Fuzzy Labels Research Articles

Elegant Fuzzy Labeling of Graphs

Fuzzy graphs and fuzzy labelings are crucial in modeling real-time systems by accommodating varying degrees of information precision. In this paper, we introduce a novel fuzzy labeling called Elegant Fuzzy Labeling. We prove that a simple graph admits Elegant Fuzzy Labeling if and only if it admits elegant labeling. Furthermore, we prove that while a simple graph that admits Elegant Fuzzy Labeling also admits fuzzy labeling, but not conversely. We identify specific classes of simple graphs that admit Elegant Fuzzy Labeling and explore practical application of this new labeling approach.

To Analyse Energy of the Electric Circuit by Using Neutrosophic Labelling

Objectives: Neutrosophic labeling was used in electric circuits for evaluating energy. Methods: Defuzzification method, was applied to the neutrosophic labeling circuit graph which produces crisp values. Findings: The energy of the electric circuit is equal to the energy of the neutrosophic applied electric circuit. Novelty : Neutrosophic Fuzzy Labeling values are converted into crisp values using Defuzzification Graded Mean Integration Method. Keywords: Labeling, Neutrosophic Labeling, Electric circuit, Kirchhoff law, Energy

Feature selection for multilabel classification with missing labels via multi-scale fusion fuzzy uncertainty measures

Numerous high-dimension multilabel data are generated, posing a challenge for multilabel learning. Building effective learning models with discriminative features is essential to improve the performance of multilabel learning. Multilabel feature selection can filter out the discriminative features according to their contribution to classification. However, ambiguity, uncertainty, and missing labels coexist in real-life multilabel data, which brings adverse effects to multilabel feature selection. The multi-scale fuzzy rough set gives an effective way to mine intrinsic knowledge hidden in uncertain data. This paper first extends the multi-scale learning to multilabel data with missing labels and proposes a feature selection method for multilabel classification with missing labels via multi-scale fusion fuzzy uncertainty measures called FSMML. The missing label space construction and feature evaluation metric are carefully investigated in the framework of multi-scale learning. A multilabel multi-scale learning strategy is formalized with the fuzzy granularity cognitive mechanism as the core, and the multi-scale fusion fuzzy label learning is given to reconstruct the missing label space. Then, a novel multilabel multi-scale fuzzy rough sets with missing labels is developed, and the significance of each scale is quantified. Moreover, some multi-scale fusion fuzzy uncertainty measures are defined by capturing the sample fuzzy similarity in the feature and reconstructed label spaces. Accordingly, the relevance between features and label set and the interactivity and redundancy between features in feature evaluation are discussed. Finally, FSMML chooses high-quality features to maximize relevance and interactivity and minimize redundancy. Extensive experiments demonstrate the effectiveness of FSMML on fifteen datasets with missing labels.

Sound classification with time-frequency features in forest environment

The study of forest sound classification has drawn more attention recently due to its potential for illegal activities and natural disaster monitoring. Based on the forest sound classification dataset (FSC22), a dataset specific to possible sound existing in the forest, five classification methods are utilized to investigate the relationship between recognition accuracy and the number of sound acoustic features, as well as the number of target classes. The results confirmed that extreme random forest is the best method for forest sound classification, with an accuracy of around 70% when the target class number is above 20. Further, Mel-frequency cepstral coefficients are the critical feature for sound classification, while fuzzy labels in the dataset may reduce the success rate of recognition.

