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Purpose The purpose of this paper is to propose a new approach that enables service innovation models to incorporate a holographic perspective into their innovation-centric business models. The essence of the holographic approach to service innovation might provide us with an innovative organization that is enclosed in its components; a knowledge-centric approach that adapts each person as a vital component of a whole; and the ability of value co-creation by each part of the organization in ways that benefit the organization as a whole. Design/methodology/approach This paper uses a narrative synthesis framework combining existing literature (by textual narrative technique) with expert opinion, based on search of Science-Direct and ProQuest academic databases. Findings A total of 114 top-cited and high relevant references were deeply reviewed. Nine principle dimensions were evolved from the final review to construct a comprehensive definition of service innovation. Then, the narrative synthesis helped us to bring forward a new approach to service innovation and applied it in the form of a conceptual model, as the literature was previously established on certain approaches. In the final stage, a comprehensive model of service innovation was designed to introduce the holographic approach to the existing literature. Research limitations/implications This paper reviewed top-cited and high relevant references published in English that were indexed in Science-Direct and ProQuest. The authors did not search any grey literature and other language publications, and hand-search any journals. Practical implications This research highlights how managers must consider service innovation as a whole. Originality/value This is the first critical review published in the peer-reviewed literature that explores the principle dimensions of service innovation and provide a new approach to the literature.

As the organizational memory in terms of collective knowledge evolves, how to construct an effective knowledge sharing mechanism to covert individual knowledge into collective knowledge becomes fairly demanding. CoPs approach is widely accepted as effective mechanism to facilitate knowledge sharing. Knowledge nodes in the context of knowledge flow, unlike workflow, can often transcend organizational boundaries and are distinct and different than workflow models. This paper aims to develop, implement, and analyze a CoPs Centered knowledge flow model in a multinational organization. This model is underpinned in a CoPs framework built around four expected performance including four dimensions and sixteen criteria as a comprehensive mechanism to intensify knowledge sharing effect. Next, this study clusters knowledge workers/nodes with common criteria (attitudes and beliefs) towards this model. These common attitudes and beliefs between two knowledge workers/nodes imply that knowledge sharing among them is likely to be more effective than between knowledge workers/nodes with dissimilar attitudes and beliefs. Fuzzy Multi-Criteria Decision Making MCDM) and cluster analysis techniques are adopted as research methods. A Dynamic knowledge flow activity analysis model is also defined as part of future work.

Currently, TV programs are evaluated by using questionnaires given after previews or by using TV ratings. There are few objective criteria useful for describing technical know-how about program production. One of the TV program producers' concerns is how to choose expression methods that convey their ideas to viewers correctly and efficiently. Research has shown that eye-gaze direction is related to the human focus and attention. Gaze-based evaluations have been proposed for image-quality evaluations and certain usability tests. Such approaches are mainly based on how often a specific region attracted the subjects' gaze or how long their gaze was fixed on it. To apply these approaches to TV programs, all the object regions that seem to attract a viewer's gaze need to be specified in advance. This causes several problems including the accuracy of specifying the region by using an image processing technique is not equal to the human subject's recognition ability and it is not feasible to manually specify such regions in an enormous number of frames (images) comprising the program. Further, how characteristics of well-produced TV programs appear on the viewer's gaze has not been objectively analyzed yet. There is a need to investigate the relationship between gaze and program contents which can be used as means for improving comprehension and quality of the TV programs. In this paper, we propose a new measurement and evaluating method for this purpose. This paper focuses on the relationship between a viewer's comprehension of a program and their gaze direction in a real experimental TV educational program involving 26 elementary school children and broadcast by NHK Broadcasting Corporation of Japan. Correlation between TV program comprehension and entropy is investigated. That is, variances in the gaze direction in relation to program comprehension are based on a entropy value that represents the degree of dispersion in each frame and is calculated from a probability density function estimated from the gaze directions. The results indicate that the variances of the gaze direction for scenes that gave better comprehension tended to be lower. This tendency was further noticeable after a keyword utterance were related to the answers of corresponding questions.

