in the world, many researches for individual identification method using the face are widely done. In the personal identification methods using the face, the feature points method and the pattern matching method are general technology. However, condition of lighting affects recognition accuracy in many cases. In this paper, we propose the face identification method which is robust for lighting based on the feature points method. The proposed method extracts the edge of facial features. Then, it determine oval parameters of each feature from the extracted edge by hough transform. Moreover, in order to show the effectiveness of the proposed method, we show computer simulations by using real images. From these results, we show the effectiveness of the proposed method.

In this paper we outline a multi-layered multi-agent distributed soft computing architecture for providing support to practitioners and problem solvers at four levels, namely, distributed processing, technology, optimization and problem solving level respectively. The architecture has been applied in complex problems like image recognition, web mining, alarm processing, and medical diagnosis. We illustrate some aspects of the architecture with help of e-Sales recruitment (e-SRS) application. An earlier version of e-SRS (expert system ) is also in commercial use.

Internet Personalized services are irresistible developing trend for e-commerce. More and more researchers are committed to personalization field. Many personalization approaches are static and lack of means to improve the personalized tasks. This paper proposes an adaptive e-commerce personalization framework using traditional data mining techniques and agent technology as well as user feedback optimisation mechanism to improve the personalized services to the e-commerce customer. The behaviours of all the agents in the framework are carefully considered and the framework has been applied to an online banking system.

In this paper we outline a multi-layered distributed agent ontology for providing system modeling support to practitioners and problem solvers at four levels, namely, distributed, tool (technology), optimization and task level respectively. We describe the emergent characteristics of the architecture from a architectural viewpoint. We also outline how the ontology facilitates development of human-centered soft computing systems. The ontology has been applied in a range areas including alarm processing, web mining, image processing, sales recruitment and medical diagnosis. We also outline definition of agents in different layers of the ontology.

In this paper we describe a distributed retrieval system for medical images based on a Human Centered Virtaal Machine (HCVM). HCVM layered architecture allows indexing and retrieval based on the user's vision of data, seamlessly linking a nominal scale to ordinal and interval based representations suitable for intelligent search agents. Moreover, it ensures applicability of the approach to a wide range of appUcation domains.

In this paper we address two important sales management issues, namely, recruitment and training of salespersons. We describe an Intelligent Assistant for Improving Sales Performance(IAISP) which can assist the management in recruitment of salespersons. IAISP can also assist a freshly recruited salesperson in improving their inter-personal skills in their day to day interactions with customers. IAISP aware of any such existing system which is based on such a synthesis. We particularly concentrate in this paper on the knowledge acquisition and analysis which brings about the synthesis between deep knowledge in the form of a a behavioral model and shallow knowledge in the form of knowledge and experience of the sales managers. IAISP has been field tested and the results are encouraging.