Fuzzy Knowledge Management Research Articles

Hybrid Fuzzy Knowledge Based Prediction Model for the Software Development and Maintenance Quality in Software Engineering Approach

The main arena of Software Engineering development with ood design, development, coding, testing, implementation, deployment of the software, finally maintaining the software with good functionality. For the development of software many organizations are investing more and more budget in their revenue. Software Engineering development has several categories of data presented in software engineering such as Graphical User Interface, Usage graphs, writing text, realities and images. Significant information be able to be obtained from this composite data by well recognized data mining techniques such as association, classification, clustering etc. By discovery hidden patterns by data mining software engineering data is made illegal. Software Engineering development has many objectives in software engineering such as Code and Design optimization, Project documentation, Development cost estimation etc. Variety of significant data mining method in each phase of software development life cycle supports in realizing these objectives proficiently and the failure rate of software is decreased. . This paper focused a new hybrid model like combination of Fuzzy Logic and knowledge management offers a significant method for developing models for software quality prediction. This research paper explains about exercise of estimate and valuation at a particular organization by developments and represents the outcomes attained with a fuzzy based classification and knowledge model for the fuzzy knowledge management predication for the quality of software engineering Approach. This result illustrate that the significance of Average Error Evaluation Efficiency observed and used in fuzzy logic is lesser than Average Error Evaluation Efficiency used in another regression multiple regression; while the value of prediction is higher value that other prediction models is used before. Thus Results demonstrate that Hybrid fuzzy knowledge management predication for the quality of software engineering can be used as alternative for predicting the Software Development and Maintenance Quality (SDMQ).

An Adaptive and Fuzzy Resource Management Approach in Cloud Computing

Resource management plays a key role in the cloud-computing environment in which applications face with dynamically changing workloads. However, such dynamic and unpredictable workloads can lead to performance degradation of applications, especially when demands for resources are increased. To meet Quality of Service (QoS) requirements based on Service Level Agreements (SLA), resource management strategies must be taken into account. The question addressed in this research includes how to reduce the number of SLA violations based on the optimization of resources allocated to users applying an autonomous control cycle and a fuzzy knowledge management system. In this paper, an adaptive and fuzzy resource management framework (AFRM) is proposed in which the last resource values of each virtual machine are gathered through the environment sensors and are sent to a fuzzy controller. Then, AFRM analyzes the received information to make decision on how to reallocate the resources in each iteration of a self-adaptive control cycle. All the membership functions and rules are dynamically updated based on workload changes to satisfy QoS requirements. Two sets of experiments were conducted on the storage resource to examine AFRM in comparison to rule-based and static-fuzzy approaches in terms of RAE, utility, number of SLA violations, and cost applying HIGH, MEDIUM, MEDIUM-HIGH, and LOW workloads. The results reveal that AFRM outweighs the rule-based and static-fuzzy approaches from several aspects.

Knowledge elicitation and mapping in the design of a decision support system for the evaluation of suppliers’ competencies

Purpose– This paper aims to present a methodology for the mapping and evaluation of suppliers’ competencies and know-how. The authors operationalize the concept of organizational competence and provide companies with a customized management tool to map suppliers’ critical competencies for screening strategic from non-strategic suppliers and providing inputs for suppliers’ development.Design/methodology/approach– Competencies assessment, carried out through a fuzzy knowledge management system (VINCI), is performed through the aggregation of indicators related to the control of critical resources, the degree of implementation of critical processes, the competitive positioning and the financial situation of a supplier. Competencies description and operationalization are based on the bottom-up elicitation of the subjective knowledge managers actually use to assess suppliers’ capability. Such subjective knowledge is then validated and formalized through a top-down approach based on strategic literature.Findings– The authors tested VINCI on a sample of 38 suppliers of a large company. The results show that the methodology provides its users with a highly customizable knowledge map and its associated decision support tool that keeps into account the peculiar strategic needs of the company in the management of an existing portfolio of suppliers.Practical implications– VINCI outcomes can be used to perform benchmarking analyses, define entry criteria and thresholds for suppliers’, identify improvement targets and service levels to be considered in the definition of supply contracts, supporting the alignment of supplier’s management with business strategy.Originality/value– The most important original contribution of this work resides in the operationalization and measurements of firms’ competencies based on the elicitation of subjective knowledge that managers use in the actual assessment. A further distinctive feature of this paper is that the method is applied to small and medium companies, whereas large part of the literature on core or organizational competencies assessment is focused on large companies.