The well-executed recruitment and retention of employees in organisations in a highly competitive global market has grown significantly in the last decade. The need for managers to be emotionally intelligent for better management and productivity to deal with employees from generation Y and Z is also in great demand. In this paper we presents a framework which embodies human computer interaction techniques like facial emotion recognition, speech recognition and synthesis in socially assistive robot with human-like communication modalities to capture, analyse, profile and benchmark verbal and non-verbal data during a real-time job interview for hiring salespersons. This research fundamentally changes how employers can leverage the data analysis to seek for the best job applicant and how they perceive the use of human computer interaction (HCI) techniques and information technology in human resource management practice. Existing approaches for recruitment primarily rely on selection criteria and/or psychometric techniques followed by face to face interviews by subjective judgements of human beings. For example, the high turnover of salespersons in the industry has shown limited success of these procedures. Additionally, existing approaches lack benchmarking analysis internally by comparing the profile of most cultural fit employees. Thus, this research incorporates behavioural psychology, data mining, image processing, HCI modelling and techniques to provide a more holistic recruitment application using emotionally aware social robot. The implications of this research not only apply into the hiring and benchmarking of employees, but also collecting big data (verbal and non-verbal) for decision-making, personalised profiling and training.

In this paper we outline a human-centered multilayered multiagent distributed soft computing architecture for providing support to practitioners and problem solvers at four levels, namely, clerical, tool, optimization and task level, respectively. We describe the emergent characteristics of the architecture from a architectural, management and domain perspective. We illustrate the application of these four levels with the help of two image processing applications namely, face detection and annotation, and unstained mammalian cell classification.

The 90’s has seen the emergence of hybrid configurations of four most commonly used intelligent methodologies, namely, symbolic knowledge based systems (e.g. expert systems), artificial neural networks, fuzzy systems and genetic algorithms. These hybrid configurations are used for different problem solving tasks/situations. In this paper we describe unified problem modeling language at two different levels, the task structure level for knowledge engineering of complex data intensive domains, and the computational level of the task level hybrid architecture. Among other aspects, the unified problem modeling language considers various intelligent methodologies and their hybrid configurations as technological primitives used to accomplish various tasks defined at the task structure level. The unified problem modeling language is defined in the form of five problem solving adapters. The problem solving adapters outline the goals, tasks, percepts/inputs, and hard and soft computing methods for modeling complex problems. The task structure level has been applied in modeling several applications in e-commerce, image processing, diagnosis, and other complex, time critical, and data intensive domains. We also define a layered intelligent multi-agent, operating system processes, intelligent technologies with the task structure level associative hybrid architecture. The layered architecture also facilitates component based software modeling process.

SUMMARY Precision agriculture is a new and developing discipline that incorporates advanced technologies to enhance the efficiency of farm inputs in a profitable and environmentally sensible manner. Yield monitoring and variable rate application are the most widely used precision technologies. Versatile guidance systems utilizing the global positioning system (GPS) and management zone approaches are also being developed to further increase productivity by reducing error, cost, and time. These technologies provide tools to quantify and manage variability existing in fields across an array of cropping systems. A review of precision farming technologies that are currently being used in the United States and around the world is presented in this article.

Like most of the developed countries, Australia's population is ageing. In this paper the authors report on deployment of a socially assistive robot (Matilda) based multi-layer service architecture to support People with Dementia (PwD) in home-based care. The robots deliver a range of services in an emotionally engaging manner based on the lifestyle of PwD. The assistive social robots have been deployed in several Australian households over a six-month period. The results of trial demonstrate that multi-layer service architecture with personalized services and human-like communication modalities of Matilda have the ability of breaking technology barriers, providing sensory enrichment and social connectivity to the PwD as well as augmenting their good memories and providing respite to the partners.

Electronic brokerage, envisioning brokers acting on customers' behalf to locate required products and services at the best available price, is a promising technique to solve a specific problem of electronic commerce business relations, i.e. the identification of the goods and services satisfying the user requirements to the highest possible degree. In this paper we describe a electronic brokerage system based on a human-centered layered architecture that allows the broker to proceed seamlessly from the user's vision of the market, based on a nominal scale, to ordinal and interval based representations suitable for intelligent search agents exploiting fuzzy techniques.

Emotional well being is one of two aspects of personal well-being that can be measured in quality of life assessments, and is a critical part of a senior citizen to help them age gracefully and remain independent as well as integrated with society. In order to improve the emotional well being of the elderly in the era of looming ageing population crisis, in this paper the authors report on design and a field trial of Matilda, a human-like assistive communication assistive robot in Australian residential care and home-based care facilities. The work demonstrates that by marrying the embodiment of care with artificial intelligence, emotion measuring techniques and positive emotion generation in its design and applications, Matilda has the ability to improve emotional well being of the elderly.

