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This study proposes two fuzzy clustering algorithms based on autoregressive moving average (ARMA) model for series data. The first, referred to as Tsallis entropy-regularized fuzzy c-ARMA model (TFCARMA), is created from k-ARMA, a conventional hard clustering algorithm for series data. TFCARMA is motivated by the relationship between the two clustering algorithms for vectorial data: k-means and Tsallis entropy-regularized fuzzy c-means. The second, referred to as q-divergence-based fuzzy c-ARMA model (QFCARMA), is created from ARMA mixtures, a conventional probabilistic clustering algorithm for series data. QFCARMA is motivated by the relationship between the two clustering algorithms for vectorial data: Gaussian mixture model and q-divergence-based fuzzy c-means. Based on numerical experiments using an artificial dataset, we observed the effects of fuzzification parameters in the proposed algorithms and relationship between the proposed and conventional algorithms. Moreover, numerical experiments using seven real datasets compared the clustering accuracy among the proposed and conventional algorithms. [ABSTRACT FROM AUTHOR]

When conducting maneuver target tracking, trajectory initialization plays a crucial role in enhancing the accuracy of tracking algorithms. During maneuver target tracking, the accuracy of the tracking algorithm can be significantly improved through trajectory initialization. However, the traditional trajectory initialization algorithms face issues such as susceptibility to noise interference, lack of universality, and poor robustness in environments with high clutter levels. To address these issues, this study proposes a trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). The algorithm utilizes multidimensional feature information of the target, such as speed and irradiance, to determine point trajectory affiliation by assigning weights based on the clustering center of each feature type. Subsequently, it updates the clustering center and weight assignment using the new target features, ultimately deriving the correct trajectory through iterative processes. Experimental results demonstrate that the proposed method achieves an average stable initialization frame number of 3.12 frames, an average correct trajectory initialization rate of 99.59%, an average false trajectory occupancy rate of 0.04%, and an average missed batch rate of 0.06%. These results indicate improvements of at least 0.87 frames, 27.11%, 60.28%, and 6.48%, respectively, in terms of initialization rate, false trajectory rate, and missed batch rate, when compared to traditional methods. The algorithm enhances the accuracy and robustness of trajectory initialization in challenging environments characterized by solid clutter and target maneuvers, offering significant practical value for target tracking in complex scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instances, including VLSI and Euclidean types with up to 316,000 nodes, our method consistently outperforms traditional and current leading techniques for large TSPs. Our heuristic's tours encompass nearly all edges of optimal or best-known solutions, and its candidate sets are significantly smaller than those produced with the POPMUSIC heuristic. This results in faster execution of subsequent improvement methods, such as Helsgaun's Lin–Kernighan heuristic and evolutionary algorithms. This substantial enhancement in computation time and solution quality establishes our method as a promising approach for effectively solving large-scale TSP instances. [ABSTRACT FROM AUTHOR]

The present investigation employed information mining and knowledge learning processes to showcase their efficacy in comprehending the viscoelastic properties of nanocomposites comprising vapor-grown carbon nanofiber and vinyl ester. The study relied solely on the data obtained from an experimental analysis. This study involves an investigation into the utilization of enhanced distance strategy in conjunction with the data-space clustering techniques possibilistic C-means and fuzzy possibilistic C-means. This study employs clustering methodologies to discern patterns of behaviour in the viscoelastic properties of polymer nano-composites. Principal component analysis is utilized as a dimensionality reduction technique to facilitate this analysis. By employing these methodologies, it was feasible to categories the nanocomposite specimens based on diverse attributes and partition the vapour-grown carbon nanofiber and vinyl ester specimens into distinct clusters. This paper emphasizes the significance and utility of data mining methodologies within the realm of materials informatics. [ABSTRACT FROM AUTHOR]

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index. [ABSTRACT FROM AUTHOR]

