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Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Russian, and Chinese as examples and focuses on identifying the common quantitative features of fake news in different languages at the word, sentence, readability, and sentiment levels. These features are then utilized in principal component analysis, K-means clustering, hierarchical clustering, and two-step clustering experiments, which achieved satisfactory results. The common features we proposed play a greater role in achieving automatic cross-lingual clustering than the features proposed in previous studies. Simultaneously, we discovered a trend toward linguistic simplification and economy in fake news. Furthermore, fake news is easier to understand and uses negative emotional expressions in ways that real news does not. Our research provides new reference features for fake news detection tasks and facilitates research into their linguistic characteristics. [ABSTRACT FROM AUTHOR]

The computational intelligence-based digital forecasting for social systems has been a novel tendency. This work takes forecasting of regional economy as the problem scenario, and introduces radial basis function neural network (RBFNN) to deal with this concern. Hence, an RBFNN-based fast forecasting model for regional economy is constructed in this paper. First, the economic flow data are encoded into sequential format, and RBFNN is employed to establish a sequential forecasting model that fits flow data. In addition, a fuzzy clustering method is further utilized to complete missing data in flow sequence, in order to improve the data quality for model calculation. In simulation procedure, the proposal is implemented on real economic operation data for assessment, and mean absolute error (MAE) is introduced to measure the forecasting performance. Several typical regression-based forecasting methods are introduced as the baseline. The experimental results show that MAE of the proposal is about 20%–30% lower than that of baseline methods, showing a better forecasting performance. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions. [ABSTRACT FROM AUTHOR]

Motivation Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. Results This study investigates a semisupervised clustering model called scTPC, which integrates the t riplet constraint, p airwise constraint, and c ross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. Availability and implementation scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780. [ABSTRACT FROM AUTHOR]

The friction stir welding (FSW) method was used to weld B4C reinforced AA 5083 metal matrix composites in this study. By coating titanium nitride (TiN), aluminium chromium nitride (AlCrN), and diamond-like carbon (DLC) to a thickness of 4 microns, three FSW tools with square pin profiles were developed and the friction coefficients of 0.69, 0.32, and 0.2 were maintained. At three levels, the process factors such as tool rotating speed, transverse feed, and axial force were examined. For each tool, 15 samples were made using the central composite design. The influence of the friction coefficient on ultimate tensile strength, microstructural features, and tool condition was studied, and the flower pollination algorithm (FPA) technique was used to find the best process parameters for obtaining maximum ultimate tensile strength of FSW joints. The improved tensile strength of FSW joints was verified using a validation test. The coating has a considerable influence on the ultimate tensile strength, microstructure, and tool condition, according to the results of the tool's friction coefficient. The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective. [ABSTRACT FROM AUTHOR]

The mill vibration signals collected during mill operation are often affected by complex working conditions and various disturbances, thus presenting strong non-stationary characteristics. After the vibration signal is processed by noise reduction, although the noise interference and modal aliasing phenomenon are solved, the extracted vibration signal of the rolling mill still has the problems of strong non-stationarity, high complexity, and great difficulty in analyzing, which largely affects the prediction model construction. To address the above problems, this study proposes a multi-scale signal reconstruction method based on Fuzzy Entropy and Gath-Geva fuzzy clustering algorithm, which effectively reduces the complexity of the original sequence, reduces the number of predictive modellings, and improves the signal predictability and prediction accuracy. The sequence reconstruction of such non-stationary vibration signals based on their internal characteristics has obvious advantages for the problem of mill vibration prediction and analysis. [ABSTRACT FROM AUTHOR]

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring. [ABSTRACT FROM AUTHOR]