Communities of Practice (CoPs) provide a good medium for effective utilization of knowledge workers and group innovation in an organization. It is also an organizational instrument for developing competitive advantage based on chosen business strategy. For effective operation of CoPs and utilization of organizational resources business strategy and knowledge sharing attributes of knowledge workers play a key role in effective design of CoPs. Furthermore, to enable effective implementation of CoPs, an IT enabled knowledge hub is considered useful. This research combines the above mentioned components to develop a CoPs enabled knowledge hub. This research can help organizations to design and implement CoPs based on business strategy and knowledge sharing attribute fit.

This paper describes the design of multimodal robotic system, embodiment of multimodal interaction (voice, gestures, emotion, touch panel and dance) in assistive social robot (Matilda) for modeling group based and one-to-one interactions with technology adverse elderly in nursing homes. It describes the human-centered evaluation of Matilda based on quality, naturalness, user satisfaction and predictive accuracy based on first ever field trials in Australia. The multimodal social robots have facilitated breaking the technology barriers with the elderly leading to several aged care facilities and community centers showing interest in future trials.

The purpose of this paper is to obtain the object. In particular, we focus on the face detection. By using the proposed method, we recognize faces with a near-infrared (NIR) camera. The face detection that used images from the NIR camera is comparatively difficult to be done, because they are gray scale images. In this paper, the filter by using the GA is designed for the purpose of getting the faces. The method of detecting faces and the position from the NIR images is proposed in the on-line image. It is demonstrated that our approach is effective for vehicle driver monitoring. Finally, it was confirmed that the proposed method works well.

This paper describes a method and system for monitoring and correlating digitized non-verbal data (like emotions and eye-gaze) with other kinds of data for decision support and information personalization. This is illustrated through two applications in this paper. The first one describes detection, monitoring and correlation of emotional state changes for behavior profiling of sales candidates. The second one outlines design of eye gaze tracking system using standard web camera (instead of more expensive infra-red camera) for holiday destination selection. The paper makes unique contributions in terms of modelling as well as correlation of non-verbal data in natural environments for improving digital information quality and decision making in business areas like recruitment and tourism.

This research discusses KFNs in the context of Communities of Practice (CoPs) and Knowledge Management (KM). KFNs unlike workflow can often transcend organizational boundaries and are distinct and different than workflow models. CoPs involve both personal and organizational aspects, and are an iteration of the transmission between explicit and tacit knowledge. This research develops, implements, and analyzes a CoPs Centered KFNs model in a multinational organization. The CoPs Centered KFNs model is underpinned in a CoPs model built around four organization performance evaluation dimensions and sixteen criteria. Many criteria and comprehensive segments should be taken into consideration while establishing CoPs model, this explains why this research employs fuzzy multi-criteria decision making. The cluster analysis techniques are used for evaluation of the CoPs Centered KFN model. The result of attribute analysis via KFNs model has been designed to determine the characteristic of each cluster and identify suggestions for effective linkage among knowledge workers.

Research in cognitive psychology and learning literature clearly suggests a link between emotions and constructive learning. In this paper the authors report on role of non-verbal emotional responses for designing e-Training programs. It involves measuring arousal or lack of it as a means of pro-active diagnostic assessment of a sales management trainee and using the assessment to customize e-Training course in inter-personal communication

This paper describes the role and correlation of non-verbal data (emotions) with other data in managerial decision making in natural environment. The work is targeted towards context-aware emotionally intelligent Human Resource Management (HRM), information personalization and managerial decision making in consumer and service sector industries like finance/banking, electronics, retail, tourism and health care.

Service innovation has been a serious debate in the literature on service management, especially when it is compared with new service development (NSD). While service innovation is defined based upon certain approaches, there is no specific definition that differentiates service innovation from NSD, especially in a certain technological context such as social assistive robots. Therefore, bringing a new approach that encompasses all features of service innovation is a topic of interest. This paper aims to introduce the holographic approach to service innovation in the condition of uncertainty by a critical narrative synthesis methodology on high quality and top-cited publications in the last three decades. The qualitative findings show that service innovation can be established in a social assistive robot as a certain technological context, based on the holographic approach in which each single component of the innovation process enfolds service innovation as a smaller whole.

