Fuzzy Domain Research Articles

Zinc Binding to Tau Influences Aggregation Kinetics and Oligomer Distribution.

Metal ions are well known modulators of protein aggregation and are key players in Alzheimer’s Disease, being found to be associated to pathologic protein deposits in diseased brains. Therefore, understanding how metals influence amyloid aggregation is critical in establishing molecular mechanisms that underlie disease onset and progression. Here, we report data on the interaction of full-length human Tau protein with calcium and zinc ions, evidencing that Tau self-assembly is differently regulated, depending on the type of bound metal ion. We established that Tau binds 4 Zn2+ and 1 Ca2+ per monomer while using native mass spectrometry analysis, without inducing order or substantial conformational changes in the intrinsically disordered Tau, as determined by structural analysis using circular dichroism and Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopies. However, Tau aggregation is found to proceed differently in the calcium- and -zinc bound forms. While the rate of aggregation, as determined from thioflavin-T (ThT) fluorescence kinetics, is highly increased in both cases, the reaction proceeds via different mechanisms, as evidenced by the absence of the lag phase in the reaction of zinc-bound Tau. Monitoring Tau aggregation using native mass spectrometry indeed evidenced a distinct distribution of Tau conformers along the reaction, as confirmed by dynamic light scattering analysis. We propose that such differences arise from zinc binding at distinct locations within the Tau sequence that prompt both the rapid formation of seeding oligomers through interactions at high affinity sites within the repeat domains, as well as amorphous aggregation, through low affinity interactions with residues elsewhere in the sequence, including at the fuzzy coat domain.

FLORA: a hierarchical fuzzy system for online accommodation review analysis

PurposeThis paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap.Design/methodology/approachFLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine.FindingsThe performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating.Research limitations/implicationsThis research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base.Practical implicationsThis research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system.Originality/valueThis research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

Behavioral Based Uncertainty Handling in Energy Depletion Reduction (BUEDR) through Alleviation of Holes in an IoT Enabled Wireless Sensor Networks

In current era the usage of Internet is gradually improvising from Internet of People to an Internet of Things (IoT). Through the gateway, Wireless Sensors Networks (WSN) are integrating things to the Internet. But there are two major elements which affect the performance of the network, the presence of void hole and energy hole. Due to the unavailability of forwarded nodes, a particular region may suffer from void hole. In addition, if the load of data is not correctly balanced among the sensor nodes, then energy hole occurs. To overcome these issues, the proposed work uses a threefold behavioral based uncertainty handling transmission policy. In the first fold, the area of WSN is divided using voronoi diagram, which resolves the geometric relationships between sensors and to distribute transmission uniformly over the network area. The second fold alleviates energy hole vagueness in an adversarial environment by gathering information about the node denoted by intuitionistic fuzzy domain based cluster head selection to enhance energy dissipation. The third fold is optimal route selection for which fuzzy fish swarm optimization is adopted. From the simulation results, it is observed that the energy consumption achieves its superiority by hole alleviation and thus maximizes the network life time.

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

Pumping phase modulation analysis for operational quality of a pumped-storage generating system

The integration of pumped-storage power with multi-energy sources pushes the electricity generation to concern about the voltage stability and reactive power balance. This study focuses on the effect of pumping phase modulation on the operational quality of the pumped-storage generating system. A precise mathematical model of the pumped-storage generating system with multiple units is presented, aiming at the analysis of dynamic performance and stability under the operating mode of the pumping phase modulation at timescale of seconds. The result reveals that the favorable capacity of leading phase modulation for unit 1 significantly decreases the release of reactive power of unit 2 owing to the effect of reactive power flow in different units. It is also obtained that the different amplitudes of external step disturbances cause the operation transition between the late phase and leading phase, while the random disturbances result in a fuzzy phase domain that refers to the obscure boundary of the leading & late phase modulation conditions in time domain (6.88 s, 10 s). The large disturbance decreases the leading phase depth of unit 1, which indirectly reduces the surplus reactive power absorbing of unit 2. The implemented method and results obtained from this method are beneficial to the risk mitigation and vibration diminishment of pumped-storage generating systems, which contributes to maximize the asset efficiency of pumped-storage power stations for generating electricity in multi-energy integration setups.