Land Use/ Land Cover change detection is an inspiring, interesting task to be performed worldwide. The real-time satellite data of the earth's surface and its different findings can be studied with the assistance of Remote Sensing and Geographic Information Systems. This work builds the novel Fuzzy Swin Transformer-based LU/LC classification model using the LISS-III satellite data of Yelagiri Hills. In the proposed work, the LU/LC features extracted from fuzzy clustering were used as the input training patches when executing the Swin Transformer model. The training patches assist the transformer in finding the LU/LC classification map with good computational complexity and accuracy. The Simple Random, Cluster, Systematic, and Stratified Random sampling methods were used to validate the accuracy of the acquired LU/LC classification map. The proposed Fuzzy Swin Transformer model attains good results with an average classification accuracy of 98.43% using Simple Random Sampling, 97.45% using Stratified Random Sampling, 97.36% using Systematic Sampling, and 96.97% using Cluster Sampling. The LU/LC change detected in this work was considered an important source of information to support the concerned land resource planners in taking necessary action to preserve the land cover, exclusively for the forest-covered areas of the hill stations. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard. Design/methodology/approach: A survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS). Findings: Based on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness. Originality/value: The findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a measure is developed to evaluate the similarity between the extracted PDFs. Finally, an algorithm is established for determining prior probabilities using fuzzy clustering techniques. By combining these improvements, we develop a more efficient algorithm for classifying image data. An image is assigned to a specific group if it has the highest value of prior probability and a similar level to that group. We explain the proposed algorithm step-by-step with a numerical example and clearly demonstrate its convergence. When applied to multiple image datasets, the proposed algorithm has shown stability and efficiency, outperforming many other statistical and machine learning methods. Additionally, we have developed a Matlab procedure to apply the proposed algorithm to real image datasets. These applications demonstrate the potential of research in various fields related to the digital revolution and artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper presents the identification and classification of Indian agricultural crop species using a novel combined local binary histogram pattern of gradient (LBHPG) image feature extraction technique. Initially, a partition of the leaf image background is done through the newly developed fast adaptive fuzzy C-mean clustering (FAFCM) technique. After that, leaf objects within the image are identified using the LBHPG method. For the classification, KNN, PNN, and SVM shallow machine learning classifiers are used for crop species identification. The performance evaluation is done using LBP and HOG individually along with the new proposed LBHPG technique for classification using KNN, PNN, and SVM Classifiers. The performance evaluation is based on six metrics parameters of the confusion matrix, viz., accuracy, sensitivity, specificity, precision, recall, and F-measure. The experimental results show that the proposed novel LBHP feature extraction technique with PNN Classifier gives the highest accuracy of 94.58%. [ABSTRACT FROM AUTHOR]

The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. [ABSTRACT FROM AUTHOR]

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = −5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data. [ABSTRACT FROM AUTHOR]

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset. [ABSTRACT FROM AUTHOR]

Based on forwards recursive input–output data clustering, a recursive least squares (RLS) algorithm is proposed to estimate nonuniformly sampled nonlinear systems. The relationship of linear and nonlinear systems is studied under nonuniform sampling, and a fuzzy model is constructed for the global system. Forwards recursive input–output data clustering based on k-means clustering is used to identify the fuzzy rule number and the antecedent parameters. Based on the membership function, the consequent parameters are identified by an RLS algorithm. A practical application verified the efficiency of the method. [ABSTRACT FROM AUTHOR]

Accurate individual-tree segmentation is essential for precision forestry. In previous studies, the canopy height model-based method was convenient to process, but its performance was limited owing to the loss of 3D information, and point-based methods usually had high computational costs. Although some hybrid methods have been proposed to solve the above problems, most canopy height model-based methods are used to detect subdominant trees in one coarse crown and disregard the over-segmentation and accurate segmentation of the crown boundaries. This study introduces a combined approach, tested for the first time, for treetop detection and tree crown segmentation using UAV–LiDAR data. First, a multiscale adaptive local maximum filter was proposed to detect treetops accurately, and a Dalponte region-growing method was introduced to achieve crown delineation. Then, based on the coarse-crown result, the mean-shift voxelization and supervoxel-weighted fuzzy c-means clustering method were used to identify the constrained region of each tree. Finally, accurate individual-tree point clouds were obtained. The experiment was conducted using a synthetic uncrewed aerial vehicle (UAV)–LiDAR dataset with 21 approximately 30 × 30 m plots and an actual UAV–LiDAR dataset. To evaluate the performance of the proposed method, the accuracy of the remotely sensed biophysical observations and retrieval frameworks was determined using the tree location, tree height, and crown area. The results show that the proposed method was efficient and outperformed other existing methods. [ABSTRACT FROM AUTHOR]

This article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach. [ABSTRACT FROM AUTHOR]

Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM's performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall's W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. [ABSTRACT FROM AUTHOR]

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data's misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. [ABSTRACT FROM AUTHOR]

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher's Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge. Author summary: Explainable models are particularly attractive nowadays since they have the advantage to convince clinicians and patients. In this work we show that the standard workflow used in onco-hematology that leverages on the Hierarchical Dirichlet Mixture Model (HDMM) can benefit from the usage of alternative statistical approaches to better model genomics data. First, HDMMs are typically utilized to cluster the presence or absence of genomic alterations into components. Second, the components are characterized by genomic drivers according to the HDMM outcome, along with to clinical considerations, and eventually patients stratification is performed based on the sole occurrence of the drivers. Here, we present a multinomial-based approach and a Multivariate Fisher's Non-Central Hypergeometric (MFNCH) based approach to tackle components characterization and patients stratification. This work highlights that the MFNCH-based approach performs as good as, if not better, than the multinomial-based approach on simulated data. At the same time the characterization provided by the MFNCH-based approach on real data succeeds to outline all genomic drivers suggested by the standard workflow, which is a result that the rigorous multinomial-based approach fails to accomplish. [ABSTRACT FROM AUTHOR]

