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Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records' label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems [ABSTRACT FROM AUTHOR]

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations. [ABSTRACT FROM AUTHOR]

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of complex evolutionary artificial intelligence in cognitive digital twinning. [ABSTRACT FROM AUTHOR]

In decision-making problem, fuzzy system is considered as an effective tool with access to uncertain information by fuzzy representations. Evolutionary fuzzy systems have been developed with the appearance of intuitionistic fuzzy, hesitant fuzzy, neutrosophic representations, etc. Moreover, by capturing compound features and convey multifaceted information, complex numbers are utilized to generalize fuzzy and intuitionistic fuzzy sets. However, the order relations established in these existing systems have certain limitations, such as they are not total order relations or they are defined based on intermediate functions, hence it is difficult to use in building and ensuring important properties of logical operators and distance measures in the systems. In this article, a representation of the intuitionistic fuzzy systems based on complex numbers (IFS-C) in the polar form by a new way is proposed to overcome the above restrictions. Specifically, an intuitionistic fuzzy set is characterized by the two functions of modulus and argument. A new order relation, set-theoretic operations, and a new distance measure by the polar form of IFS-C are defined and investigated. The applicability of the proposal is illustrated by a new decision-making model called P-distance measure. It is tested on the benchmark medical datasets in comparison with the existing methods. The experiments confirm the advantages of the proposal. [ABSTRACT FROM AUTHOR]

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness. [ABSTRACT FROM AUTHOR]

Neutrosophic set is a powerful general formal framework which generalizes the concepts of classic set, fuzzy set, interval-valued fuzzy set, intuitionistic fuzzy set, etc. Recent studies have developed systems with complex fuzzy sets, for better designing and modeling real-life applications. The single-valued complex neutrosophic set, which is an extended form of the single-valued complex fuzzy set and of the single-valued complex intuitionistic fuzzy set, presents difficulties to defining a crisp neutrosophic membership degree as in the single-valued neutrosophic set. Therefore, in this paper we propose a new notion, called interval complex neutrosophic set (ICNS), and examine its characteristics. Firstly, we define several set theoretic operations of ICNS, such as union, intersection and complement, and afterward the operational rules. Next, a decision-making procedure in ICNS and its applications to a green supplier selection are investigated. Numerical examples based on real dataset of Thuan Yen JSC, which is a small-size trading service and transportation company, illustrate the efficiency and the applicability of our approach. [ABSTRACT FROM AUTHOR]

The health care support system is a special type of recommender systems that play an important role in medical sciences nowadays. This kind of systems often provides the medical diagnosis function based on the historic clinical symptoms of patients to give a list of possible diseases accompanied with the membership values. The most acquiring disease from that list is then determined by clinicians’ experience expressed through a specific defuzzification method. An important issue in the health care support system is increasing the accuracy of the medical diagnosis function that involves the cooperation of fuzzy systems and recommender systems in the sense that uncertain behaviors of symptoms and the clinicians’ experience are represented by fuzzy memberships whilst the determination of the possible diseases is conducted by the prediction capability of recommender systems. Intuitionistic fuzzy recommender systems (IFRS) are such the combination, which results in better accuracy of prediction than the relevant methods constructed on either the traditional fuzzy sets or recommender system only. Based upon the observation that the calculation of similarity in IFRS could be enhanced by the integration with the information of possibility of patients belonging to clusters specified by a fuzzy clustering method, in this paper we propose a novel hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis so-called HIFCF (Hybrid Intuitionistic Fuzzy Collaborative Filtering). Experimental results reveal that HIFCF obtains better accuracy than IFCF and the standalone methods of intuitionistic fuzzy sets such as De, Biswas & Roy, Szmidt & Kacprzyk, Samuel & Balamurugan and recommender systems, e.g. Davis et al. and Hassan & Syed. The significance and impact of the new method contribute not only the theoretical aspects of recommender systems but also the applicable roles to the health care support systems. [ABSTRACT FROM AUTHOR]

