How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

Knowledge Management

Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.

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A survey on Data Mining approaches for Healthcare

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios

The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other. Therefore, our proposed approach is based on the evolvement technique through computerized adaptive testing (CAT). Then the genetic algorithm (GA) and case-based reasoning (CBR) are employed to construct an optimal learning path for each learner. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing; (2) it illustrates the case-based reasoning to develop a summative examination or assessment analysis; and (3) it uses empirical research to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.

https://isiarticles.com/bundles/Article/pre/pdf/17181.pdf

Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach

The threats to people's health from chronic diseases are always exist and increasing gradually. How to decrease these threats is an important issue in medical treatment. Thus, this paper suggests a model of a chronic diseases prognosis and diagnosis system integrating data mining (DM) and case-based reasoning (CBR). The main processes of the system include: (1) adopting data mining techniques to discover the implicit meaningful rules from health examination data, (2) using the extracted rules for the specific chronic diseases prognosis, (3) employing CBR to support the chronic diseases diagnosis and treatments, and (4) expanding these processes to work within a system for the convenience of chronic diseases knowledge creating, organizing, refining, and sharing. The experiment data are collected from a professional health examination center, MJ health screening center, and implemented through the system for analysis. The findings are considered as helpful references for doctors and patients in chronic diseases treatments.

Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

This paper proposes an approach of measuring a technology university's knowledge management (KM) performance from competitive perspective. The approach integrates analytical network process (ANP), which is a theory of multiple criteria decision-making and is good at dealing with tangible and intangible information, with balanced scorecard (BSC) that contains four perspectives, including customer perspective, internal business perspective, innovation and learning perspective, and financial perspective, being adopted as the indicators of KM performance measurement (KMPM). This paper makes three important contributions: (1) it propose a methodology of comparing an organization's knowledge management performance with its major rivals to offer effective information for improving KM, increasing decision-making quality, and obtaining clear effort direction of attaining competitive advantage; (2) it explores the case involving a lot of findings that present the positions of the case organization against it major rivals and imply that the technology university has to reinforce knowledge creation and accumulation to catch up with its competitive rivals; and (3) it is generic in nature and applicable to benefit an organization. The results prove the proposed method can act as a measurement tool for the entire KM of an organization.

Measuring knowledge management performance using a competitive perspective

This study proposes a knowledge discovery model that integrates the modification of the fuzzy transaction data-mining algorithm (MFTDA) and the Adaptive-Network-Based Fuzzy Inference Systems (ANFIS) for discovering implicit knowledge in the fuzzy database more efficiently and presenting it more concisely. A prototype was built for testing the feasibility of the model. The testing data are from a company's human resource management department. The results indicated that the generated rules (knowledge) are useful in supporting the company to predict its employees' future performance and then assign proper persons for appropriate positions and projects. Furthermore, the convergence of ANFIS in the model was proven to be more efficient than a generic fuzzy artificial neural network.

Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge

Chinese Medicine (CM) has been recognized as the popular complementary medicine in the world and the abundance of healing knowledge has been transmitted and enriched from generation to generation. The well-recorded archive of Chinese medical knowledge has grown and developed into very detailed theory and philosophy together with a comprehensive and subtle system of diagnosis. Recently, the Internet has been quite often used in a variety of applications for medical purpose, i.e. to provide real-time and platform-independent access which can build and share knowledge about clinical diagnosis experiences, knacks, diseases treatments, and even prescriptions. In this respect, there is a real need of an intelligent system for Chinese medical physician or education because Chinese medicine has evolved complex methods of diagnosis and treatment tailored to the individual’s subtle patterns of disharmony. This allows it not only to treat fully manifest diseases, but also to assist in maintaining health and balance to prevent illnesses from occurring. Ontologies become an important mechanism to build knowledge-based systems. The role of ontologies is to capture domain knowledge and provide a commonly agreed upon understanding of a domain. The advantages include the sharing and re-use of knowledge, and the better engineering of knowledge-based system with respect to acquisition, verification, and maintenance. In this paper, we propose a unifying framework for intelligent disease diagnosis system named CMDS – Chinese Medical Diagnostic System where the medical ontology has been integrated for the system development, and the methodologies of its implementation for digestive health. CMDS uses web interface and expert system technology to act as human expertise and diagnose a number of digestive system diseases. Besides, to efficiently elicit the knowledge of digestive system from domain experts and construct the medical ontology, a hybrid knowledge acquisition strategy is proposed. CMDS provides a truly precise analysis for digestive system disease and the prototype system can diagnose up to 50 types of diseases amongst 10 species of primary digestive system, and just uses over 500 rules and 600 images for various diseases. The satisfactory performance of the system has proven that it could serve the educational purpose, act as a consultant role and the functionality can be extended to the whole body health system.

Mu-Jung Huang

Papers

Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach

Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

Measuring knowledge management performance using a competitive perspective

Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge

Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS) – Systematic development for digestive health