In a health care context, the use RFID (radio frequency identification) technology can be employed for not only bringing down health care costs but also facilitate automating and streamlining patient identification processes in hospitals and use of mobile devices like PDA, smart phones, for design a health care management systems. In this paper, we outline a RFID model for designing a system in the health care. An application of the architecture is described in the area of RFID-based real-time hospital patient management system (HPMS).

Emotions form an important component of human behaviour and decision making and thus are an essential part of context-aware web-based decision support systems. In this paper a conceptual model based on sensemaking, affective and situational feedback is outlined for design of context-aware web-based decision support systems. An application of the model is illustrated in the area of web-based behaviour profiling and recruitment of salespeople. In this application cognitive responses of a sales candidate to behavioural questions from a e-Sales Recruitment and Benchmarking System (e-SRBS) are mediated with their real-time emotional or affective responses to facilitate context-aware decision support in e-recruitment applications. . The work reported has implications in not only e-recruitment but also in knowledge management, data mining, web personalisation and context-aware web-based decision support systems in general.

In this paper we outline a seven layer context-aware data mining architecture which combines context, sensemaking (cognitive and affective) and data mining technologies to design adaptive context-aware data mining systems. We particularly show how cognitive constructs and emotional attitudes of a user mediate in interpretation of meaning in hidden patterns. We illustrate the role of cognitive constructs in interpreting a CRM situation by a relationship manager in a banking and finance application. We also illustrate the role of emotional attitudes as an important factor in context-aware interpretation of mined behavioral patterns in a sales recruitment and benchmarking application.

In this paper we describe a novel intelligent web based system which employs human feedback and evaluation to assist an Internet user to interactively identify the clothing of a missing person. It is novel in terms of employing GA to interactively create different shirt designs. It also illustrates human-centeredness from two perspectives. Firstly, from a multimedia information system perspective, in order to capture the user semantics it employs user feedback. Secondly, from a computational intelligence perspective the user is involved in defining objective function of a GA agent for optimizing search. The system can be used by the law enforcement authorities to identify the type, color and design of the shirt worn by a missing person. It has been tested on a small number of users with encouraging results.

Emotions form an important component of human behavior 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 behavior to an intelligent sales recruitment and benchmarking system. This research is part of a larger effort aimed at giving computers and other pervasive devices the skills of emotional intelligence, such as the ability to recognize a person's emotions and to respond appropriately to those emotions. The work reported has implications in not only in recruitment and benchmarking but also web recommender and information personalization systems.

This paper proposes a human-centered multi-layered multi-agent architecture for performance optimization of data mining applications. Feedback from user is used for optimizing the data mining performance. This paper describes application of the architecture in banking and finance domain

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 we outline a task-centered agent-oriented approach to modelling (or software engineering) of intelligent real-time systems for critical events like environmental disasters (e.g., bush fires, earthquakes), alarm processing in critical and emergency situations in power systems, and in war situations involving detection and destruction of enemy planes by fighter pilots. We model the user (e.g., operator or fighter pilot) at two distinct levels. These include the problem solving or the task level and performance optimisation level. Alarm processing is used as an application to illustrate the modelling at the task and performance optimisation levels respectively

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.

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

E-recruitment area has emerged as an important e-business function in the past few years. We describe a novel e-recruitment multiagent application for recruitment and benchmarking of salespersons. Most existing approaches to recruitment rely on the interview process or on psychometric techniques. Both these approaches have had limited success. We describe a multiagent e-sales recruitment system (e-SRS), which integrates a selling behavioral model with expert systems and soft computing techniques like fuzzy-K-means for predicting the selling behavior profile of a sales candidate.

Nowadays, recommendation systems in e-commerce are booming because of their potential and applicability for personalized services to customers. However, recommendation is not always what customers are expecting. Odd pitches and poor matches in the system have led to outpouring of anecdotes. It means that quality control doesn't apply to the recommendation here. To cope with this problem, this paper proposes a new way to improve both the quality of rating and recommendation itself for e-recommendation system. The concepts will integrate to the implementation of our on-going e-recommendation system and the second concept is illustrated in a financial domain.