Feature extraction is essential in bioinformatics because it transforms genomics sequences into feature vectors, which are needed for clustering to discover the family of newly sequenced genome. Most of the existing feature extraction methods extract similar features for dissimilar sequences, do not extract context-based features and unable to handle millions of genome sequences because they are not scalable. So, to tackle these challenges, we proposed an efficient apache spark-based scalable feature extraction approach that extracts significantly important features from millions of genome sequences in less computational time. The proposed approach extracts features in five stages, i.e., based on the length of the sequence, the frequency of nucleotide bases, the pattern organization of nucleotide bases, distribution of nucleotide bases, and the entropy of the sequence to generate a fixed-length numeric vector consist of only 14 dimensions to describe each genome sequence uniquely. The proposed approach efficiently extracts the context-based features in terms of pattern organization and distribution, also removes the drawback of extracting same features for the dissimilar sequences using a novel power method. The feature extracted with the proposed scalable feature extraction approach is applied on k-means and fuzzy c-means clustering techniques. The experimental results show that the proposed method is highly successful and efficient in terms of computing time in comparison to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

One of the most used algorithms to solve the fuzzy clustering problem is Fuzzy C-Means; however, one of its main limitations is its high computational complexity. It is known that the efficiency of an algorithm depends, among other factors, on the strategies for its initialization and convergence. In this research, a new convergence strategy is proposed, which is based on the difference of the objective function values, in two consecutive iterations, expressed as a percentage of its value in the next to the last one. Additionally, a new method is proposed to optimize the selection of values of the convergence or stop threshold of the algorithm, which is based on the Pareto principle. To validate our approach, a collection of real datasets was solved, and a significant reduction in the number of iterations was observed, without affecting significantly the solution quality. Based on the proposed method and the experiments carried out, we found it is convenient to use threshold values equal to 0.73 and 0.35 if a decrease in the number of iterations of approximately 75.2% and 64.56%, respectively, is wanted, at the expense of a reduction in solution quality of 2% and 1%, respectively. It is worth mentioning that, as the size of the datasets is increased, the proposed approach tends to obtain better results, and therefore, its use is suggested for datasets found in Big Data and Data Science. [ABSTRACT FROM AUTHOR]

With the changing trends in ideological and political teaching methodologies in colleges and higher institutes, the previous political and ideological teaching mode of blackboard and power point-enabled face-to-face instruction is in danger of being phased out. As a result, college counselors' work in political and ideological education is an essential component of college-level instruction. The reform's focal points, i.e., integral platforms, are wireless communication and big data video streaming. These platforms provide a sense of situation, immersion, and involvement that traditional ideological and political education lacks. Using big data concepts for wireless-enabled video streaming, this paper assesses the effectiveness of college counselors' ideological and moral instructions. We provide data-driven insights to improve the efficacy of education by analyzing a large quantity of video data, which include teaching content, student behavior, and interaction status. We present a big data-enabled evaluation approach that blends ideological and moral education aspects, employing least squares fitting analysis for indicator distribution control, fuzzy support vector machine and fuzzy clustering models for assessment. The study conducts quantitative analysis, dynamic monitoring, and feature extraction of efficacy indicators by assessing college counselors' advice based on parameters and benefits. The experiment shows that the proposed method has a high degree of confidence in evaluating the effectiveness of college counselors' ideological and moral education and can dynamically adjust their approach to improve data interaction ability and analysis. [ABSTRACT FROM AUTHOR]

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching. [ABSTRACT FROM AUTHOR]

An approach for generating an optimal rule base of a fuzzy system is proposed, that relies on ellipsoidal clustering of observable data. Premises of fuzzy rules are formed by constructing projections of ellipsoids onto the coordinate axes while conclusions are formed either using ellipsoid axes or also based on projections. The idea of optimization is in using ellipsoids of the minimal volume that includes all points of the cluster. A comparative analysis of various ways to choose optimal parameters for ellipsoids covering the clusters is performed. The root-mean-square error is used to estimate the approximation accuracy of the resulting fuzzy system. [ABSTRACT FROM AUTHOR]

With the increasingly severe unemployment situation, the competition among undergraduates in employment is becoming more and more fierce, and the psychological pressure of undergraduates in employment is also growing. Undergraduates have a huge psychological gap because of the conflict between their ideals in employment and the specific social reality. This gap puts undergraduates in a state of psychological imbalance, often accompanied by anxiety, inferiority, self-denial, and other mentalities. It is very necessary to deeply explore the internal relationship between the position of the enterprise and the comprehensive quality of the employed students, and on this basis, it is necessary to construct an analysis mechanism for the matching of people and posts based on the portrait of students and the information of job requirements. Taking these points into account, in this study, we investigated the use of fuzzy clustering algorithms in the evaluation of psychological health in undergraduate students. Student psychological pressure is on the rise as a result of the increasingly competitive work landscape, prompting the development of novel early detection and intervention strategies. We proposed using fuzzy clustering methods to efficiently classify students according to their mental health. For this purpose, we collected many psychologically relevant characteristics, from different students using a SCL-90 symptom checklist. After the data collection, the preprocessing and feature extraction were carried out. The preprocessed data is then used for the students' psychological health analysis. The students are given degrees of membership based on their distance from cluster centroids, allowing them to simultaneously belong to many clusters (good, normal, mild abnormality, and severe abnormality). To maximize the performance of our proposed model to divide students into various mental health groups with flexible and exact membership degrees, we fine-tuned the membership function by iteratively adjusting parameters. The resulting our proposed method offers a flexible and precise way to grasp the mental health landscape, opening up a promising new direction for specialized assistance and interventions. Our research adds to the expanding body of knowledge on data-driven approaches to mental health evaluation by presenting a novel application of fuzzy clustering in the context of student well-being assessment. [ABSTRACT FROM AUTHOR]

