• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.1-22
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• 2011

In this paper, we propose an adaptive genetic algorithm (aGA) approach for effectively solving the sequencing problem with precedence constraints (SPPC). For effective representation of the SPPC in the aGA approach, a new representation procedure, called the topological sort-based representation procedure, is used. The proposed aGA approach has an adaptive scheme using a fuzzy logic controller and adaptively regulates the rate of the crossover operator during the genetic search process. Experimental results using various types of the SPPC show that the proposed aGA approach outperforms conventional competing approaches. Finally the proposed aGA approach can be a good alternative for locating optimal solutions or sequences for various types of the SPPC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.197-204
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• 2020

Nowadays, many construction engineering and technology enterprises are evolving to find that prosperity is driven and inspired by an open economy with dynamic markets and fierce multifaceted competition. Besides brand and product uniqueness, the ability to quickly provide customers with quotes are matters of concern. Such a requirement for prompt cost estimation of construction investment projects with the use of a construction price index poses a significant challenge to contractors. This is because the nature of the construction industry is shaped by changes in domestic and foreign economic factors, socio-financial issues, and is under the influence of various micro and macro factors. This paper presents a fuzzy decision-making approach for calculating critical factors that affect the construction price index. A qualitative approach was implemented based on in-depth interviews of experts in the construction industry in Vietnam. A synthetic comparison matrix was calculated using Buckley approach. The CoA approach was applied to defuzzified the fuzzy weights of factors that affect the construction price index. The research results show that the top five critical factors affecting the construction price index in Vietnam are (1) consumer price index, (2) gross domestic product, (3) basic interest rate, (4) foreign exchange rate, and (5) total export and import.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.29-43
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• 2017

Innovation Cosystems are Conceptualized as Organizational Networks of Economic Actors, Technologies and Social Contexts that Interact for Knowledge Production, use, and Adaptation. This Paper Proposed a Conceptual Framework to Describe Value Co-creation of Organizational Networks Engaged in Technology Innovation. We Adopted Theory-Based Approach by Integrating the Perspective of Service-Dominant (S-D) Logic Into the Evolutionary Model of the Triple Helix. The Framework Gives a Plausible Explanation on how Actors Collaborate to Create Value in Dynamic Contexts of an Innovation Ecosystem. The Innovation Ecosystem can be Considered as a Composite of Sub-Ecosystems, Including Knowledge, Sectoral, and Business Ecosystems. When these Sub-Ecosystems are Recursively Transformed by Coordination of Functional Mechanisms that Serve Value Co-creation in the Innovation Process, the Innovation Ecosystem will be Re-Organized and Evolve. The case of the Digital Living Network Alliance (DLNA) was Examined to Demonstrate the Fundamental Mechanisms for Value Co-creation that was Described in the Framework. The case Study Indicates Features of Value Co-creation when Implementing Innovation in Organizational Networks.

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.219-237
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• 2010

This study proposes a 'Bibliographic Universe Relationship Vocabulary'(burv) using the RDF/OWL Object Property under the SPO predicate logic according to the relationship type among all entities of bibliographic universe and implemented a 'relationship ontology system' to establish a new cataloging business domain called 'Relationship Description Cataloging' based on the ontology.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.257-263
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• 2022

The objective of this study was to see how artificial intelligence applications affected organizational innovation in Jordanian commercial banks. Both independent and dependent variables were measured in three dimensions: expert systems, neural network systems, and fuzzy logic systems for artificial intelligence applications variable. Product innovation, process innovation, and management innovation for the organizational innovation variable. To achieve study objectives, a questionnaire was developed and distributed to a sample of one hundred fifty-three managers in Jordanian commercial banks, who were selected according to the simple random sampling method. Except for the neural network systems dimension, which comes in at an average level, the study indicated that there is a high level of organizational innovation and artificial intelligence applications. Furthermore, the findings revealed that artificial intelligence applications have a significant impact on organizational innovation in Jordanian commercial banks, with the most important artificial intelligence application being a fuzzy logic system. The study suggested keeping track of technological advancements in the field of artificial intelligence applications and incorporating them into banking operations by benchmarking with the best commercial bank practices and allocating a portion of the budget to technological applications and infrastructure development, as well as balancing between technology use and information security risks to ensure client privacy is protected.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.347-357
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• 2003

The web is an important source of information and most of Web applications are based on form documents in HTML-based form documents only play a role as user interfaces, and they do not involve the procedures or rules if business process which form document designers assume. However, from documents imply methods for treating documents, and these embedded procedural knowledge can be utilized.actively in automation of business process. In this respect, we Investigate the activeness of documents with cognitive science to automate business processes based on from documents. Through this, we have a new concept and applicability of active documents. Our active documents include business rules and declarative knowledge to support the automation of document processing. Also, we propose a processing framework for the active documents. The framework has two phases: build-time and run-time. in order to demonstrate the usefulness of the proposed framework, a prototype called ActiveForm is designed and implemented for requisition processing them in an inference engine can enhance the intelligence of Internet applications.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.75-91
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• 2023

As the business-to-customer (B2C) online market expands after the COVID-19 pandemic, the logistics industry has been constructing automated warehouses to handle multi-product, low-volume logistics. When constructing a logistics automation warehouse, it is crucial to validate that the facility's performance and operational logic are designed to meet the required throughput of the automated warehouse from the system design phase. This study proposes simulation-based validation and optimal alternatives for an H logistics automation warehouse in Iksan, Jeollabuk-do. Firstly, we focused on the box supply and packing processes, which are related to the release process, among the entire logistic processes. Then, we analyzed the potential bottlenecks in the target process and designed and implemented a discrete-event simulation model based on the analysis results. The simulation experiments showed that the facility parameters and operational logic identified in the system design phase did not satisfy the performance requirements of the entire automated warehouse. Additional experiments were conducted to suggest alternatives to meet the system performance requirements by changing the facility parameters and operational logic. We expect that the proposed study will be utilized in the future, not only in the system design phase but also in the system construction phase, for verification purposes to ensure that the construction proceeds according to the design.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.