An Intelligent Approach to Assess Tacit Knowledge Fitness in Networked Enterprises

Many organizations attempt to form strategic networked enterprises, yet such strategies are difficult to implement because they are as likely to fail as to succeed. This failure is due to intangible differences and mismatches between partners in tacit knowledge (TK). Despite the various proposed partnership assessment models/tools in the literature, an immediate need exists for a new approach to measure the mismatch in TK across different organizations. This is due to the complex, vague, and uncertain nature of TK attributes. Hence, an instrument for measuring vagueness (imprecise), such as fuzzy linguistic variables, is needed. In this study, the author applies a neuro-fuzzy approach to assess TK fitness in networked enterprises. The results show how differences in TK between partners affect the networked enterprise’s performance. Furthermore, the assessment approach reveals the most significant values to adopt and the irrelevant values that must be abandoned to smooth the partnership formation. The proposed model can prevent unexpected conflicts between partners if managed properly.

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A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

Advances in Fuzzy Systems ◽

10.1155/2012/823052 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Mohammad Hossein Fazel Zarandi ◽

Neda Mohammadhasan ◽

Susan Bastani

Keyword(s):

Performance Evaluation ◽

Expert System ◽

Intellectual Capital ◽

Fuzzy Rule ◽

Qualitative Evaluation ◽

Linguistic Variables ◽

Rule Based ◽

Proposed Model ◽

Linguistic Approach ◽

Fuzzy Linguistic

A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

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Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach

Current World Environment ◽

10.12944/cwe.12.2.27 ◽

2017 ◽

Vol 12 (2) ◽

pp. 429-435

Author(s):

M. Sudha

Keyword(s):

Intelligent Systems ◽

Prediction Accuracy ◽

Intelligent System ◽

Data Driven ◽

Fuzzy Approach ◽

Neuro Fuzzy ◽

Proposed Model ◽

Hybrid Data ◽

Weather Modeling ◽

Using Data

Recently, hybrid data-driven models have become appropriate predictive patterns in various hydrological forecast scenarios. Especially, meteorology has witnessed that there is a need for a much better approach to handle weather-related parameters intelligently. To handle this challenging issue, this research intends to apply the fuzzy and ANN theories for developing hybridized adaptive rough-neuro-fuzzy intelligent system. . Assimilating the features of ANN and FIS has attracted the rising attention of researchers due to the growing requisite of adaptive intelligent systems to solve the real world requirements. The proposed model is capable of handling soft rule boundaries and linguistic variables to improve the prediction accuracy. The adaptive rough-neuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques.

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User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach

Educational Research Review ◽

10.1016/j.edurev.2009.02.001 ◽

2009 ◽

Vol 4 (2) ◽

pp. 142-155 ◽

Cited By ~ 27

Author(s):

Hamed Fazlollahtabar ◽

Iraj Mahdavi

Keyword(s):

Fuzzy Approach ◽

Optimal Learning ◽

Learning Path ◽

Neuro Fuzzy ◽

E Learning

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Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Applied Sciences ◽

10.3390/app11146590 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6590

Author(s):

Krittakom Srijiranon ◽

Narissara Eiamkanitchat

Keyword(s):

Particulate Matter ◽

Fuzzy Model ◽

Original Data ◽

Northern Region ◽

National Standard ◽

Minimum Entropy ◽

Fuzzy Transformation ◽

Neuro Fuzzy ◽

Proposed Model ◽

Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

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Intelligent recognition of semantic relationships based on antonymy

Multiagent and Grid Systems ◽

10.3233/mgs-200332 ◽

2020 ◽

Vol 16 (3) ◽

pp. 263-290

Author(s):

Hui Guan ◽

Chengzhen Jia ◽

Hongji Yang

Keyword(s):

Semantic Similarity ◽

New Approach ◽

Word Similarity ◽

Semantic Relationships ◽

Proposed Model ◽

Path Distance ◽

The Hierarchical Structure ◽

Thinking Process ◽

Similarity Measuring ◽

Intelligent Recognition

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.

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