Membership Values Research Articles

Prediction of metabolic status of dairy cows in early lactation using milk fatty acids and test-day variables

Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with or without milk fatty acids (FA) content to classify metabolically imbalanced cows and (2) to evaluate the robustness of the metabolic classification with external data. A data set was compiled from 3 experiments containing plasma β-hydroxybutyrate, nonesterified FA, glucose, insulin-like growth factor-I, FA proportions in milk fat, and TD variables collected from 244 lactations in wk 2 after calving. Based on the plasma metabolites, 3 metabolic clusters were identified using fuzzy c-means clustering and the probabilistic membership value of each cow to the 3 clusters was determined. Comparing the mean concentration of the plasma metabolites, the clusters were differentiated into metabolically imbalanced, moderately impacted, and balanced. Following this, the 2 metabolic status groups identified were imbalanced cows (n = 42), which were separated from what we refer to as "others" (n = 202) based on the membership value of each cow for the imbalanced cluster using a threshold of 0.5. The following 2 FA data sets were composed: (1) FA (groups) having high prediction accuracy by Fourier-transform infrared spectroscopy and, thus, have practical significance, and (2) FA (groups) formerly identified as associated with metabolic changes in early lactation. Metabolic status prediction models were built using FA alone or combined with TD variables as predictors of metabolic groups. Comparison was made among models and external evaluations were performed using an independent data set of 115 lactations. The area under the receiver operating characteristics curve of the models was between 75 and 91%, indicating their moderate to high accuracy as a diagnostic test for metabolic imbalance. The addition of FA groups to the TD models enhanced the accuracy of the models. Models with FA and TD variables showed high sensitivities (80-88%). Specificities of these models (73-79%) were also moderate and acceptable. The accuracy of the FA models on the external data set was high (area under the receiver operating characteristics curve between 76 and 84). The persistently good performance of models with Fourier-transform infrared spectroscopy-quantifiable FA on the external data set showed their robustness and potential for routine screening of metabolically imbalanced cows in early lactation.

Solicit Irresistible Membership Value Statements

The Membership Management ReportVolume 19, Issue 6 p. 4-4 Member Recruitment Tips Solicit Irresistible Membership Value Statements First published: 05 May 2023 https://doi.org/10.1002/mmr.32133Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume19, Issue6June 2023Pages 4-4 RelatedInformation

Sistem Pendukung Keputusan Untuk Pengendalian Bad Stock (BS) Menggunakan Fuzzy Tsukamoto

In Bad Stock (BS) control at PT Sari Roti Semarang, it is crucial to reduce the company's significant losses. However, the selection of rejected and returned bread with poor quality is still prevalent. Based on production data and the amount of bad stock (a collection of returned bread from stores and rejected production), a decision support system for controlling bad stock using Fuzzy Tsukamoto is needed. In this decision support system, three variables are modeled: production, store returns, and production rejects. The production variable consists of two fuzzy sets: increase, remain the same, and decrease. The store returns variable consists of two fuzzy sets: decrease, remain the same, and increase. The production rejects variable consists of three fuzzy sets: few, normal, and many. The membership values are only two values, 0 and 1, while the membership values in the fuzzy sets are closed intervals [0,1]. By combining all these fuzzy sets, three fuzzy rules are obtained, which are then used in the inference stage. In the inference stage, the membership value of the antecedent (α) and the estimated amount of production (z) are found for each rule. The amount of rejected production (Z) is obtained by centered average defuzzification.
 The research results based on expert validation testing indicate that the obtained values are in the range of 2.51-3.25, which is categorized as valid or good. Meanwhile, the validation testing from users indicates that the obtained value of 3.4 is in the range of 3.26-4.00, which is categorized as highly valid. The effectiveness testing of the system's performance results in an average value of 89.5%.

