Jose L. Salmeron

Benchmarking main activation functions in fuzzy cognitive maps

Fuzzy cognitive maps (FCM) are graph-based modeling tools. FCM can to be used for structuring and supporting decisional processes. Also, FCM allow developing what-if analysis, through the definition of scenarios. It is possible to choose among four activation functions: (1) sigmoid function, (2) hyperbolic tangent function, (3) step function and (4) threshold linear function. The use of each function can provide different alternatives. In this context, the main objective of the present study is to develop a benchmarking analysis among the mentioned functions using a same decisional model. Findings show how the sigmoid function offers significantly greater advantages than the other functions.

A Review of Fuzzy Cognitive Maps Research During the Last Decade

This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years.

An AHP-based methodology to rank critical success factors of executive information systems

For academics and practitioners concerned with computer-based information systems, one central issue is the study of critical success factors (CSF) of information systems development and implementation. Whereas several critical success factors analyses appear in the literature, most of them do not have any technical background. In this paper, we propose the use of the analytic hierarchy process (AHP) to set critical success factors priorities. Results suggest that technical elements are less critical than information and human factors and that an adequate knowledge of the information requirements of users is the most important critical success factors related with executive information systems (EIS).

Modelling IT projects success with fuzzy cognitive maps

Abstract IT projects have certain features that make them different from other engineering projects. These include increased complexity and higher chances of project failure. To increase the chances of an IT project to be perceived as successful by all the parties involved in the project from its conception, development and implementation, it is necessary to identify at the outset of the project what the important factors influencing that success are. Current methodologies and tools used for identifying, classifying and evaluating the indicators of success in IT projects have several limitations that can be overcome by employing the new methodology presented in this paper. This methodology is based on using Fuzzy Cognitive Maps (FCMs) for mapping success, modelling Critical Success Factors (CSFs) perceptions and the relations between them. This is an area where FCM has never been applied before. The applicability of the FCM methodology is demonstrated through a case study based on a new project idea, the Mobile Payment System (MPS) project, related to the fast evolving world of mobile telecommunications.

Augmented fuzzy cognitive maps for modelling LMS critical success factors

This paper proposes to build an Augmented Fuzzy Cognitive Map-based for modelling Critical Success Factors in Learning Management Systems. The study of Critical Success Factors helps decision makers to extract from the multidimensional learning process the core activities that are essential for success. Using Fuzzy Cognitive Maps for modelling Critical Success Factors provides major assistance to the e-learning community, by permitting prediction comparisons to be made between numerous tools measured by multiple factors and its relations.

Fuzzy modeling Enterprise Resource Planning tool selection

ERP tool selection can be seen as one of the most relevant decision-making stages for an organization. This task is conceived as being one of the most difficult ones when an organization is trying to acquire an ERP tool. This work proposes structuring the decision-making selection of ERP tools. With this purpose, we have applied a Fuzzy Cognitive Map based approach capable of offering a definitively organized and structural outline in the acquisition of an ERP tool. Also, this proposed model offers a selection model where the more relevant criteria, their intensity and the relationships between them are identified.

Modelling grey uncertainty with Fuzzy Grey Cognitive Maps

An innovative and flexible model based on Grey Systems Theory and Fuzzy Cognitive Maps called Fuzzy Grey Cognitive Maps (FGCM) is proposed. It can be adapted to a wide range of problems, specially in multiple meaning-based environments. FGCMs offer some advantages in comparison with others similar tools. First, the FGCM model is defined specifically for multiple meanings (grey) environments. Second, the FGCM technique allows the defining of relationships between concepts. Through this characteristic, decisional models that are more reliable for interrelated environments are defined. Third, the FGCM technique is able to quantify the grey influence of the relationships between concepts. Through this attribute, a better support in grey environments can be reached. Finally, with this FGCM model it is possible to develop a what-if analysis with the purpose of describing possible grey scenarios. IT projects risks are modelled to illustrate the proposed technique.

Fuzzy cognitive maps for artificial emotions forecasting

At the present, emotion is considered as a critical point of human behaviour, and thus it should be embedded within the reasoning module when an intelligent system or a autonomous robot aims to emulate or anticipate human reactions. Therefore, current research in Artificial Intelligence shows an increasing interest in artificial emotion research for developing human-like systems. Based on Thayers emotion model and Fuzzy Cognitive Maps, this paper presents a proposal for forecasting artificial emotions. It provides an innovative method for forecasting artificial emotions and designing an affective decision system. This work includes an experiment with three simulated artificial scenarios for testing the proposal. Each scenario generate different emotions according to the artificial experimental model.

A multicriteria approach for risks assessment in ERP maintenance

Enterprise resource planning (ERP) systems cannot remain static after their implementation, they need maintenance. ERP maintenance is a key process required by the rapidly changing business environment and the usual software maintenance needs. However, these projects are highly complex and risky. So, the risks management associated with ERP maintenance projects is crucial to attain a satisfactory performance. Unfortunately, ERP maintenance risks have not been studied in depth. For this reason, this paper presents a general risks taxonomy. It gathers together the risks affecting the performance of ERP maintenance. Moreover, the authors use the analytic hierarchy process (AHP) methodology to analyze the risks factors identified. It helps managers, vendors, consultants, auditors, users and IT staff to manage ERP maintenance better. Results suggest that the most critical stage in ERP maintenance is the first phase, which receives, identifies, classifies and ranks the software modification. The most important hazards in ERP maintenance are the cooperation and commitment of ERP users and managers.

Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps

Worldwide, firms have made great efforts to implement Enterprise Resource Planning (ERP) systems. Despite these efforts, ERP adoption success is not guaranteed. Successful adoption of an ERP system also depends on proper system maintenance. For this reason, companies should follow a maintenance strategy that drives the ERP system toward success. However, in general, ERP maintenance managers do not know what conditions they should target to successfully maintain their ERP systems. Furthermore, numerous risks threaten these projects, but they are normally dealt with intuitively. To date, there has been limited literature published regarding ERP maintenance risks or ERP maintenance success. To address this need, we have built a dynamic simulation tool that allows ERP managers to foresee the impact of risks on maintenance goals. This research would help professionals manage their ERP maintenance projects. Moreover, it covers a significant gap in the literature.