A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips

Multimodal emotion understanding enables AI systems to interpret human emotions. With accelerated video surge, emotion understanding remains challenging due to inherent data ambiguity and diversity of video content. Although deep learning has made a considerable progress in big data feature learning, they are viewed as deterministic models used in a “black-box” manner which does not have capabilities to represent inherent ambiguities with data. Since the possibility theory of fuzzy logic focuses on knowledge representation and reasoning under uncertainty, we intend to incorporate the concepts of fuzzy logic into deep learning framework. This paper presents a novel convolutional neuro-fuzzy network, which is an integration of convolutional neural networks in fuzzy logic domain to extract high-level emotion features from text, audio, and visual modalities. The feature sets extracted by fuzzy convolutional layers are compared with those of convolutional neural networks at the same level using t-distributed Stochastic Neighbor Embedding. This paper demonstrates a multimodal emotion understanding framework with an adaptive neural fuzzy inference system that can generate new rules to classify emotions. For emotion understanding of movie clips, we concatenate audio, visual, and text features extracted using the proposed convolutional neuro-fuzzy network to train adaptive neural fuzzy inference system. In this paper, we go one step further to explain how deep learning arrives at a conclusion that can guide us to an interpretable AI. To identify which visual/text/audio aspects are important for emotion understanding, we use direct linear non-Gaussian additive model to explain the relevance in terms of causal relationships between features of deep hidden layers. The critical features extracted are input to the proposed multimodal framework to achieve higher accuracy.

Quantale-valued fuzzy Scott topology

The aim of this paper is to extend the truth value table oflattice-valued convergence spaces to a more general case andthen to use it to introduce and study the quantale-valued fuzzy Scotttopology in fuzzy domain theory. Let $(L,*,varepsilon)$ be acommutative unital quantale and let $otimes$ be a binary operationon $L$ which is distributive over nonempty subsets. The quadruple$(L,*,otimes,varepsilon)$ is called a generalized GL-monoid if$(L,*,varepsilon)$ is a commutative unital quantale and the operation $*$ is$otimes$-semi-distributive. For generalized GL-monoid $L$ as thetruth value table, we systematically propose the stratified$L$-generalized convergence spaces based on stratified $L$-filters,which makes various existing lattice-valued convergence spaces asspecial cases. For $L$ being a commutative unital quantale, wedefine a fuzzy Scott convergence structure on $L$-fuzzy dcpos anduse it to induce a stratified $L$-topology. This is the inducing wayto the definition of quantale-valued fuzzy Scott topology, whichseems an appropriate way by some results.

A Framework for Optimal Attribute Evaluation and Selection in Hesitant Fuzzy Environment Based on Enhanced Ordered Weighted Entropy Approach for Medical Dataset.

Background:In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Extensive research that focuses on incorporating vagueness in the form of fuzzy sets, fuzzy rough sets and hesitant fuzzy sets (HFS) has been in past decades. Objective: The paper aims to develop an enhanced entropy based on the clustering technique for calculating the weights of the attributes to finally generate appropriately clustered attributes. Material and Methods: Finding optimal attributes to make a decision has always been a matter of concern for the researchers. Metrics used for optimal attribute generation can be broadly classified into mutual dependency, similarity, correlation and entropy based metrics in fuzzy domain .The experimentation has been carried out on ECG dataset in a hesitant fuzzy framework with four attributes. Results: We propose a novel correlation based on an algorithm that takes entropy based weighted attributes as input which effectively generates a relevant and non-redundant set of attributes. We have also derived correlation coefficient formulas for HFSs and applied them to clustering analysis under framework of hesitant fuzzy sets. The results show the comparison of the proposed mathematical model with the existing similarity based on algorithms. Conclusion: The selection of optimal relevant attributes certainly highlights the robustness and efficacy of the proposed approach. The entire experimentation and comparative results help us conclude that selection of optimal attributes in hesitant fuzzy domain certainly prove to be a powerful tool in order to express uncertainty in the process of data acquisition and classification.

Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance

With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.

Optimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid

An AC-DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC-DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC-DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC-DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power-flow technique, and by modelling uncertainties by Hong's two-point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max-min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6-bus AC-DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi-objective GA indicates that the optimised solution lies on the Pareto-front, thereby validating the proposed technique.

Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression

Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms.

A comprehensive review of type-2 fuzzy Ontology

Ontologies are not only crucial for extending the traditional web into the Semantic Web but also for developing intelligent applications, by converting the raw data into smart data, through semantic enrichment. However, crisp Ontologies are not able to represent fuzzy knowledge which is often encountered in real-world applications. Fuzzy Ontology introduces fuzzy logical rules in Ontology for representing imprecise domain concepts such as darkness, hotness, thickness, creamy etc. in a machine-readable and interoperable format. The performance of fuzzy Ontology decreases with the increase of fuzziness in the domain knowledge. Type-2 fuzzy Ontologies (T2FO) were introduced to represent the domain knowledge where the concepts are either extremely vague or their vagueness increases gradually. The type-2 fuzzy Ontology domain is continuously expanding and there is a need to provide a comprehensive review incorporating the literature of T2FO development approaches, its applications in different domains, reasoners developed for inferencing on type-2 fuzzy Ontology, and evaluation approaches. To perform a comprehensive survey about the T2FO, we used Google Scholar as the main literature research tool to review papers published between 1998 to 2018. We then summarized the published approaches by comparing their features proposed for T2FO development, reasoning or inference, and evaluation approaches. This paper also identifies the domains wherein the past T2FO has been used to develop real-world applications. We conclude this paper by summarizing the previous work, and by identifying the research gaps for investigators.

Characterizations of L-convex spaces via domain theory

We provide some new characterizations of L-convex spaces by using the way-below relation in domain theory. For this purpose, we first study the notion of domain finiteness in the lattice-valued case, and then introduce three kinds of spaces: algebraic L-closure spaces, restricted L-hull spaces, and L-entailment spaces. These three spaces are shown to be categorically isomorphic to L-convex spaces. Additionally, we introduce the notion of L-polytopes and prove that a subcollection of a dense L-convex structure is a base if and only if it contains all L-polytopes. These results indicate that in the study of the theory of (fuzzy) convex spaces, (fuzzy) domain theory has important applications.

Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces

Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.

Category and subcategories of (L,M)-fuzzy convex spaces

Inthispaper, (L,M)-fuzzy domain finiteness and (L,M)-fuzzy restricted hull spaces are introduced, and several characterizations of the category (L,M)-CS of (L,M)-fuzzy convex spaces are obtained. Then, (L,M)-fuzzy stratified (resp. weakly induced, induced) convex spaces are introduced. It is proved that both categories, the category (L,M)-SCS of (L,M)-fuzzy stratified convex spaces and the category (L,M)-WICS of (L,M)-fuzzy weakly induced convex spaces, are coreflective subcategories of (L,M)-CS. It is also proved that three isomorphic categories, namely, the category MCS of M-fuzzifying convex spaces, the category (L,M)-CGCS of (L,M)-fuzzy convex spaces induced by M-fuzzifying convex spaces and the category (L,M)-ICS of (L,M)-fuzzy induced convex spaces, are coreflective subcategories of both (L,M)-SCS and (L,M)-WICS.

