A knowledge management system based on artificial intelligence (AI) methods: A flexible fuzzy regression-analysis of variance algorithm for natural gas consumption estimation

The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness; the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.

Combining nonlinear programming and least squares method, this paper proposes a two-stage mixed fuzzy linear regression model based on distance criterion. In order to ensure that the error of fuzzy estimated value and fuzzy observation value can be reduced when the explanatory variable is a clear number, the fuzzy regression model has a fuzzy adjustment term in addition to the clear regression coefficient. Firstly, based on the distance criterion, a nonlinear programming model is established to obtain the regression coefficient of the explanatory variables, and the fuzzy adjustment term is obtained based on the distance criterion and the least square method. Compared with the existing methods that cannot determine the sign of the coefficient, the method can accurately determine the sign of the regression coefficient. Finally, it is verified that the model has smaller mean square error and higher reliability than other models through a large number of numerical experiments and practical examples.

In the present dynamic and knowledge-driven business environment, Knowledge Management (KM) has emerged as a critical factor for enhancing organizational performance, fostering innovation, and ensuring long-term sustainability. Organizations increasingly rely on effective KM practices to systematically create, store, share, and apply knowledge, thereby improving decision-making processes, strengthening collaboration, and achieving strategic objectives. KM serves as a catalyst for managing intellectual assets and transforming them into valuable organizational outcomes, contributing significantly to competitiveness and growth . Despite its growing importance, the implementation of effective KM practices remains a significant challenge across organizations. Various barriers such as cultural resistance, technological limitations, lack of resources, and inadequate organizational support hinder the successful adoption of KM systems. Cultural dimensions, including organizational hierarchy, power distance, and communication patterns, play a crucial role in influencing knowledge-sharing behavior and the overall effectiveness of KM practices. In many cases, rigid organizational structures and resistance to change restrict open communication and limit the free flow of knowledge within organizations . Furthermore, the increasing emphasis on sustainability and digital transformation has highlighted the need for robust and adaptive KM frameworks. Emerging concepts such as Green Knowledge Management (GKM) emphasize the role of KM in achieving environmental, social, and economic sustainability. Organizations are increasingly integrating KM practices with sustainability goals to promote resource efficiency, environmental responsibility, and long-term development. At the same time, advancements in digital technologies, including artificial intelligence (AI), data analytics, and information systems, are transforming traditional KM processes by enhancing knowledge creation, storage, and dissemination capabilities . Although significant progress has been made in KM research, several gaps still exist in understanding its application across diverse organizational and industrial contexts. Existing KM frameworks often fail to adequately address cultural and contextual differences, which are critical for successful implementation. Moreover, while KM has been extensively studied in large organizations, its application in small and medium-sized enterprises (SMEs) remains relatively underexplored. SMEs face unique challenges such as limited resources, lack of structured KM systems, and informal management practices, which affect the effectiveness of KM initiatives . In addition, there is a need for more empirical and longitudinal studies to evaluate the long-term impact of KM practices on organizational performance, innovation, and sustainability. Many existing studies are conceptual in nature and lack practical validation across different industries and cultural settings. The integration of KM with emerging technologies and its role in supporting innovation and decision-making processes also require further exploration . This study adopts a literature review-based approach to examine the current landscape of Knowledge Management research, with a focus on its interrelationship with culture, innovation, and sustainability. By synthesizing findings from various studies, the research aims to provide a comprehensive understanding of KM practices, identify key challenges and research gaps, and suggest directions for future research. The study contributes to the existing body of knowledge by highlighting the importance of context-specific KM frameworks and the need for integrating technological, cultural, and sustainability dimensions in KM practices.

Purpose This paper aims to address the limitations of traditional knowledge management (KM) theories in the era of artificial intelligence (AI) and propose a comprehensive framework to redefine KM in the context of AI-driven technological advancements. Design/methodology/approach The study constructs the “Cognition-Process-Organization-Strategy” (CPOS) four-dimensional collaborative theoretical framework. It integrates insights from traditional KM theories, AI technologies and emerging practices to systematically analyze the impact of AI on KM. The framework is developed through a three-stage logic of technology-driven transformation, theoretical adaptation and system reconstruction. Findings The CPOS framework reveals that AI technologies fundamentally reshape KM by enabling cognitive reconstruction, process parallelization, organizational decentralization and strategic paradigm shifts. It highlights the transition from human-centered KM to human–machine collaborative knowledge production, emphasizing dynamic, open and distributed knowledge ecosystems. Originality/value This paper contributes to the field by bridging the gap between traditional KM theories and AI applications. It introduces a novel theoretical framework that redefines KM in the AI era, offering a systematic approach to understanding and implementing AI-driven KM systems. The CPOS framework provides a comprehensive roadmap for organizations to navigate the challenges and opportunities of, AI, KM, CPOS framework, human–machine collaboration.

Many mobile knowledge management (KM) systems have been described in the literature having unique and interesting features, each being inserted into a specific application domain. This fragmentation in the application domain makes it difficult to compare and define a set of core features of mobile KM systems. Analysing and comparing features and strategies of several systems from different application domains makes it possible to identify the core features that form a mobile KM system. Consequently, we may use these core features to define the evaluation and comparison framework of mobile KM systems, regardless of its application domain. The purpose of this work is to define these core features and discuss their importance, through the analysis of six mobile KM systems, highlighting the positive and the negative aspects of the KM processes. The contributions of this work are the framework and the list of core features of mobile KM systems.