This study is concerned with a realization of horizontal federated Gustafson–Kessel clustering algorithm and the ensuing construction of ellipsoidal information granules. As a fundamental component of Granular Computing, information granules play an important role in human-centric computing, such as human cognition and decision-making. Driven by the concerns of data privacy and confidentiality, it is of interest to investigate how to construct information granules on the basis of horizontally partitioned numeric data distributed across different sites using a privacy-preserving approach. To meet this challenge, federated learning has become an appealing solution to the problem of forming meaningful clusters (information granules) while ensuring data privacy and confidentiality. A two-development strategy is applied in the proposed algorithm: first, a collection of numeric representatives (prototypes) is obtained with the use of federated Gustafson–Kessel algorithm, which is able to reveal ellipsoidal shapes in the datasets and second, information granules are built through engaging the principle of justifiable granularity. A series of experimental studies demonstrate the effectiveness of the proposed federated Gustafson-Kessel algorithm in revealing the structure of the entire dataset. The formed ellipsoidal information granules help us gain a better insight into the topology of the overall dataset. [ABSTRACT FROM AUTHOR]

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neurofuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty. [ABSTRACT FROM AUTHOR]

The risk control model of the power supply chain system is established. A fault information identification method based on fuzzy clustering is proposed. This method fully considers the power grid's characteristics and uses terrible data. A risk assessment model based on fuzzy set theory is established by the COWA operator weight method and grey cluster evaluation method. The security risk identification model of power grid enterprises uses insufficient data. The security risk identification data are normalized and classified. Empirical analysis determines various risk factors that may appear in power projects. The applicability and feasibility of the index system and evaluation model are verified. [ABSTRACT FROM AUTHOR]

In order to implement personalized recommendation algorithms for e-commerce, the author proposes a genetic fuzzy algorithm based on user behavior to improve the sales, personalized recommendation, user satisfaction, and purchase matching performance of e-commerce. Collect data based on e-commerce personalized preference recommendation information, extract the associated feature quantities of personalized data for clustering processing, and then combine fuzzy B-means clustering method to achieve e-commerce personalized recommendation. According to the individual preferences of e-commerce, the collected data samples are fitted with differences and restructured, and a genetic evolution method is adopted for global optimization. The experimental results show that the optimized genetic fuzzy algorithm used in this method has improved stability and accuracy compared to the PSO method, with an accuracy increase of 4%. This proves that the algorithm can provide the services needed by users more quickly and is an effective means. [ABSTRACT FROM AUTHOR]

Collaborative search with multiple Autonomous Underwater Vehicles (AUVs) significantly enhances search efficiency compared to the use of a single AUV, facilitating the rapid completion of extensive search tasks. However, challenges arise in underwater environments characterized by weak communication and dynamic complexities. In large marine areas, the limited endurance of a single AUV makes it impossible to cover the entire area, necessitating a collaborative approach using multiple AUVs. Addressing the limited prior information available in uncertain marine environments, this paper proposes a map-construction method using fuzzy clustering based on regional importance. Furthermore, a collaborative search method for large marine areas has been designed using a policy-iteration-based reinforcement learning algorithm. Through continuous sensing and interaction during the marine search process, multiple AUVs constantly update the map of regional importance and iteratively optimize the collaborative search strategy to achieve higher search gains. Simulation results confirm the effective utilization of limited information in uncertain environments and demonstrate enhanced search gains in collaborative scenarios. [ABSTRACT FROM AUTHOR]

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67–0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45–0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44–0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows. [ABSTRACT FROM AUTHOR]

Privacy protection plays a crucial role in recommender systems as it helps to protect users’ sensitive information from disclosure risks. Recent recommendation unlearning has attracted increasing attention as an effective method of privacy protection. Existing methods often partition data into sub-partitions before training to enhance model training efficiency. However, simply partitioning interactions into sub-partitions can disrupt the integrity of user-item relationships and reduce the availability of data. In addition, the presence of false-positive noise in sub-partitions with implicit feedback can interfere with model training, preventing it from accurately capturing users’ true preferences. To address these challenges, a recommendation unlearning algorithm combining fuzzy clustering and adaptive denoising (FDRU) is proposed. Firstly, fuzzy clustering determines membership by calculating cosine distances between samples and various cluster centers, subsequently dividing the training dataset into several sub-partitions. Then, FDRU designs an adaptive denoising algorithm that dynamically eliminates false positive noise in sub-partitions based on thresholds. Finally, it utilizes dynamic weighted aggregation of sub-models for prediction and top-N recommendations. In order to assess the performance of the proposed algorithm, extensive experiments are carried out on three public datasets. Experimental results indicate that FDRU outperforms other benchmark algorithms on Recall and NDCG.