An effective object detector via diffused graphic large selective kernel with one-to-few labelling strategy for small-scaled crop diseases detection

Amid the swift evolution of advanced technologies such as autonomous robotics, edge computing, and the Internet of Things, Intelligent Agriculture Management Systems (IAMS) have become increasingly pivotal in contemporary smart agriculture. One vital task, crop disease monitoring, significantly contributes to ensuring agricultural production quality and food supply security. However, IAMS encounters a particular challenge: due to the limited computational capacity of agricultural equipment and the scarcity of available training datasets, general detection models struggle to achieve anticipated performance in agricultural environments, especially when dealing with small-scaled targets. In this paper, we propose a diffusion learning detector named CropDetdiff, designed for disease detection in agricultural environments where training samples are limited. We have developed a backbone network based on dynamic convolutional kernels, which also embeds a graph attention network in its final stage, enhancing feature extraction capabilities for small targets. Additionally, we employ a fuzzy label assignment strategy to balance positive and negative samples and devise a new loss function, HIoU, to supervise the detection of small targets. A series of experiments on two crop disease datasets validate that CropDetDiff achieves an optimal balance between detection performance and operational efficiency. Specifically, the proposed model has achieved a mean Average Precision (mAP) of 73.2% and 40.1% on the two datasets, improving by at least 3.7% and 2.9% over other detectors, respectively, and there is a significant improvement in small targets particularly, thus achieving optimal performance. Additionally, the proposed model exhibits commendable performance in terms of parameter quantity and execution velocity. These results substantiate the efficacy and stability of CropDetDiff, underscoring its suitability for real-world applications within IAMS.

EEG-based depression recognition using feature selection method with fuzzy label

Depression diagnosis is easily affected by subjective consciousness.It is of great significance to study objective and accurate identification methods. Electroencephalogram (EEG) can reflect brain activity and working state. Therefore, this paper aims to explore features with significant differences based on brain functional connectivity to improve the accuracy of depression recognition. We propose a Functional Connection Feature Selection based on Fuzzy Label (FLFCFS), it calculates the correlation between electrode pairs through the phase lag index (PLI), constructing a functional connection matrix. The cluster center is initialized with the same number as the actual category, and the local distance from the sample to the cluster center is calculated to determine its membership degree, serving as the fuzzy label. And a sparse regression model is employed to select the most related features associated with the fuzzy label. Finally, the top ranked feature subset is selected and input into support vector machine (SVM) for depression recognition. The experimental results show that FLFCFS effectively improves the recognition accuracy, reaching 92.59%, and obtains the highest classification performance. Our method makes full use of the semantic information implied in category markers, it effectively guides feature selection to obtain discriminant feature subsets, enhancing the accuracy of depression recognition.

On fuzzy labeling of cycle with k-parallel P_s and C_s chords

Fuzzy Labeling is one of the emerging areas in fuzzy graph theory. In this article, fuzzy labeling of cycle with chord related graphs like cycle with parallel chord, cycle with -parallel chord, cycle with -parallel chord are investigated.

A Modified Fuzzy Labeling Graph using Geometric Mean and Harmonic mean and its Application

A Modified Fuzzy Labeling Graph using Geometric Mean and Harmonic mean and its Application

HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT.

Learning from fuzzy labels: Theoretical issues and algorithmic solutions

In this article we study the problem of learning from fuzzy labels (LFL), a form of weakly supervised learning in which the supervision target is not precisely specified but is instead given in the form of possibility distributions, that express the imprecise knowledge of the annotating agent. While several approaches for LFL have been proposed in the literature, including generalized risk minimization (GRM), instance-based methods and pseudo label-based learning, both their theoretical properties and their empirical performance have scarcely been studied. We address this gap by: first, presenting a review of the previous results relative to the sample complexity and generalization bounds for GRM and instance-based methods; second, studying both their computational complexity, by proving in particular the impossibility of efficiently solving LFL using GRM, as well as impossibility theorems. We then propose a novel pseudo label-based learning method, called Random Resampling-based Learning (RRL), which directly draws from ensemble learning and possibility theory and study its learning- and complexity-theoretic properties, showing that it achieves guarantees similar to those for GRM while being computationally efficient. Finally, we study the empirical performance of several state-of-the-art LFL algorithms on wide set of synthetic and real-world benchmark datasets, by which we confirm the effectiveness of the proposed RRL method. Additionally, we describe directions for future research, and highlight opportunities for further interaction between machine learning and uncertainty representation theories.