Many mobile knowledge management (KM) systems have been described in the literature having unique and interesting features, each being inserted into a specific application domain. This fragmentation in the application domain makes it difficult to compare and define a set of core features of mobile KM systems. Analysing and comparing features and strategies of several systems from different application domains makes it possible to identify the core features that form a mobile KM system. Consequently, we may use these core features to define the evaluation and comparison framework of mobile KM systems, regardless of its application domain. The purpose of this work is to define these core features and discuss their importance, through the analysis of six mobile KM systems, highlighting the positive and the negative aspects of the KM processes. The contributions of this work are the framework and the list of core features of mobile KM systems.

This study presents an integrated fuzzy regression and time-series technique to estimate and predict electricity demand. Furthermore, it is difficult to model uncertain behaviour of energy consumption with only conventional time-series and fuzzy regression, which could be an ideal substitute for such cases. After reviewing various fuzzy regression models and studying their advantages and shortcomings, the best model is selected. Also, the impact of data preprocessing and post-processing on the fuzzy regression performance is to study and to show that this method does not contribute to the efficiency of the model. In addition, another unique feature of this study is utilisation of autocorrelation function to define input variables versus trial and error method. At last, the comparison of actual data with fuzzy regression and ARIMA model, using Granger?Newbold test, is achieved. Monthly electricity consumption of Iran from 1995 to 2005 is considered as the case of this study.

This study investigates the factors affecting the perceived usefulness of and the intention to use knowledge management (KM) systems by students. The research model posits that the intention to use KM systems in higher education depends on perceived usefulness, perceived user-friendliness, organizational rewards, and community of practice. A survey method was used to collect the data for the study. We used a convenience sample consisting of undergraduate students enrolled in various business courses in a Canadian University. The data obtained from a sample of 120 students were initially factor analyzed to identify the relevant factors. Separate factor analysis was conducted for each of three types of measures – the independent measures, the intermediate measures, and the dependent measure. In order to test the proposed hypotheses, we employed the method of multiple regression analysis. The findings suggest that organizational rewards and KM system characteristics positively impact perceived usefulness, and that user-friendliness, usefulness, organizational rewards, and community of practice are significant predictors of intention to use KM system. This analysis reveals that business schools need to focus on usefulness and practical relevance of knowledge captured in knowledge management systems. This is in line with the current debate in management education regarding the appropriateness of methods employed to teach business knowledge. Organizational rewards being a significant predictor of intention to use KM systems corroborate the expectancy theory. Therefore, it is important for business schools to communicate on the usefulness of their KM systems but also to encourage its usage through different incentives.

The purpose of this study is to examine the effect of the knowledge management (KM) system on the hospital services performance. The hospital located in DKI Jakarta (Indonesia) that has implemented the KM system as a pilot project is the population of this research. Where the respondents are medical personnel at the hospital. This study analyzes the correlation between the use of KM systems and the performance of hospital services, using independent-samples T-tests and ANOVA tests from SPSS and R Commander programs to prove the hypothesis of the two variables, namely the use of KM systems (capabilities) and hospital services performance. The results of this research have succeeded in proving that there are significant differences between medical personnel who rarely use the KM system and who often use it, with those who often have a greater level of service performance. Thus, it can be concluded that the KM system used by hospital has a positive effect seen in the performance of medical personnel.

he paper purposes to explore the association between AI and KM by analyzing how AI can contribution companies in handling and using information to improve knowledge processes efficiently. An electronic bibliographic database search and the reference lists of pertinent review articles were used to conduct a systematic review. To guarantee a thorough and organized approach to examining the literature, the study utilized the PRISMA criteria and the PICO model. The results found that the strong relationship between AI and KM. Knowledge management (KM) offers a structure for organizing and managing knowledge, while artificial intelligence (AI) enables machines to learn and process enormous amounts of data. This similarity between AI capabilities and KM framework is crucial for improving organizational information management procedures. Factors including organizational structure, rewards/incentives, and communication, trust, and information systems all play a role in how firms share their knowledge. Organizational performance is enhanced through optimized knowledge management and flow when AI is integrated into KM systems. This leads to more efficient knowledge resource distribution, providing organizations with a competitive edge. Future research should explore the potential of smart assistant systems, which offer social welfares and strengthen competitive advantage. The study also emphasizes the importance of understanding leadership traits and organizational structures that are essential for consistent performance through effective KM practices

Using case-based reasoning (CBR), the authors integrate the techniques of cognitive task analysis (CTA), hierarchical clustering and ontology and propose a Web-based CBR knowledge management (KM) system for investigating the construction of a KM system with multiple information techniques to support KM activity in industry. The maintenance service centres of a computer company are used as an example to illustrate extracting the maintenance knowledge necessary to construct a PC troubleshooting KM system. The effectiveness of applying a Web-based CBR KM system to support KM activities in the KM life cycle is subjected to practical verification.

Discussions at previous HICSS conferences have shown that there is no general agreement on definitions of knowledge management (KM) and knowledge management systems (KMS) success. A deep understanding of these concepts would help to design and implement efficient KM initiatives and systems. We present an exploratory research study to begin and facilitate a debate that will hopefully lead to a consensus definition of KM and KMS success. We chose an expert panel approach followed by two exploratory surveys to approach the KM and KMS success definition. The research shows only a few points of consensus. We present areas of agreement as well as of disagreement, which serve as a good starting point for further discussions on KM and KMS success

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

Error measures for fuzzy linear regression: Monte Carlo simulation approach