A Hesitant Fuzzy Model for Ranking Maintenance Strategies in Small and Medium-Sized Enterprises

Presently, the topic of ranking is very significant. Most of real world affairs can be solved by using decision-making techniques. Selecting and ranking of maintenance strategies are sometimes complicated. They have no proper structure and also have multi-criteria viewpoint. Hence, proper selecting and ranking could be considered as a multiple-criteria decision-making (MCDM). Consequently, the main goal of this research is selecting the best maintenance strategy by applying factor analysis (FA) technique and also decision-making methods in the hesitant fuzzy domain. In this study firstly by applying literature and experts' viewpoints on maintenance, the variables and their sub-variables were identified. Then, to reduce the number of variables, the factor analysis method was used. Additionally, to calculate the weights of sub-variables De Luca-Termini, which is a normalised hesitant entropy method, was applied. Finally, the strategies were ranked by using hesitant fuzzy VIKOR method. According to this research, the maintenance strategy based on reliable has higher priority in comparison with the others.

Versatility of fuzzy logic in chronic diseases: A review

The review aims at providing current state of evidence in the field of medicine with fuzzy logic for diagnosing diseases. Literature reveals that fuzzy logic has been used effectively in medicine. Different types of methodologies have been applied to diagnose the diseases based on symptoms, historical and clinical data of an individual. Increase in the number of recent applications of medicine with fuzzy-logic is an indication of growing popularity of fuzzy systems. Fuzzy intelligent systems developed during 2007–2018 have been studied to explore various techniques applied for disease prediction. In the traditional approach, a physician is required to diagnose disease based on historical and clinical data but the intelligent system will help physicians as well as individuals to detect disease at any location of the world. The studies of various fuzzy logic systems and classified fuzzy logic applications in the field of diabetes, iris, heart, breast cancer, dental, cholera, brain tumor, liver, asthma, viral, parkinson, lung, kidney, huntington and chest diseases have been included in the review. This study indicates all the benefits of the fuzzy logic to the society and direction to tackle the diseases that still need software for their accurate detection. Further, different case studies for celiac disease have been reported earlier. The current review aims at exploring the future direction for fuzzy methodologies and domain on celiac disease.

Dynamic Fuzzy Ship Domain Considering the Factors of Own Ship and Other Ships

In this paper, we propose a novel dynamic fuzzy ship domain that considers factors associated with both one's own ship and other ships. This is in contrast to existing ship domain models that operate from the perspective of one's own ship, considering only the factors relevant to that ship. First, the domain was determined by considering the distance of the ships around one's own ship in different directions, which sufficiently accounts for factors associated with one's own ship and with the other ships. At the same time, the factors were chosen based on an analysis of their importance. Second, the domain was dynamic and modelled by establishing the relationship between the domain size and the chosen factors in different directions from one's own ship, obtained by using neural networking and wavelet decomposition. Third, the domain was developed using fuzzy sets related to different safety levels and this related the model to the practical applications of estimating spatial collision risk. The model was calibrated using Automatic Identification System (AIS) data of vessel movements in the Bohai Sea and the northern Yellow Sea. The reasonableness and the superiority of establishing a ship domain model considering the factors affecting both one's own ship and other ships were analysed, and the results show that the new model can determine the spatial collision risk of the navigational situation and is thus suitable for use as part of a ship collision avoidance system.

Confidence soft sets and applications in supplier selection

Abstract The evaluation of the alternatives against multiple criteria is of the utmost importance in a multi-criteria decision making (MCDM) problem. It is often the case that the experts have a varying degree of confidence in their evaluations. That is, an evaluation has an associated degree of credibility. To take into account this crucial piece of information in determining the best choice, we present a new data structure. More specifically, we present a confidence-based soft set. We also extend the same to the fuzzy and intuitionistic fuzzy domains. The proposed concepts are elucidated through a number of illustrative examples. We establish their usefulness in a real case-study.

Laplacian mixture modeling for network analysis and unsupervised learning on graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.