Development of type-2 Pythagorean fuzzy set with its application to sustainable transport system selection

In resolving multicriteria decision-making (MCDM) problems, it is observed that the solution process is frequently affected by the available vague information together with intra-personal uncertainties associated with expert’s opinion. In most of the time fuzzy set or its existing variants fail to capture such uncertainties for making reasonable balance in decision making processes. To reduce the effects of uncertainties, this paper introduces a new variant of fuzzy set, viz., type-2 Pythagorean fuzzy set (T2PFS). The main advantage of using T2PFS in MCDM is that the membership and non-membership values of each argument are presented by Pythagorean fuzzy numbers rather than using fuzzy number or intuitionistic fuzzy number. In model formulation process, at first, T2PFS is explicitly formulated and some basic operations are defined. Subsequently, four aggregation operators are constructed to aggregate the arguments, and their properties are verified. Afterwards, two information measures and a score function are defined. Moreover, two MCDM algorithms based on the developed aggregation operators and TOPSIS are framed. To show the application potentiality of the proposed method, a case study relating to the selection of sustainable transport system in India is considered and solved. In the case study five different fuel-based vehicles, mostly used in India in recent times, are evaluated by the experts as transport alternatives with the consideration of thirteen sustainability related criteria. From the achieved result it is found that in future electric cars would become the most sustainable vehicle in India. Sensitivity analysis of the developed models and comparative study with existing fuzzy and non-fuzzy MCDM methods establish effectiveness of the developed models. This study may facilitate transport authority to select appropriate transport system in connection with sustainable development of a region.

A new fuzzy support vector machine with pinball loss

The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.

Graph based fuzzy clustering algorithm for crime report labelling

Globalization influences the frequency and severity of localized traditional patterns of crime in addition to creating conditions for new types of crime. The scope and makeup of traditional crime have been significantly impacted by the world’s compression. This makes it impossible for law enforcement officials to examine crime reports for the purpose of conducting investigations and taking the necessary preventive measures. Clustering facilitates investigations by grouping crime reports into different categories based on the crime types. In the proposed work, the crime reports are preprocessed and embedded with the help of the BERT model to capture the contextual meaning of the reports. An undirected graph is constructed considering each crime report vector as a node where an edge exists between a pair of nodes only if the cosine similarity between them is more than a threshold value. The constructed graph is partitioned based on the concepts of node betweenness, edge connectivity, and two proposed concepts, namely, cut of a node, and safe node, introduced in the paper. Based on the edge connectivity of the graph, we decide whether the graph needs to be further partitioned or not. If edge connectivity is high, we consider the graph as a cluster; otherwise, we partition the graph with the help of a node, say v, with the highest node betweenness. During partition, we find the cut of v to determine whether v is safe or not. If v is safe, the algorithm provides two overlapping subgraphs of the graph; on the other hand, if it is not safe, then the algorithm provides a modified graph with a replica of node v. The algorithm is iterative in nature and terminates when all subgraphs are of higher edge connectivity. As the generated subgraphs are overlapped in nature, a novel graph theory-based fuzzification technique is utilized to measure the membership value of each node in different subgraphs, which allows investigators to focus on the most serious crimes in the reports. The proposed method is assessed using crime report datasets as well as other text datasets and compared with several state-of-the-art methods by utilizing a variety of performance metrics to reflect the effectiveness of the method in different domains.

Show Membership Value Through Dynamic Video

The Membership Management ReportVolume 19, Issue 5 p. 6-6 Share the Excitement With Probable Members Show Membership Value Through Dynamic Video First published: 03 April 2023 https://doi.org/10.1002/mmr.32121Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume19, Issue5May 2023Pages 6-6 RelatedInformation

Emergency response scheme selection with T-spherical hesitant probabilistic fuzzy TODIM-TPZSG approach