The present research investigates priority areas of interventions for improving accessibility to Metro stations in Delhi based on private vehicle users' perceptions. The focus is on identifying the avenues of infrastructure improvement in parking facilities near Metro stations. A tablet-based face-to-face questionnaire survey was adopted to collect 1,050 responses regarding perceived importance and satisfaction ratings of 17 service quality attributes under study on a 6-point Likert scale. The data were checked, cleaned, and analyzed using revised importance–performance analysis. A fuzzy c-means clustering technique was used to develop the factor structure and management scheme clusters. Finally, priority intervention areas were identified, and resources allocated by relating factor structures and management schemes. The findings indicated that the presence of designated parking facilities, provision of variable message signs (VMSs), parking guidance information, parking charges, and in-vehicle travel time were top priority attributes and warranted immediate improvement. Security, luggage space, extreme weather conditions, and universal design considerations were under low priority. Security at the Metro parking lots had optimum service levels and was expected to be maintained. Travel route information, safety, riding comfort, convenience, egress time, operational and maintenance cost, and illumination were attributes under "possible overkill," indicating the need to transfer resources to improve other service attributes. The findings of the current work shall be useful to transport planners, traffic engineers, and officials in enhancing accessibility of private vehicle users to Metro stations by providing better Park-and-Ride facilities. [ABSTRACT FROM AUTHOR]

Job scheduling of high performance cluster is a crucial task that affects the efficiency and performance of the system. The accuracy of job runtime prediction is one of the key factors that influences the quality of job scheduling. In this paper, we propose a novel method for job runtime prediction based on Transformer with plain connection and attention mechanism. The proposed method utilizes the job category information obtained by clustering the historical log datasets, and selects six-dimensional features that are highly correlated with job runtime. We divide the datasets into multiple job sets according to the length of job runtime, train and predict each job set separately. We evaluate the proposed method on the HPC2N dataset, and compare it with several existing methods. The results show that the proposed method achieves an average accuracy of 0.892, with 15.2% MAPE, and outperforms other methods in terms of prediction performance and training time. The proposed method can be applied to improve the efficiency and quality of job scheduling in high performance cluster. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of traditional Adaptive Neural-Fuzzy Inference System (ANFIS) in water quality prediction, such as low learning efficiency and poor prediction accuracy, this paper proposed an optimal coagulant dosage prediction hybrid model based on fuzzy C-means clustering algorithm (FCM) and improved sparrow search algorithm (SSA). The hybrid prediction model is named as FCM-ISSA-ANFIS. Firstly, the water quality data of drinking water treatment plant (WTP) are statistically characterized and Pearson correlation analysis is used to determine the input variables and output variables of ANFIS. Then, the water quality data is divided into training set and test set, and the divided data sets are clustered and analyzed by FCM to determine the new fuzzy rule numbers of ANFIS. What’s more, the improved SSA is used to train the antecedent parameters and consequent parameters of ANFIS to accelerate the convergence of the algorithm and improve the ability of jumping out the local optimum. Compared with the traditional ANFIS model based on subtractive clustering, the experimental results show that the root mean square error (RMSE), mean absolute error (MAE) and standard deviation (SD) of the proposed FCM-ISSA-ANFIS for predicting the annual coagulant dosage of drinking WTP are decreased by 45.24%, 66.34% and 54.21% respectively. The proposed algorithm can not only solve the shortcomings of traditional ANFIS, but also has fast convergence and high accuracy, which can meet the real-time production demand of drinking WTP. [ABSTRACT FROM AUTHOR]