Contrastive Learning for Prediction of Alzheimer's Disease Using Brain 18F-FDG PET.

Brain 18F-FDG PET images are commonly-known materials for effectively predicting Alzheimer's disease (AD). How-ever, the data volume of PET is usually insufficient, which is unfavorable to train an accurate AD prediction networks. Fur-thermore, the PET image is noisy with low signal-to-noise ratio, and simultaneously the feature (metabolic abnormality) used for predicting AD in PET image is not always obvious. Such charac-teristics of 18F-FDG PET images hinder the existing deep learning networks to learn the feature of lesion (i.e., glucose metabolism abnormality) effectively, which leads to unsatisfactory classifica-tion performance and poor robustness. In this paper, a contrastive-based learning method is proposed to address the challenges of PET image inherently possessed. Firstly, the slices of 3D PET image are amplified by cropping the image of anchors (i.e., an augmented version of the same image) to generate extended train-ing data. Meanwhile, contrastive loss is adopted to enlarge inter-class feature distances and reduce intra-class feature differences using subject fuzzy labels as supervised information. Secondly, we construct a double convolutional hybrid attention module to enhance the network to learn different perceptual domains where two convolutional layers with different convolutional kernels (7 × 7 and 5 × 5) are constructed. Moreover, we recommend a diagnosis mechanism by analyzing the consistency of predicted result for PET slices alone with clinical neuropsychological assessment to achieve a better AD diagnosis. The experimental results show that the proposed method outperforms the state-of-the-arts for brain 18F-FDG PET images while remaining satisfactory computational performance, and hence demonstrate the advantage of the method in effectively predicting AD.

Heterogeneous data-driven measurement method for FeO content of sinter based on deep learning and tensor decomposition

FeO content is an important index reflecting the reducibility and drum strength of sinter, which is of great significance to the subsequent iron-making process. However, due to the time delay of chemical analysis method, FeO content cannot be obtained in real-time, which promotes the research on online measurement methods. Based on deep learning and information fusion, this paper proposes an intelligent heterogeneous data-driven method to accurately measure the level of FeO content in real time. Firstly, in view of the interference information such as background and furnace contour, the region proposal network is adopted to detect the burning ore region in the cross-section image of sintering machine. Secondly, a split attention-based deep convolutional neural network is constructed to extract deep image features. In particular, a fuzzy labeling strategy is proposed aiming at the problem of dividing images at the junction of different levels. In the end, the image features and the sintering process variables are fused through a bilinear model, and tensor decomposition is introduced to obtain the final fusion feature. Experimental results on actual industrial data demonstrate the effectiveness of the proposed method for measuring the level of FeO content, achieving an excellent accuracy of 98.35%.

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

Construction of Operation Portraits Based on a Cloud Model for Power Distribution Networks

With the high proportion of new energy access in power distribution networks, the operation of distribution networks becomes more uncertain and complex. The mechanism on how to extract features that embody the full perspective of the distribution network from the diverse and varying conditions is of great significance to master the operating property of distribution networks under the high proportion of new energy. This study investigates the portrait construction of power distributions. A data-driven method of portrait construction for a distribution network based on a cloud model is proposed. First, the indexes describing the operation of the medium voltage distribution network (MVDN) and low-voltage station area (LVSA) are established. By means of statistics, fuzzy label templates are provided for the indexes. Then, based on the daily monitoring power grid data, the operation index is calculated to form the collected samples, and the reverse cloud generator is used as the data-driven method to process the samples, and the fuzzy model of cloud parameter representation is obtained. Finally, the fuzzy distance between the obtained fuzzy model and the label template is calculated, and the scores of operation indexes are obtained, thus forming a portrait method of distribution network operation behavior. Taking the IEEE 33 distribution network and real distribution network as examples and taking the LVSA as an example, it is verified that this method can effectively construct the distribution network operation portrait.