This paper aims to investigate an innovative framework to handle emergency response scheme selection (ERSS) issues by integrating TODIM and TPZSG (two-person zero-sum game) methods under novel T-spherical hesitant probabilistic fuzzy set (T-SHPFS) environments. First, T-SHPFS is defined as an extension of the existing tools, which can depict the complex assessment information including several possible values of the various membership functions’ degrees and the associated statistical uncertainty information. Concomitantly, T-SHPFS’s normalization method, comparison laws, operation rules, cross-entropy measure and Hausdorff distance are explored. Then, an objective attribute weight determining model is constructed, considering the credibility of T-SHPF evaluations and the divergence degrees between attribute assessments simultaneously. Next, an integrated TODIM-TPZSG decision-making approach is developed to select the most desirable emergency response scheme. Finally, an illustrative example concerning the selection of the best medical waste disposal method during the COVID-19 epidemic is conducted to verify the effectiveness of the proposed TODIM-TPZSG method. Sensitivity analysis and comparisons between the TODIM-TPZSG and other representative methods are also provided to demonstrate the superiorities of the proposed method. The results reveal that the developed T-SHPFSs give DMs more assessment freedom; the proposed TODIM-TPZSG approach considers the decision makers’ psychological behaviors; the ranking results of the proposed method can reflect the specific divergence degrees among the alternatives; and the needed computation burden and computational complexity are low and less affected by the number of alternatives and criteria than most current ERSS methods.

A Mixed Clustering Approach for Real-Time Anomaly Detection

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods either follow a partitioning or a hierarchical approach without addressing time attributes of the dataset distinctly. In this article, a mixed clustering approach is introduced for this purpose, which also takes time attributes into consideration. It is a two-phase method that first follows a partitioning approach, then an agglomerative hierarchical approach. The dataset can have mixed attributes. In phase one, a unified metric is used that is defined based on mixed attributes. The same metric is also used for merging similar clusters in phase two. Tracking of the time stamp associated with each data instance is conducted simultaneously, producing clusters with different lifetimes in phase one. Then, in phase two, the similar clusters are merged along with their lifetimes. While merging the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are merged using superimposition operation, producing a fuzzy time interval. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging to sparse clusters, not belonging to any of the clusters or falling in the fuzzy lifetimes with low membership values can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies. The experimental results with a real world dataset and a synthetic dataset show that the proposed algorithm can detect the anomalies with 90% and 98% accuracy, respectively.

Measures of embedding for interval-valued fuzzy sets

Interval-valued fuzzy sets are a generalization of classical fuzzy sets where the membership values are intervals. The epistemic interpretation of interval-valued fuzzy sets assumes that there is one real-valued membership degree of an element within the membership interval of possible membership degrees. Considering this epistemic interpretation, we propose a new measure, called IV-embedding, to compare the precision of two interval-valued fuzzy sets. An axiomatic definition for this concept as well as a construction method are provided. The construction method is based on aggregation operators and the concept of interval embedding, which is also introduced and deeply studied.

Enhancing Contrast of Dark Satellite Images Based on Fuzzy Semi-Supervised Clustering and an Enhancement Operator

Contrast enhancement of images is a crucial topic in image processing that improves the quality of images. The methods of image enhancement are classified into three types, including the histogram method, the fuzzy logic method, and the optimal method. Studies on image enhancement are often based on the rules: if it is bright, then it is brighter; if it is dark, then it is darker, using a global approach. Thus, it is hard to enhance objects in all dark and light areas, as in satellite images. This study presents a novel algorithm for improving satellite images, called remote sensing image enhancement based on cluster enhancement (RSIECE). First, the input image is clustered by the algorithm of fuzzy semi-supervised clustering. Then, the upper bound and lower bound are estimated according to the cluster. Next, a sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels because after clustering, pixels belong to clusters with the corresponding membership values. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. The test results demonstrate that the suggested algorithm is superior to several recently developed approaches.

Credit risk evaluation using clustering based fuzzy classification method

Credit scoring is a crucial indicator for banks to determine the financial position and the eligibility of a client for credit. In order to assign statistical odds or probabilities to predict the risk of nonpayment in relation to many other factors, the scoring criterion becomes an important issue. The focus of this study is to propose a clustering based fuzzy classification (CBFC) method for credit risk assessment. We aim to illustrate the beneficial use of machine learning (ML) methods whose prediction power is increased by adopting fuzzy theory to calculate the default risk with a better selection of the features contributing to it. An important feature of the CBFC method is that membership values obtained as a result of the fuzzy k-means clustering algorithm are used for the purpose of better capturing the structure of an existing system.An extensive comparison is performed to show how CBFC performs compared to the traditional ones for the datasets having different characteristics in terms of the variable types. Five different real-life datasets are studied to expose the contribution of fuzzy approach on improving the ML use. Our findings show that the proposed CBFC models can produce the promising classification results in credit risk evaluation which aid the practitioners and decision makers for issuance of credit purposes.