In previous studies, customer preferences were assumed to be static when modeling their preferences based on online reviews. However, in fact, customer preferences for products are dynamic and changing over time. Few research has been conducted to model dynamic customer preferences as the time series data of customer preference are difficult to be obtained. Based on online reviews, an adaptive neuro fuzzy inference system (ANFIS) was introduced to model customer preferences, which can take into account the fuzzy nature of customers' emotions and the nonlinearity of the model. However, ANFIS is plagued with black box problems, and the nonlinearity of the model cannot be directly demonstrated. To address the above research issues, a multi-objective chaos optimization algorithm (MOCOA)-based ANFIS approach is proposed to generate customer preferences models by using online reviews, which has explicit nonlinear inputs. Firstly, a sentiment analysis approach is used to derive information from online reviews by periods, which is used as the time series data sets of the proposed model. A MOCOA is combined into ANFIS to identify the nonlinear inputs, which include single items, interactive items, and terms of second order and/or higher-order terms. Consequently, the fuzzy rules in ANFIS are expressed in polynomial form, which allows for the explicit representation of the nonlinearity between customer preferences and product attributes. A case study of sweeping robots is used to compare the validation results of the proposed approach with those of ANFIS, subtractive cluster-based ANFIS, fuzzy c-means-based ANFIS, and K-means-based ANFIS. Moreover, the proposed approach provides better performance than the other four approaches in terms of mean relative error and variance of error. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative filtering (CF) algorithm for a recommendation system motivated by the need for higher recommendation accuracy. Cluster analysis captures information from a group of users such that users within a given cluster are more similar to each other than to those in other clusters. Therefore, clustering helps to detect similar users. However, inconsistent similarity measures have been applied during the clustering and prediction stages in the literature. Hence, this study resolves such discrepancies through the proposed CF algorithm, which uses fuzzy clustering for relational data such that a common similarity measure is applied to both the clustering and prediction stages. Experiments were conducted with ten datasets based on an artificial dataset and 40 datasets based on eight real datasets to demonstrate that the proposed algorithm could achieve a higher CF accuracy than conventional methods. [ABSTRACT FROM AUTHOR]

Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches’ results not only to the base model built on an imbalanced dataset but also to the previously presented state- of-the-art solutions undersampling, SMOTE oversampling, and Borderline Smote Oversampling. The model evaluation metrics for the comparison are accuracy, roc_auc score, precision, recall and F1-score. We evaluated the success of the brand-new proposed method on three different datasets having different imbalanced ratios and for three different machine learning algorithms (k-nearest neighbors algorithm, support vector machines and random forest). According to the experiments, FuzzyCSampling is an effective way to improve the model performance in the case of imbalanced datasets. [ABSTRACT FROM AUTHOR]

One's working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction. [ABSTRACT FROM AUTHOR]

Generator rotor angle oscillations can be caused by sudden changes in its mechanical power input or its electrical power output. When dampening or synchronizing torque is inadequate, rotor angle instability occurs, resulting in an increase in rotor angle, loss of synchronism, or oscillatory swings of the rotor angle with increasing amplitude. To this end, in this work, we offer a novel two-stage approach for predicting rotor angle stability following a large disturbance. The process begins with the creation of a database of dynamic simulation scenarios. This collection contains a wide set of stable and unstable rotor angle trajectories derived from different fault simulations. Then, the fuzzy c-means (FCM) clustering algorithm is utilized to put the sampled data of rotor angles with the highest degree of similarity in the same cluster. Angle sets generated by FCM are used to train the adaptive neuro-fuzzy inference system (ANFIS). Finally, the trained ANFIS is used to predict the future rotor angle stability situation of generators. In addition, a stability index is suggested in this article, which will assist ANFIS in more accurately predicting the stability condition. The efficiency of the proposed method is tested on the IEEE 39-bus system. The obtained results from simulations confirm that the proposed strategy can correctly predict the rotor angle instability or stability situation of generators in a few cycles after fault clearance. [ABSTRACT FROM AUTHOR]

The emotional well-being of college students is of utmost significance. The psychological states of college students who are on the verge of entering the social work field form the key factor that directly influences the quality of social construction because these students constitute the primary driving force in the field. On the other hand, the overwhelming amount of schoolwork, the intense level of competitiveness, and the undeveloped psychological qualities of college students are the primary contributors to their mental health problems. Currently, an increasing number of college students are struggling with mental health issues, which will have a significant impact on the growth of families and schools and the future construction of the nation. In this paper, deep features and a multiview fuzzy clustering technique are presented, as well as a mental health assessment model (CNN-MV-MEC) that is proposed for college students. The primary purpose of this research is to determine the mental state of the input sample by classifying and identifying an EEG that was acquired through the application of CNN-MV-MEC. If a certain number of samples are found to be in negative emotional states on a regular basis or for an extended period of time, this indicates that the sample most likely contains individuals who struggle with mental health issues. At this point in time, university officials are in a position to implement follow-up mental health management actions based on the outcomes of the model evaluation process. The primary contributions of this study are as follows. First, to extract the deep features from the given dataset, this paper makes use of a traditional convolutional neural network (CNN). In the second step, a classification model is trained using a multiview maximum entropy clustering (MV-MEC) technique. In the final step, the input test data are categorized by employing the trained classification model to determine the emotional state of the sample. The SEED dataset is used as the training data for the mental health assessment model proposed in this paper. Thus, the performance of the model can be evaluated. Model comparison experiments demonstrate that the proposed approach yields more accurate results than competing methods when assessing the mental health of college students. [ABSTRACT FROM AUTHOR]