Resource-constrained problems for technology-based applications/services are common due to pervasive utilization and in-definite user/demand densities. Traditional resource allocation methods consume high allocation time and make it difficult to predict the possible solutions from the collection of resources. Various range of solutions through optimizations are provided for addressing the issues that, however, result in imbalanced solutions. This article assimilates genetic algorithm (GA) and fuzzy clustering process and introduces resource-constrained reduction framework. The proposed framework utilizes a GA for mutating the allocation and availability possibilities of the resources for different problems. The possibilities of solutions are tailored across various demands preventing replications. Post this process, the fuzzy clustering segregates the optimal, sub-optimal, and non-optimal solutions based on the mutation rate from the genetic process. This reduces the complexity of handling heterogeneous resources for varying demand, user, and problem densities. Based on the clustering process, the crossover features are tailored across multiple resource allocation instances that mitigate the existing constraints. This proposed framework improves the problem-addressing ability (11.44%) and improves resource allocation (8.08%), constraint mitigation (11.1%), and allocation time (11.85%).

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.

Abstract Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

In recent years, extensive studies have been carried out in community detection for social network analysis because it plays a crucial role in social network systems in today's world. However, most social networks in the real world have complex overlapping social structures, one of the NP-hard problems. This paper presents a new model for overlapping community detection that uses a multi-objective approach based on a hybrid optimization algorithm. In this model, the Modified Selection Function (MSF) hybrids the algorithms and recovery mechanism, the Slime Mould Algorithm (SMA), the Sine Cosine Algorithm (SCA), and the association strategy. Also, considering that these algorithms have been presented to solve single-objective optimization problems, the Pareto dominance technique has been used to solve multi-objective problems. In addition to overlapping community detection and increasing detection accuracy, the fuzzy clustering technique has been used to select the heads of clusters. Sixteen synthetic and real-world data sets were utilized to assess the suggested model, and the outcomes were contrasted with those of existing optimization techniques. The proposed model has performed better than the other tested algorithms in comparing the tests conducted by us in all 16 data sets, in the comparisons made with the algorithms proposed in other works in 11 data sets out of 14 data. The set has performed better than competitors. As a conclusion, the findings show that this model performs better than other methods. [ABSTRACT FROM AUTHOR]

Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent research topic in recent years. In this paper, we propose a novel approach: the semi-supervised consensus fuzzy clustering method for multiview relational data (SSCFMC). This method combines the advantages of fuzzy clustering and consensus clustering to address the challenges posed by multi-view data. By leveraging available labeled information and the relational structure among views, our method aims to enhance clustering performance. Extensive experiments on benchmark datasets demonstrate that our method surpasses existing single-view and multi-view relational clustering algorithms in terms of accuracy and stability. Specifically, the SSCFMC algorithm exhibits superior clustering performance across various datasets, achieving an adjusted rand index (ARI) of 0.68 on the multiple features dataset and an F-measure of 0.91 on the internet dataset, highlighting its robustness and efficiency. Overall, this study advances multi-view clustering techniques for relational data and provides valuable insights for researchers in this field. [ABSTRACT FROM AUTHOR]

Abstract The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches.

Summary It is estimated that there will be over two dozen billion Internet of Things (IoT) connections in the future as the number of connected IoT devices grows rapidly. Due to characteristics like low power consumption and extensive coverage, low‐power wide area networks (LPWANs) have become particularly relevant for the new paradigm. Long range wide area network (LoRaWAN) is one of the most alluring technological advances in these networks. Although it is one of the most developed LPWAN platforms, there are still unresolved issues, such as capacity limitations. Hence, this research introduces a novel resource scheduling technique for the LoRAWAN network using deep reinforcement learning. Here, the information on the LoRaWAN nodes is learned by the reinforcement technique, and the knowledge is utilized to allocate resources to improve the packet delivery ratio (PDR) performance through a proposed coati optimal Q‐reinforcement learning (CO_QRL) model. Here, Q‐reinforcement learning is utilized to learn the information about nodes, and the coati optimization algorithm (COA) helps to choose the optimal action for enhancing the reward. In the proposed scheduling algorithm, the weighted sum of successfully received packets is treated as a reward, and the server allocates resources to maximize this Q‐reward. The evaluation of the proposed method based on PDR, packet success ratio (PSR), packet collision rate (PCR), time, delay, and energy accomplished the values of 0.917, 0.759, 0.253, 85, 0.029, 7.89, and 10.08, respectively. [ABSTRACT FROM AUTHOR]