Medical diagnosis has been being considered as one of the important processes in clinical medicine that determines acquired diseases from some given symptoms. Enhancing the accuracy of diagnosis is the centralized focuses of researchers involving the uses of computerized techniques such as intuitionistic fuzzy sets (IFS) and recommender systems (RS). Based upon the observation that medical data are often imprecise, incomplete and vague so that using the standalone IFS and RS methods may not improve the accuracy of diagnosis, in this paper we consider the integration of IFS and RS into the proposed methodology and present a novel intuitionistic fuzzy recommender systems (IFRS) including: (i) new definitions of single-criterion and multi-criteria IFRS; (ii) new definitions of intuitionistic fuzzy matrix (IFM) and intuitionistic fuzzy composition matrix (IFCM); (iii) proposing intuitionistic fuzzy similarity matrix (IFSM), intuitionistic fuzzy similarity degree (IFSD) and the formulas to predict values on the basis of IFSD; (iv) a novel intuitionistic fuzzy collaborative filtering method so-called IFCF to predict the possible diseases. Experimental results reveal that IFCF obtains better accuracy than the standalone methods of IFS such as De et al., Szmidt and Kacprzyk, Samuel and Balamurugan and RS, e.g. Davis et al. and Hassan and Syed. [ABSTRACT FROM AUTHOR]

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones. [ABSTRACT FROM AUTHOR]

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS. [ABSTRACT FROM AUTHOR]

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER). [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks are non-dependent on any wired infrastructure and provide exceptional benefits during typical situations like conference, fair/exhibition, disaster recovery and emergency rescue operations. But these networks have to deal with several open issues, since they are battery operated and the dynamic topology can change due to mobility. Thus, multi-hop wireless ad hoc networking needs an energy efficient routing protocol that discovers/maintains routes to any other member node in the network, minimizing computation/communication overhead and battery power consumption. An adaptive neuro fuzzy inference system (ANFIS) based ad hoc routing approach has not been investigated before. In this paper, an ANFIS and cross layer approach-based energy efficient routing protocol for wireless multi-hop networks is proposed and its performance is compared with the Ad Hoc On-demand Distance Vector (AODV) Routing Protocol and fuzzy based AODV, using the network simulator ns2. Using remaining battery power, hop count and link stability as the input variables, the ANFIS module computes the optimum path cost, as the output. The proposed ANFIS based approach produces better results when compared using the metrics: average throughput, packet delivery ratio, average end-to-end delay in packet delivery between source and destination and average routing overhead. [ABSTRACT FROM AUTHOR]

The decision-making problems based on fuzzy inference systems have received much attention from the worldwide scientific community. The M-CFIS-FKG model is considered one of the best models to solve classification problems based on uncertain and amplitude input datasets. It can infer and find the output labels of new samples that are not in the fuzzy rule base. Recently, the FKG-Pairs model has been considered an extension of FKG in the M-CFIS-FKG model by combining attribute pairs to infer and find the output labels in the cases of input datasets with incomplete gathering. It has overcome the limitation of the M-CFIS-FKG model. However, with real-time large input datasets or too-small training datasets, the FKG-Pairs model has also revealed limitations as it takes too much time to compute, and the accuracy is still relatively low. This paper has proposed a decision-making model in extreme cases (called FKG-Extreme) by using the Q-learning-based FKG-Pairs approach to enrich the fuzzy rule base after each time step with the cumulative mechanism of new rules. The proposed FKG-Extreme model has overcome the FKG-Pairs model's limitation in the extreme case. It has significantly improved the system's accuracy, while the computation time is acceptable. To validate the proposed model, we conducted experiments based on the standard UCI datasets, and the experimental results demonstrated that the system's performance in terms of accuracy is superior to the other reliable models in case of too small training data. Furthermore, the results of the two-way ANOVA also proved that the FKG-Extreme model is better than the FIS and FKG-Pairs models. [ABSTRACT FROM AUTHOR]