Structuring a Blurry Skill-Ability System for Personal Made E-Learning by Using Fuzzy Methods

Personal making of education is one of the most consequential methods of enhancing the efficiency and productivity of knowledge imparting and education. Concurrently, it is also a complicated procedure that needs careful attention of several factors such as students’ profiles, learning substance and learning strategies. The Blurry or Fuzzy Knowledge Management System for personalized learning discussed in this paper considers all the aforesaid reasons and factors as a whole, though not separately, as compared with many studies on the research troubles. The major task of the system is to suggest the most suitable learning stuff to the students in view their knowledge level and other learning belongings. The knowledge foundation of the system includes course and ideas of ontologies, student’s profiles and learning articles knowledge. Fuzzy logic procedures are applied for describing and working of the knowledge. Early experimental results indicate the feasibility of the system.

Building a Fuzzy Knowledge Management System for Personalized E-learning

Abstract Personalization of learning is one of the most important ways of improving the effectiveness of education. At the same time, it is also a complex process that requires consideration of numerous factors such as learners’ profiles, learning materials and learning strategies. The Fuzzy Knowledge Management System (FKMS) for personalized learning presented in this paper considers all the above-mentioned factors as a whole, but not separately, in contrast to many studies on the research problem. The main objective of the system is to offer the most appropriate learning materials to the learners by considering their knowledge level and other learning characteristics. The knowledge base of the system contains course and concepts ontologies, learners’ profiles and learning objects knowledge. For expressing and processing of the knowledge, fuzzy logic methods are applied. Preliminary experimental results reveal the operability of the system.

Fuzzy Knowledge Management through Knowledge Engineering and Fuzzy Logic

Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are used to organize and express various types of fuzzy knowledge through fuzzy CGs. Fuzzy inference rules in fuzzy CGs are identified to offer the deduction capability for reasoning about fuzzy knowledge. Second, fuzzy knowledge models can be classified and stored in a hierarchical ontology system. Ontologies serve as the common understanding of fuzzy knowledge and facilitate the finding of specific fuzzy knowledge relevant to a given domain.

M indful: A framework for Meta-INDuctive neuro-FUzzy Learning

Common inductive learning strategies offer tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome the limitations of such base-learning approaches, a research trend explores the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This may lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. In this paper, we present a meta-learning framework called M indful (Meta INDuctive neuro-FUzzy Learning) which is founded on the integration of connectionist paradigms and fuzzy knowledge management. Due to its peculiar organisation, M indful can be exploited on different levels of application, being able to accumulate learning experience in cross-task contexts. This specific knowledge is gathered during the meta-learning activity and it is exploited to suggest parametrisation for future base-learning tasks. The evaluation of the M indful system is detailed through an ensemble of experimental sessions involving both synthetic domains and real-world data.

Knowledge management evaluation framework for effective supply value chain

Knowledge Management on the supply chain is a major and competitive issue in many industries, since the importance of value creation through collaboration with the suppliers and the customers has become the business focus. Embracing supply value chain based on the effective knowledge management requires determination of knowledge management issues that can be measured, through which supply value chain be effectively achieved and enhanced. However, the ambiguity of knowledge management assessment where most measures are described subjectively using linguistic terms is a problem; fuzzy logic provides a useful tool. Thus, a fuzzy knowledge management evaluation framework for effective supply value chain management is developed in this study. To illustrate the efficacy of the approach, this study also evaluates the performance of Knowledge Management Dynamics (KMD) in two Turkish textile companies in a supply value chain perspective. Case evaluations demonstrate that the proposed framework can provide analysts with more informative and reliable information for decisions.