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. In this paper we describe a multi-agent distributed control system model for image processing of unstained human (mammalian) cell images. The control system model develops a symbiotic relationship between soft computing agents like neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

It is often known that an EEG has a personal characteristic. However, there are no researches to achieve the consideration of the personal characteristic. Then, the analyzed frequency components of the EEG have that the frequency components in which characteristics are contained significantly, and that not. Moreover, these combinations have the human equation. We think that these combinations are the personal characteristics frequency components of the EEG. In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.

Recently in the world, many researches of personal identification method using biometrics are widely done. Especially, personal identification using the face is used because of needless for physical contact. In this paper, we propose the new method to identify the personal using the SPCA and the NN. The SPCA has been proposed in order for the PCS to achieve high-speed processing. Moreover, the personal identification system needs to have not only ability to recognize registrant correctly but also ability to reject un-registrant certainly. Therefore, we perform simulation using un-registrant. Furthermore, we analyzed and examined about the individual feature in a face by using the GA and the SPCA. Then, in order to show the effectiveness of the proposed method, we show computer simulations by suing the real image. From these results, we show the effectiveness.

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.

This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper is to eradicate all traffic congestion and improve the end-user's video quality. It possesses the capability to predict, to a very high level of accuracy, a state of congestion based upon the training data collected about the network behaviour. Initially, the current traffic shaping technologies are discussed and later a simulation in a controlled environment is illustrated to exhibit the effects of this intelligent traffic-shaping algorithm on the underlying network real time packet traffic and the eradication of unwanted abruption in the streaming video qualiy. This paper concluded from the end results of the simulation that neural networks are a very superior means of modeling real-time traffic and that it can be applied as an appropriate solution to network congestion.

Intelligent agents (or software agents) today can be seen as providing support to practitioners and problem solvers at three levels, namely, clerical, tool and task levels. In particular, we outline computational level based agent definitions of fuzzy neural agents. We show the application of these agents in determining faulty components in a electrical power system network.

A generic neuro-expert system architecture which can overcome difficulties faced by stand-alone expert systems and artificial neural networks is proposed. It can be applied in various problem domains, such as engineering and fault diagnosis, which require problem decomposition. It is recommended for use in real-time systems. The neuro-expert system architecture can be used at different levels of a power system hierarchy for alarm interpretation and fault diagnosis. >

The applications in this book employ a number of concepts and technologies associated with e-business. The e-business concepts include types of e-business systems, e-business strategies and e-business models. The technologies can be grouped under Internet and web technologies, intelligent technologies, web software engineering (Object-oriented and agents) technologies, and multimedia. E-business strategies and e-business models also provide the context in which e-business technologies are applied by organizations to gain competitive advantage. This chapter introduces the reader to these concepts and technologies.

It is widely acknowledged that the main barrier to e-business lies in the need for applications to meaningfully share information. The negative impact on e-business of the inherent limitations of traditional approaches to knowledge sharing has been comparable to the Internet’s initial lack of reliability or security. In the past, knowledge sharing and organization efforts nearly always produced document-based Knowledge Management Systems (KMS), i.e. collections of documents internally maintained by organizations and focused on particular domains.

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.

This chapter builds on the foundations laid down in the previous chapter. It describes the human-centered e-business system development framework for multi-agent e-business systems based on human-centered criteria outlined in the first chapter and the pragmatic considerations and enabling theories discussed in chapter 3, which contribute towards realization of those criteria. The human-centered framework is described in terms of four components, namely, activity-centered e-business analysis, problem solving ontology, transformation agent, and multimedia interpretation, respectively. The three human-centered criteria are used as guidelines for development of the human-centered framework. The pragmatic considerations and contributing theories are used to develop the structure and content, or knowledge base, of the four components. The structure and content are described at the conceptual and computational (transformation agents) level. We start this chapter by describing the external and internal planes of human interaction which underpin the development of the human-centered framework. We follow it with the description of two components of the human-centered e-business system development framework, namely, activity-centered e-business analysis and problem solving ontology. In the next chapter we continue with the description of the problem solving ontology component and describe two other components, namely, the transformation agent and multimedia interpretation component. These four components have been used to define the external and internal planes of human interaction with the environment.

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.

The objective of this chapter is to outline the computational framework of intelligent multimedia multi-agent systems based on the human-centered approach. The title human-centered virtual machine encapsulates the integration of conceptual components of the human-centered system development framework and the technology based artifacts used to realize the conceptual components at the computational level.