Product selection by consumers utilizing the membership values

Assessment and assortment of the most favorable, competent, and trustworthy products is getting to be more and more important for businesses in the current scenario, so the majority of the production industries do their utmost to achieve the requirements, standards, and expenditure patterns of the consumer to attain their expectations. Approaches are made to fulfill the needs of the consumer, as shown by the creation of diverse technologies for improving the capability to manage complex optimizations regarding decision attributes that involve uncertainty. The procedure of detecting the best alternative from all the feasible options is the basis of the decision-making problem. This article recommended two approaches: (a) multi-objective decision making by utilizing fuzzy technique and (b) fuzzy TOPSIS (Technique for Ordering Preferences by Similarity to Ideal Solution) method for choosing the best product while many products with many attributes are present in the marketplace. As per the need of the customer priority and their satisfaction level, this paper used linguistic quantifiers to evaluate the weight of the objective that is represented in triangular or trapezoidal fuzzy numbers. To demonstrate the technique used for the suggested approaches a case study was conducted. The simulation result shows a specific path that fuzzy logic can evolve in the problem of decision making as well as in the planning process in the product selection practice. Solutions obtained by employing both techniques were compared, which urges that the fuzzy TOPSIS approach provides more accurate information than the fuzzy MODM (multi-objective decision making) approach to find the best product from among many available products. This work helps to assist the managers to adopt suitable techniques while planning to design the product according to the customer requirements so that the consumer can get the best product as per their objectives.

Multi‐sensor data fusion for swine house environment evaluation and control

AbstractData fusion from various sensors can obtain more accurate and reliable results than a single monitoring sensor. The data of air quality in the swine breeding environment comes from various sensors; therefore, the data must be fused. In this study, statistical criteria, batch estimation theory, a fuzzy comprehensive evaluation (FCE) method, as well as an improved analytic hierarchy process (IAHP), were combined to provide accurate data for multi‐sensor data fusion modeling. To make a fair comparison, four statistical criteria were employed to process abnormal data gross errors. Besides, an improved batch estimation adaptive weighted fusion method was adopted to, respectively, integrate the data from temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) of air quality at the data level. The predetermined weights were given using IAHP combined with the knowledge of experts. To decide the fuzzy membership values of each environmental parameter, we presented a method to establish fuzzy membership functions and devised a calculation method for related parameters. The gaussmf type membership function was employed to build a global fusion model that fits well with IAHP–FCE. The IAHP–FCE method was exploited to comprehensively integrate multi‐sensor data at the decision level. Experimental results show that the presented method can improve the data accuracy and provide decision results and scientific basis for the precision control of swine breeding environment.

Application of Fuzzy Membership Value Matrix

Fuzzy set theory plays a dynamic role in medicinal fields. There are varieties of simulations involving fuzzy matrices to deal with different complicated aspects of medical diagnosis. Today may be a world of uncertainty with its associated problems, which may be well handled by fuzzy soft set theory. Sanchez formulated the diagnostic models including intuitionistic fuzzy matrices representing the medical knowledge between the symptoms and diseases. In this paper, an algorithm is developed and a few examples are proposed to construct the decision method for Diagnosis.

Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired People

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%.

Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis

Deep learning can be applied in the field of fault diagnosis without an accurate mechanism model. However, the accurate diagnosis of minor faults using deep learning is limited by the training sample size. In the case that only a small number of noise-polluted samples is available, it is crucial to design a new learning mechanism for the training of deep neural networks to make it more powerful in feature representation. The new learning mechanism for deep neural networks model is accomplished by designing a new loss function such that accurate feature representation driven by consistency of trend features and accurate fault classification driven by consistency of fault direction both can be secured. In such a way, a more robust and more reliable fault diagnosis model using deep neural networks can be established to effectively discriminate those faults with equal or similar membership values of fault classifiers, which is unavailable for traditional methods. Validation for gearbox fault diagnosis shows that 100 training samples polluted with strong noise are adequate for the proposed method to successfully train deep neural networks to achieve satisfactory fault diagnosis accuracy, while more than 1500 training samples are required for traditional methods to achieve comparative fault diagnosis accuracy.