This study proposes an automatic genetic algorithm in fuzzy cluster analysis for numerical data. In this algorithm, a new measure called the FB index is used as the objective function of the genetic algorithm. In addition, the algorithm not only determines the appropriate number of groups but also improves the steps of traditional genetic algorithm as crossover, mutation and selection operators. The proposed algorithm is shown the step by step throughout the numerical example, and can perform fast by the established Matlab procedure. The result from experiments show the superiority of the proposed algorithm when it overcomes the existing algorithms. Moreover, it has been applied in recognizing the image data, and building the fuzzy time series model. These show the potential of this study for many real applications of the different fields. [ABSTRACT FROM AUTHOR]

Aiming to the problem that thermal error seriously weakens grinding accuracy, according to the involute property and involute helical gear form-grinding principle, this work proposes a novel thermal error predicting model with the combination of steady-state temperature field working condition and non-steady-state temperature field working condition to optimize machining accuracy, eliminate warm-up time, and improve processing efficiency. In view of the above, the independently developed new type of high-precision horizontal forming CNC gear grinder L300G is taken as research object, and the combination of fuzzy clustering algorithm and mutual information theory is taken to optimize temperature variables, which is also verified with the comprehensive gray correlation; on this basis, L300G thermal error predicting model is established based on the combination of autoregressive time series and BP neural network. Then, the model is given practice tests through temperature field finite element analysis, temperature and thermal error measuring, temperature variables optimizing, thermal error predicting, and involute helical gear form-grinding. Results show that the model is fairly effective in thermal error predicting; what is more, after thermal error real-time compensation, it can eliminate warm-up time of gear grinder about 6000 s, and the Gleason measuring results show that the involute helical gear form-grinding tooth profile accuracy is improved from level 6 to 4, which verifies the correctness. So that it is expected to be widely used in high-precision form-grinding of horizontal gear grinder; besides, it also has significance for high-precision form-grinding research of vertical gear grinder. [ABSTRACT FROM AUTHOR]

Fuzzy C-Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlapping clusters, it involves issues such as sensitivity to noise and outliers, the fuzzification parameter m, and the initial guess of cluster centers. There are various improvements made to FCM to tackle these limitations. One such extension, FCOM, has proved to be an efficient method for handling noise and outliers. However, it still suffers from the last two ones. In the present paper, inspired by FCOM, a new model is designed to solve these issues. Whereas varying the degree of fuzziness m leads to different clusters, determining the appropriate value optimizes solutions. However, manually tuning this parameter can be time-consuming, especially when dealing with large data sets. To mitigate the dependence on this parameter, the proposed model utilizes the entropy theory to control the uncertainty associated with the input data. Extensive evaluations are conducted on benchmark datasets to analyze the impact of m on cluster formation and clustering results. The competitive results confirm the effectiveness of the proposed model for handling fuzziness degree and its capability to accelerate convergence to optimal solutions. Moreover, the results show that the proposed model discovers vague boundaries precisely. [ABSTRACT FROM AUTHOR]

Various fuzzy clustering algorithms have been proposed for vectorial data. However, most of these methods have not been applied to series data. This study presents three fuzzy clustering algorithms for series data based on shape-based distances. The first algorithm involves Shannon entropy regularization of the k-shape objective function. The second algorithm is similar to the revised Bezdek-type fuzzy c-means algorithm obtained by replacing the membership of the hard c-means objective function with its power. The third algorithm involves Tsallis entropy regularization of the objective function of the second algorithm. Theoretical observations revealed that the third algorithm is a generalization of the first and second algorithms, which was validated by numerical experiments. Furthermore, numerical experiments were performed using 11 benchmark datasets to demonstrate that the third algorithm outperforms the others in terms of accuracy. [ABSTRACT FROM AUTHOR]

Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive information about the load weight indirectly from other sensors. In this paper, a static payload weight is estimated based on the local strain of a crane's girder and the current position of the trolley. Soft-computing-based techniques are used to derive a nonlinear input–output relationship between the measured signals and the estimated payload mass. Data-driven identification is performed using a novel variant of genetic programming named grammar-guided genetic programming with sparse regression, multi-gene genetic programming, and subtractive fuzzy clustering method combined with the least squares algorithm on experimental data obtained from a laboratory overhead crane. A comparative analysis of the methods showed that multi-gene genetic programming and grammar-guided genetic programming with sparse regression performed similarly in terms of accuracy and both performed better than subtractive fuzzy clustering. The novel approach was able to find a more parsimonious model with its direct implantation having a lower execution time. [ABSTRACT FROM AUTHOR]

