Time series analysis models, understands, and predicts phenomena from different domains such as meteorology, medicine, and economics. In this context, Fuzzy Time Series has been standing out due to ...its capacity of using mathematical functions to represent linguistic variables, resulting in interpretative and more accurate models. Several studies aim at improving time series forecasting using fuzzy set theory, however such efforts were executed solely to obtain modeling improvements in the fuzzification stage, disregarding the stochastic and deterministic influences composing time series to assist the modeling process. In an attempt to fill out such gap, this manuscript employs the Empirical Mode Decomposition (EMD) to extract the deterministic influences which are next modeled using fuzzy clustering to improve time series forecasting. EMD reduces the data imprecision and uncertainty thus helping the fuzzy clustering stage that automatically finds an adequate space partitioning to produce the fuzzy sets. We use Fuzzy C-means to generate fuzzy sets with different characteristics, contributing to improvements in the definition of the number of sets, usually barely explored in the fuzzification stage. Our method was assessed using different validation indices while forecasting time series, which has confirmed promising results and supported the interpretability of data ranges along time.
•FTS stands out for using mathematical functions to represent linguistic variables.•Our approach models deterministic influences using fuzzy to improve TS forecasting.•We use FC-means to estimate the number of sets barely explored in fuzzification stage.•Our experiments were designed using the same TS and setup proposed in the literature.•Our approach overcomes studies from the literature while reducing noise influences.
Clearly, ontology components are distinguished depending on features of inexactitudes and uncertainties. Such shortcomings are mostly the outcome an indistinct inaccurate semantic lingual ...representation, supplied by professionals. So as to tackle this lack of exactness issue, the notion of ”fuzziness” has to be considered. In this respect, fuzzy ontologies have been shown to be useful tools to represent specific knowledge (crisp and fuzzy) and reasoning over it. Thus, an advanced fuzzy ontology called the COVID-19 Fuzzy Ontology (CFO) is exhibited in this work. The latter licenses a semantically meaningful representation of fuzzy crusty medical data particulars related to the diagnosis of COVID-19. The CFO also takes into account the imprecise aspects associated to the induced knowledge of this disease. The CFO is grounded from a domain ontology about COVID-19. The evaluation of the CFO ontology shows that it is accurate, consistent, and that it covers COVID-19 terminologies.
Optimization of a training set - fuzzification of the output vectors.•A significantly lower prediction error - the proposed improvements increase the performance of neural networks.•A neural network ...can work with data sets with varying scalable values.
This article describes a new approach which uses a histogram to fuzzify variables. We used a linguistic expression to form a training set output vector. The whole fuzzification process of the training set output vector is described in detail. This proposed method was verified on a real data set. We found out that the adaptation of a neural network by fuzzified output vectors has a considerably lower prediction error rate compared with another one without such transformation. Another advantage of the fuzzification approach is that only one neural network can be used for more various data sets with a high range of data attributes (units, thousands, millions). The proposed improvements increase the performance of neural networks, which is presented in the final part.
•Develop a novel hybrid forecasting system focusing on small sample forecasting.•Forecast the renewable energy power generation of different regions in the world.•Improve the fuzzification ability by ...combined strategy and hesitant fuzzy sets.•Search the optimal aggregated weights based on improved optimization algorithm.•Introduce the chaotic map into the equilibrium optimization algorithm.
Renewable energy power generation has significant potential to electricity supply sector with great sense to greenhouse gas control. Thus, it is vital to develop an effective forecasting model for renewable energy power generation forecasting which can provide a reference and basis for power generation planning and the energy strategic deployment. However, influenced by the complex data characteristic and sample size limitation, the application of some traditional forecasting models is restricted with poor forecasting performance. In this paper, a novel fuzzy time series forecasting based on combined fuzzification strategy and improved optimization algorithm is proposed for renewable energy power generation forecasting. The hesitant fuzzy sets are applied to deal with the combined fuzzification strategy and the improved optimization algorithm is developed to optimize the aggregate weights of the hesitant fuzzy sets. The experimental analysis and discussion all demonstrated the excellent performance of the proposed forecasting system in small sample forecasting for renewable energy power generation forecasting.
Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In ...the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be provided.
Vehicular ad-hoc network (VANET) technology has gained prominence, especially in the context of the emerging field of VANET Cloud as an integral part of connected and autonomous vehicles. The ...automotive industry’s move towards automation and the integration of vehicles into the digital ecosystem has revolutionized wireless network communications. Nevertheless, security remains a paramount concern in these advanced technological landscapes. Safeguarding system integrity and data privacy is of utmost importance before the widespread adoption of VANET Cloud solutions. This study addresses the critical challenge of security within the context of VANET Cloud. Specifically, the focus is on anticipating and mitigating Distributed Denial of Service (DDoS) attacks, which can potentially disrupt the functioning of connected vehicles and associated cloud-based services. To tackle this issue, an innovative architectural framework is proposed to capture and analyze network flows within the VANET Cloud environment. Additionally, it leverages machine learning techniques for classification and predictive analytics with an accuracy of 99.59%. The architecture presented in this research offers the potential to significantly enhance security measures in VANET Cloud deployments. Its adaptability ensures practical applicability to real-world systems, enabling timely responses to security threats and breaches.
This paper presents a new method to evaluate the quality of speech signals through images generated from a psychoacoustic model to estimate PESQ (ITU-T P862) values using a first-order Fuzzy Sugeno ...approach implemented in the Adaptive Neuro-Fuzzy Inference System - ANFIS. The factors feeding the network were obtained using an image-processing technique from the perceptual model coefficients. All simulations were performed using a database containing clean and corrupted signals by eight types of noises found in everyday situations. The proposal uses the PESQ values of the signals to train the network. The analyses proved that the predictive performance will depend on the choice of a psychoacoustic model, the factor extraction technique, the combination of these factors, the fuzzification algorithm, and the type of membership function in the ANFIS input space. The data sets for training and testing for each signal directory were randomly created and executed fifty times. The proposal achieves the best prediction values for PESQ when the averages of the measurements reach MAPE ≤0.09, RMSE ≤0.20, and R2≥95. In general, the approach provided satisfactory results compared to Multilayer Perceptron networks with their different learning algorithms, compared to another psychoacoustic model, to ITU-T P.563 and other non-intrusive methods that evaluate the quality of voice signals, and it was efficient regardless of the number of signals and the database used.
•Extraction of speech signal factors using image processing techniques.•Signal image extracted from a psychoacoustic model.•Non-intrusive measurement based on PESQ values trained by ANFIS.•Configuration of ANFIS with fuzzy c-means clustering fuzzification algorithm.•Using signals with different types of noises.
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