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The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study. [ABSTRACT FROM AUTHOR]

This research work confronts a sender-based responsive and novel protocol named "Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)" protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of 'n' number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node's trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols. [ABSTRACT FROM AUTHOR]

In this manuscript, we present numerical solutions of a p-fuzzy system that models the Goodwin economic cycle. This model describes the dynamic interaction between labour share and the employment rate in an economy. One can use p-fuzzy systems to obtain solutions for differential equations whose the vector field are uncertain and partially known. The proposed p-fuzzy system is based on a fuzzy rule-based systems whose fuzzy rules represent economic premises described in the Goodwin model. We use two approaches to adjust the fuzzy terms of the corresponding fuzzy rule base from the economical data of a given country. The first approach is based on statical measures and the second one uses fuzzy c-means clustering method. Finally, we test our proposal to estimate the growth cycles of the labour share and the employment rate of Germany and we compare the obtained results with the historical data. [ABSTRACT FROM AUTHOR]

In this research, we succeeded in introducing a new reasoning procedure which applies interval type-2 fuzzy sets into a rule induction process. Our proposal allows information granulation which resulted in achieving good experimental results. We introduced decision tables with elements assumed as interval type-2 fuzzy sets which greatly generalize information. Next, by applying corresponding rule induction procedure, we introduced the possibility to generate directly from a benchmark data fuzzy rulebases for type-2 fuzzy inference models. We strongly believe that our reasoning approach will be a proper solution for different research issues such as classification or ranking procedures as well as determining knowledge for fuzzy inference models. The method proposed was tested in a classification problem verified by using medical benchmark data. [ABSTRACT FROM AUTHOR]

First-order recurrent neural networks can be trained to recognize strings of a regular language. Finite state automata can be extracted from these neural networks. Normally, a search process in the output domain of the neurons is necessary for carrying out this extraction procedure. On the other hand, studies about fuzzy rules extraction from feedforward multilayered neural networks can be considered to define new techniques that transform first-order recurrent neural networks into finite state automata. With these new techniques, a fuzzy description of the action of each neuron can be obtained. From these descriptions, the transition function of the automaton can be directly found and, in this way, the search process is not necessary. A technique with this approach is presented in this paper. Besides, the used method to extract fuzzy rules from a neuron has the advantage that the inputs of the fuzzy system coincide with the inputs of the neuron. Thus, the fuzzy system is more intuitive. Once the transition function is obtained, the automaton structure can be found with the analysis of the transitions for every state and input from the initial state. Finally, several examples are presented to illustrate the method. [ABSTRACT FROM AUTHOR]

In the past, intelligent system often realized reasoning operation by interpolation method for one-dimensional sparse rule base, and could not analyze fuzzy reasoning of multi-dimensional sparse rule condition, which greatly improved the error and volatility of reasoning results. Therefore, a multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is proposed. Through the CMAC neural network, the influence weight of each variable is extracted. CMAC neural network is applied to train weights of multi-dimensional variables in multiple multi-dimensional fuzzy reasoning rules, and local correction weights are made, so that the weights of each modification are very few. After fast learning, the influence weights of the multi-dimensional variables on the reasoning result are obtained. A multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is applied to input the given neighboring rules into CMAC neural network, and the weights of the variables in the neighboring rules are obtained. According to the linear interpolation and the sequence of interpolation cardinal numbers, the influence weights of the variables in the observation value are determined. According to the linear interpolation reasoning method, a new fuzzy rule is constructed. Based on the approximation between the new fuzzy rules and the observed values, the similarity between the predicted values and the new fuzzy rules is constructed. The result of fuzzy inference is obtained according to the similarity. The experimental results show that the proposed algorithm has high reasoning precision and stability, and the practical application effect is good. [ABSTRACT FROM AUTHOR]

Sentiment (or emotional) dictionaries play a key role and are “the materials” for sentiment analysis applications, especially in Vietnamese where the corpus is not yet available. In this research, we propose a sentiment dictionary for Vietnamese, known as VNSD, which adopts machine translation, Logistic Regression, “double propagation,” and fuzzy rules. The sentiment dictionary counts approximately 5,000 adjectives, 2,000 verbs, 300 nouns, and more than 200 adverbs, which lead to a combination of more than 100,000 sentiment phrases covering the Vietnamese emotional lexicons. We found that a hybrid approach offers better performance compared to stand-alone data-mining or natural language processing techniques. The key contribution of VNSD that distinguishes it from related work is a deep investment in exploiting Vietnamese linguistic characteristics to propose suitable rules for computing the sentiment scores of Vietnamese text phrases. [ABSTRACT FROM AUTHOR]

Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses. [ABSTRACT FROM AUTHOR]

In this study, a fracture-healing model based on the mechanical environment and blood supply conditions was proposed to simulate and predict the complex regenerative repair process for tissues. A three-dimensional finite element model was established to describe fracture callus mechanics based on tetrahedral units. The blood perfusion was regarded as a spatio-temporal state variable and was included into the model to simulate a revascularization process. The dynamic model described the callus mechanics and biological processes of tissue differentiation by using a combination of a finite element method and fuzzy logic. The callus healing process was simulated time-discretely by an explicit Euler integration iterative loop over equidistant time steps and was implemented via Visual Studio 2012. Finally, the model predicted the value of interfragmentary of two groups with different axial stabilities. A comparison of the results with the experiment data indicated that the simulation results were distributed within the scope of the average deviation of the experimental data, and this corresponded well with the experiment trend and numerical value. The agreement between the simulation results and experimental results verified the accuracy of the fracture-healing model and demonstrated its advantages for simulating healing process. [ABSTRACT FROM AUTHOR]

A substantial amount of scientific research and publications in the smart home domain deal with different aspects of fuzzy logic application. However, we feel there is a general lack of complex modelling done of the whole smart home environment. So, the main goal of this paper is threefold: 1) to present a virtual model of a smart home environment to be intellectualized, 2) to demonstrate the efficiency of Mamdani type inference procedure in modelling of intellectual behaviour of the environment and 3) to show that it is possible to minimize the quantity of fuzzy rules by taking the hierarchical approach and adding the Takagi-Sugeno inference procedure to empower it. The results of computerized modelling and simulation are delivered in the paper demonstrating the practical viability and efficiency of the theoretical approach. The model of the intelligent home environment was used in two projects. Since a formalized analytical method for evaluating the sensitivity of system parameters still does not exist, the experimental sensitivity simulation in our case is performed and presented in this paper. Characteristics obtained in the modelled virtual environment can be easily expanded and used in a real home environment by changing pixels into real coordinates and the light intensity into other real variables. [ABSTRACT FROM AUTHOR]