Clustering is a common task in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g., rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g., detecting critical situations such as detecting boundary crossing from a robot arm that could injure human beings) demand for efficient data clustering algorithms that can effectively utilize the increasing computational capacities of modern hardware. To that end, we leverage approximation and parallelization, where the former is to scale down the amount of data, and the latter is to scale up the computation. Regarding parallelization, we explore a design space for synchronization and workload distribution among the threads. As we study different parts of the design space, we propose representative Parallel Multiphase Approximate Cluster Combining, abbreviated as PARMA-CC, algorithms. We show that PARMA-CC algorithms yield equivalent clustering outcomes despite their different approaches. Furthermore, we show that certain PARMA-CC algorithms can achieve higher efficiency with respect to certain properties of the data to be clustered. Generally speaking, in PARMA-CC algorithms, parallel threads compute summaries associated with clusters of data (sub)sets. As the threads concurrently combine the summaries, they construct a comprehensive summary of the sets of clusters. By approximating a cluster with its respective geometrical summaries, PARMA-CC algorithms scale well with increasing data volumes, and, by computing and efficiently combining the summaries in parallel, they enable latency improvements. PARMA-CC algorithms utilize special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC algorithms can complement and outperform well-established methods, with significantly better timeliness especially when utilizing multiple threads, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour. • Parallelization and approximation are utilized to increase data clustering efficiency. • Data in IoT applications appears with a wide range of properties. • Adjusting data structures and algorithms based on data for efficiency. • Adjusting parallel work sharing and collaboration based on data for efficiency. • Efficient approximate clustering with adjustable accuracy-timeline trade-off. [ABSTRACT FROM AUTHOR]

Although there are different approaches and models to understand and interpret the structures in crustal deformations, one of them is the block modeling method. Using block modeling, one can determine plate movements, parameters such as slip rates, locking depths or Euler poles on faults. However, the accuracy of the block modeling results is related to how well the block boundaries are determined. One of the most important steps of block modeling is the detection of block boundaries and clustering can be used as a tool for this. Cluster analysis assigns data to similar groups based on similarities and differences in the data subject to clustering. In this study, Türkiye was determined as the study area. In this context, we utilized the ensemble clustering algorithm to cluster recent Global Navigation Satellite Systems (GNSS) velocity field in Türkiye and determine block boundaries. Current GNSS velocity field, which consists of 78% survey and 22% continuous type GNSS data processed together, used for clustering analysis for the first time in this study. Before clustering, we employed three different methods - Davies-Bouldin, Gap statistics, and Silhouette - to determine the optimal number (cluster number that best fit to GNSS velocity field) of clusters. Then, k-means, HAC, and spectral clustering techniques were then applied to cluster current GNSS velocities. Finally, we utilized the Meta-Clustering Algorithm (MCLA) as an ensemble clustering technique to cluster the horizontal components of the current velocity domain and present our findings. [ABSTRACT FROM AUTHOR]

Visualization techniques can aid in the comprehension and appreciation of the inherent connections between ecological and economic data. However, traditional visualization techniques still suffer from the problems of information overload and missing information. To address this problem, the study proposed a technique to improve the clustered data based on force-directed edge binding of segmented skeletons and constructed a visual analysis model for ecological and economic data based on the improved technique. The study used the parallel coordinates technique, the technique of presenting complex data relationships in parallel coordinates, as the control experimental group in the technical performance comparison test. The results showed that the data relevance indexes of the improved technique and all the control experimental groups were 0.145 and 0.138, respectively. The maximum positive value of the accuracy error of the number of clusters in the improved model was 0.012, while the minimum negative value was -0.003. The control experimental groups were 0.022, -0.016 and 0.022, -0.018, respectively. The study proposed a superior visual analysis model for ecological and economic research. The model utilized a force-directed edge-bound clustering technique based on segmented skeleton data, providing accurate and comprehensive insights. The model could provide more accurate and comprehensive data analysis and decision-making basis to promote the development and sustainability of ecological economy. [ABSTRACT FROM AUTHOR]

Pile foundations are often subjected to lateral pressures in addition to axial loads due to earthquakes, ground pressure, and wind pressures in various buildings. As a result, pile foundations have gotten more research than any other type of foundation. Furthermore, estimating the lateral load capacity of a pile (LLCP) accurately is a difficult undertaking, and there has been relatively little study in this field. To overcome these problems, in this study, the adaptive neuro-fuzzy inference system (ANFIS) was ustilized to construct forecasting models for the indirect assessment of LLCP embedded in clay in this work. The fuzzy c-means clustering technique (FCM) and the subtractive clustering method (SCM) were implemented as ANFIS models. The data from open-source literature were used to evaluate the two ANFIS models. In these models, pile length (L), pile diameter (D), undrained shear strength of soil (Su), and eccentricity of load (e) were used as the inputs, while the measured LLCP in clay was the output. To compare the performance of the estimating models, several statistical performance measures were used. The modeling results show that the relationships determined for estimating the LLCP in clay by ANFIS models (ANFIS-SCM and ANFIS-FCM) are accurate and close to the real value. It can also be concluded that the use of ANFIS models to predict the LLCP in clay is very efficient. [ABSTRACT FROM AUTHOR]