Automated Clustering Technique (ACT) for Early Onset Scoliosis: A preliminary report

PurposeWhile the C-EOS system helps organize and classify Early Onset Scoliosis (EOS) pathology, it is not data-driven and does not help achieve consensus for surgical treatment. The current study aims to create an automated method to cluster EOS patients based on pre-operative clinical indices.MethodsA total of 1114 EOS patients were used for the study, with the following distribution by etiology: congenital (240), idiopathic (217), neuromuscular (417), syndromic (240). Pre-operative clinical indices used for clustering were age, major curve (Cobb) angle, kyphosis, number of levels involved in a major curve (Cobb angle) and kyphosis along with deformity index (defined as the ratio of major Cobb angle and kyphosis). Fuzzy C-means clustering was performed for each etiology individually, with one-way ANOVA performed to assess statistical significance (p < 0.05).ResultsThe automated clustering method resulted in three clusters per etiology as the optimal number based on the highest average membership values. Statistical analyses showed that the clusters were significantly different for all the clinical indices within and between etiologies. Link to the ACT-EOS web application: https://biomed.drexel.edu/labs/obl/toolkits/act-eos-application.ConclusionAn automated method to cluster EOS patients based on pre-operative clinical indices was developed identifying three unique, data-driven subgroups for each C-EOS etiology category. Adoption of such an automated clustering framework can help improve the standardization of clinical decision-making for EOS.

A novel high order hesitant fuzzy time series forecasting by using mean aggregated membership value with support vector machine

Over the last three decades, several researchers have been putting their efforts into developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set. However, considering a set of membership values to each element of the time series, the hesitant fuzzy set glorifies the chances to capture the fuzziness and uncertainty due to randomness better than the traditional fuzzy set. Motivated by this, the present study proposes a novel hesitant FTS forecasting model employing a support vector machine (HFTSF-SVM). In this model, to diminish the computational complexity and increase the forecasting accuracy, we have addressed three key issues by proposing three new methods such as (a) a length-based discretization technique is developed to compute the number of intervals, (b) two sets of universe of discourse are determined for the same time series to compute the non-membership and membership value for each observation (c) each observation along with its mean aggregated membership values are adopted to construct the fuzzy logical relationships (FLR). To assess the competency and reliability of the proposed HFTSF-SVM model, six promising FTS models from the recent literature and four promising time series forecasting (TSF) models are considered for comparison using sixteen time series datasets. Extensive statistical analyses of obtained results confirm the supremacy of the proposed HFTSF-SVM model.

Robust Control of SEDCM by Fuzzy-PSO

Industries have many rotational operations that are used for design, transport, lift, drilling, rolling, robotics, and many other applications. These rotating applications require a proper controller for accurate control of the operation. Separately excited DC motors (SEDCMs) are versatile and have various industrial operations because of their specific speed control characteristics. So, for smooth and accurate operation of an SEDC motor, controllers should be used. PI and PID controllers are used in many cases, but they are ineffective for nonlinear load operation. A fuzzy controller is a heuristic controller and can provide automatic control of the operation. Its operation depends on the selection of the correct membership values. This work proposes a novel particle swarm optimization (PSO) technique that would provide the optimum value of the membership for fuzzy controllers for optimum control of the industrial processes. To obtain SEDC results, MATLAB simulation was performed, and the fuzzy controller with novel PSO was implemented. A fuzzy PSO controller used for motor speed control operation obtains a rise time of 0.00026 s, settling time of 0.000214 s, maximum overshoot of zero, and delay time of 0.016 s, which are the best values when compared to PID and PID-Fuzzy controllers. It is observed that the results obtained from the separately excited DC motor using a fuzzy PSO controller improve the dynamic behavior of the motor that so it smoothly tracks the required speed without any more overshoot or oscillation than the PID controller. Such dynamic, stable operation of the motor makes it perfect for industrial as well as household operations.