Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts

Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning

Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimation. In this study, we propose a zero-poles model and a data-driven evolutionary learning method for identification. We also perform a comparison with existing first and second order models and gradient descent identification methods. The proposed model is based on a five-phase description of heart rate dynamics. Data was collected from 30 healthy participants using a treadmill and a thoracic sensor in two protocols (static and dynamic), for increasing and decreasing activity. Results show that the zero-poles model is a good fit for heart rate response to exercise (Pearson's coefficient ρ>.95), while first and second order models are also suitable (ρ>.92). The evolutionary learning method shows excellent results for fast model identification, in comparison with least-squares methods (p<.03). We surmise that the parameters of investigated linear dynamic models make good candidates for digital biomarkers and continuous monitoring.

Boundary Spanning Methodological Approaches for Collaborative Moose Governance in Eeyou Istchee

Natural resource governance challenges are often highly complex, particularly in Indigenous contexts. These challenges involve numerous landscape-level interactions, spanning jurisdictional, disciplinary, social, and ecological boundaries. In Eeyou Istchee, the James Bay Cree Territory of northern Quebec, Canada, traditional livelihoods depend on wild food species like moose. However, these species are increasingly being impacted by forestry and other resource development projects. The complex relationships between moose, resource development, and Cree livelihoods can limit shared understandings and the ability of diverse actors to respond to these pressures. Contributing to this complexity are the different knowledge systems held by governance actors who, while not always aligned, have broadly shared species conservation and sustainable development goals. This paper presents fuzzy cognitive mapping (FCM) as a methodological approach used to help elicit and interpret the knowledge of land-users concerning the impacts of forest management on moose habitat in Eeyou Istchee. We explore the difficulties of weaving this knowledge together with the results of moose GPS collar analysis and the knowledges of scientists and government agencies. The ways in which participatory, relational mapping approaches can be applied in practice, and what they offer to pluralistic natural resource governance research more widely, are then addressed.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Causal Inference of Optimal Control Water Level and Inflow in Reservoir Optimal Operation Using Fuzzy Cognitive Map

Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow conditions. The current research uses a fuzzy cognitive map (FCM) as a tool to effectively model complex systems and then extracts systematic relationship diagrams from the dataset. A new fuzzy cognitive map with offset (FCM-O) is proposed to overcome the causal inference error caused by non-linear mapping of the activation function in a traditional FCM. With the application of inferring the causal relationship between the optimal control water level and inflow of ROO for the Three Gorges Reservoir (TGR), the experimental results show that, compared with FCM in the min data error, FCM-O reduces 11.11% and 7.14% in the training and the testing, respectively. Also, the experimental results of FCM-O are more reasonable than those of FCM. Finally, the following conclusions about the causal inference of optimal control water level and inflow in ROO for TGR are drawn: (1) The optimal control water level in September, October and November needs to be raised as much as possible to raise the water head of power generation, which is mainly affected by the constraints of the maximum operating water level of the reservoir rather than inflow; (2) the optimal control water level in January, February and March is positively affected by the inflow of the adjacent months; (3) the optimal control water level in April is due to the approaching flood season. In order to prevent water discarding, the water level is low and the optimum operation space is small. All of those shows that FCM-O is more competent than FCM in the causal relationship between optimal control water level and inflow in ROO.