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.

Privacy and Trust in eHealth: A Fuzzy Linguistic Solution for Calculating the Merit of Service.

The use of eHealth and healthcare services are becoming increasingly common across networks and ecosystems. Identifying the quality and health impact of these services is a big problem that in many cases it is difficult determine. Health ecosystems are seldom designed with privacy and trust in mind, and the service user has almost no way of knowing how much trust to place in the service provider and other stakeholders using his or her personal health information (PHI). In addition, the service user cannot rely on privacy laws, and the ecosystem is not a trustworthy system. This demonstrates that, in real life, the user does not have significant privacy. Therefore, before starting to use eHealth services and subsequently disclosing personal health information (PHI), the user would benefit from tools to measure the level of privacy and trust the ecosystem can offer. For this purpose, the authors developed a solution that enables the service user to calculate a Merit of Service (Fuzzy attractiveness rating (FAR)) for the service provider and for the network where PHI is processed. A conceptual model for an eHealth ecosystem was developed. With the help of heuristic methods and system and literature analysis, a novel proposal to identify trust and privacy attributes focused on eHealth was developed. The FAR value is a combination of the service network’s privacy and trust features, and the expected health impact of the service. The computational Fuzzy linguistic method was used to calculate the FAR. For user friendliness, the Fuzzy value of Merit was transformed into a linguistic Fuzzy label. Finally, an illustrative example of FAR calculation is presented.

New concepts in intuitionistic fuzzy labelling graphs

In this paper, the concept of intuitionistic fuzzy labelling (IFL) has been introduced. Intuitionistic fuzzy bi-magic labelling (IFBL) and intuitionistic fuzzy anti-magic labelling (IFAL) have been discussed for cycle, star and path graph. Intuitionistic fuzzy sets will be given both degree of membership and degree of non-membership, which are independent of each other to the extent, that their sum is not greater than one. Various types of arcs have been analysed and blank arc has been introduced and also discussed for intuitionistic fuzzy labelling. We also discussed about intuitionistic fuzzy bridge and intuitionistic fuzzy cut-vertices with examples and their properties are studied.

New concepts in intuitionistic fuzzy labelling graphs

In this paper, the concept of intuitionistic fuzzy labelling (IFL) has been introduced. Intuitionistic fuzzy bi-magic labelling (IFBL) and intuitionistic fuzzy anti-magic labelling (IFAL) have been discussed for cycle, star and path graph. Intuitionistic fuzzy sets will be given both degree of membership and degree of non-membership, which are independent of each other to the extent, that their sum is not greater than one. Various types of arcs have been analysed and blank arc has been introduced and also discussed for intuitionistic fuzzy labelling. We also discussed about intuitionistic fuzzy bridge and intuitionistic fuzzy cut-vertices with examples and their properties are studied.

An efficient algorithm of fuzzy reinstatement labelling

<abstract><p>The fuzzy reinstatement labelling ($ FRL $) puts forward a reasonable method to rewind the acceptable degrees of arguments in fuzzy argumentation frameworks. The fuzzy labelling algorithm ($ FLAlg $) computes the $ FRL $ by infinitely approximating the limits of an iteration sequence. However, the $ FLAlg $ is unable to provide an exact $ FRL $, and its computation complexity depends on not only the number of arguments but also the accuracy. This brings a quick increase in complexity when higher accuracy is acquired. In this paper, through the in-depth study of the $ FLAlg $, we introduce an effective algorithm for decomposing $ FRL $ by strongly connected components. For simple fuzzy frameworks in the form of trees, odd cycles, and even cycles, the new algorithm provides an exact value of the limit. Therefore, by avoiding the infinite approximation process, it is independent of accuracy. And for complex frames, the new algorithm outputs an approximate value to the $ FLAlg $. It is more efficient because the number of arguments in the approximation process is usually reduced.</p></abstract>

Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy.

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.