In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability. [ABSTRACT FROM AUTHOR]

Web page recommendation system has attracted more attention in recent decades. The web page recommendation has various characteristics than the classical recommenders. It is the process of predicting the request of the next web page that users are significantly interested while searching the web. It helps the users to find relevant pages in the field of web mining. In particular, web user may spend more time to identify expected information. To understand behavior of users and to visit the page based on their interest at a specific time, an effective web page recommendation method is developed by developed Multi-Verse Sailfish Optimization (MVSFO)-based Deep Residual network. Accordingly, proposed MVSFO is derived by the integration of Multi-Verse Optimizer (MVO) and Sailfish Optimizer (SFO), respectively. Here, the process of recommendation is carried out using weblog data and the web page image. The sequential patterns are acquired from weblog data, and the patterns are grouped with Deep fuzzy clustering based on cosine similarity. The matching process among test pattern and sequential patterns are made using Canberra distance. Here, the recommended web pages obtained from the weblog data and pages obtained from web pages image using the Deep Residual network are enable to generate the output using fractional order-based ranking. The developed scheme attained more effectiveness by the measures, such as F-measure, precision, and recall as 85.30%, 86.59%, and 86.04%, respectively for MSNBC dataset. [ABSTRACT FROM AUTHOR]

Among the metro cities in India, Kolkata is chosen incidentally for the present study. The study aims at clustering of pre-monsoon days of the urban area, Kolkata (22º32′N, 88º20′E) (India) in two groups (thunderstorm days denoted by TS and non-thunderstorm days denoted by NTS in the literature) using hard k-mean technique, backward selection procedure and fuzzy c-mean algorithm (FCM). Various thermodynamic and dynamic parameters that are already identified by several scientists as responsible for thunderstorm formation have been considered here for different atmospheric layers up to 500 hPa. The study is performed in two stages: In the first stage, the hard k-mean technique is applied to cluster the days of a semi-supervised dataset in the two categories mentioned above. Then the backward selection procedure is used to find the best possible combinations of the theoretically influential atmospheric parameters considered in the study, that plays the dominant role in the categorization on the basis of performance score (PC). In the second stage of the work, fuzzy c-mean algorithm is applied to the same semi-supervised dataset of parameters to clarify the results obtained in the first stage. This study is performed separately for the morning (0000 UTC) and afternoon (1200 UTC) atmosphere as it is already revealed that there is a structural difference between the morning and afternoon atmosphere of Kolkata, India. In the first stage with the (thunderstorm and non-thunderstorm) dates of 0000 UTC (morning) data reveals that the combination of maximum vertical velocity and P–PLCL at 1000 hPa level performs better in detecting the pre-monsoon thunderstorm days, whereas with those at 1200 UTC (afternoon) data showed vertical wind speed shear for (1000–850) hPa layer, maximum vertical velocity, P–PLCL at 1000 hPa level and (θes–θe) at 850 hPa level dominate better in detecting thunderstorm days. It is interesting to note that these findings are supported by FCM in the second stage for both morning and afternoon atmosphere. Research highlights: The study in the first stage reveals that the hard k-means clustering technique with two different metrics, Euclidean distance produces more stable results than Manhattan distances. In the second stage the fuzzy c-means algorithm helps to clarify the reasons behind the results obtained in the first stage. In this study proportion correct, HK skill score and backward selection procedure indicates that combination of maximum vertical velocity and P–PLCL furnish better results in morning whereas in afternoon along with the above two parameters, vertical wind speed shear (1000–850 hPa) and (θes–θe) at 850 hPa also gives better results for the categorization of pre-monsoon days of Kolkata (India), in two groups, thunderstorm and non-thunderstorm. [ABSTRACT FROM AUTHOR]

In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields. However, the traditional Euclidean distance cannot measure the similarity between samples well in high-dimensional data. Moreover, if there is an overlap between clusters or a strong correlation between features, clustering accuracy will be easily affected. To overcome the above problems, a collaborative possibilistic fuzzy clustering algorithm based on information bottleneck is proposed in this paper. This algorithm retains the advantages of the original algorithm, on the one hand, using mutual information loss as the similarity measure instead of Euclidean distance, which is conducive to reducing subjective errors caused by arbitrary choices of similarity measures and improving the clustering accuracy; on the other hand, the collaborative idea is introduced into the possibilistic fuzzy clustering based on information bottleneck, which can form an accurate and complete representation of the data organization structure based on make full use of the correlation between different feature subsets for collaborative clustering. To examine the clustering performance of this algorithm, five algorithms were selected for comparison experiments on several datasets. Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of clustering accuracy and collaborative validity. [ABSTRACT FROM AUTHOR]