Image segmentation through fuzzy clustering has been widely used in diverse areas. However, most of those clustering algorithms require that some of their parameter values be determined manually. The number of clusters, C, is one of the most important parameters because it impacts the number of regions to segment and directly affects the performance of the clustering algorithms. Some state-of-the-art general clustering algorithms methods automatically determine C. However, not all of them can be employed for image segmentation. Therefore, this paper describes the method automatic estimation of number of clusters by image complexity (AENCIC). AENCIC is a method that automatically estimates the best C needed by state-of-the-art clustering algorithms to segment an image, considering the image complexity perceived by humans. AENCIC was designed to work with fuzzy clustering algorithms employed to segment real-world images because this kind of segmentation is an ill-defined problem causing a high variation of C per image to attain a good segmentation. Results using the database BSDS500 demonstrate that using AENCIC to estimate C improves the performance of state-of-the-art fuzzy clustering image segmentation algorithms up to 94% of their ideal maximum performance, allowing those algorithms to work without human intervention. [ABSTRACT FROM AUTHOR]

The School Bus Routing Problem (SBRP) is a challenging optimization problem that has received increasing attention in recent years. The problem is composed of three sub-problems: facility location selection, assignment problem, and vehicle routing problem, which can be solved in a single stage or across multiple stages. In this study, we propose a novel two-stage approach to solve the SBRP that combines Fuzzy C Means (FCM) and K-means clustering algorithms with a Genetic Algorithm (GA). In the first stage, we used FCM and K-means to identify the optimal bus stop locations and assigned students to the nearest stop based on the distance metric. This two-stage approach reduces the search space and improves the efficiency of the GA in the second stage. In the second stage, we employed the GA to generate the optimal vehicle route that minimizes the total distance traveled by all vehicles. We compared our results with those in the literature and found that the K Means-GA approach outperformed the previous results. However, the FCM-GA approach yielded significantly inferior results, indicating that the choice of clustering algorithm plays a crucial role in the performance of the overall system. Our study provides insights into the importance of selecting appropriate clustering algorithms for solving the SBRP and proposes a two-stage solution that can be easily implemented in real-world scenarios. Our approach reduces the computational time and provides an effective solution for reducing the total distance traveled by school buses. [ABSTRACT FROM AUTHOR]

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. [ABSTRACT FROM AUTHOR]

Poverty is always associated with poor green infrastructure. Indonesia has a significant poverty rate and low green infrastructure investment. Therefore, this study uses fuzzy clustering with area weighting (FGWC-HHOP) and 2018 Potential Village Census (PODES) data to analyze green infrastructure risk and poverty in Indonesia. The first cluster, which is mainly urban, is vulnerable, while the second cluster has poor air quality and green infrastructure. Future government policies should encourage sustainable development, especially green infrastructure, to reduce social vulnerability. Consider green infrastructure and social vulnerability when planning urban expansion and poverty alleviation in Indonesia. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that influence brain's neurological, behavioral, and physiological functions and includes motor and nonmotor manifestations. Although there have been several PD diagnosis systems with supervised machine learning techniques, there are more efforts that need to enhance the accurate detection of PD in its early stage. The current paper developed a novel approach by integrating Least Squares Support Vector Regression (LS-SVR) and Fuzzy Clustering for Unified Parkinson's Disease Rating Scale (UPDRS) diagnosis. This paper used feature selection and Principal Component Analysis (PCA) to overcome the multicollinearity issues in data. This paper used a large medical dataset including Motor- and Total-UPDRS to demonstrate how the proposed method can improve prediction performance via extensive evaluations and comparisons with existing methods. Compared to other prediction methods, the experimental results demonstrate that the proposed method provided the best accuracy for Total-UPDRS (Root Mean Squared Error = 0.7348; R 2 = 0.9169) and Motor-UPDRS (Root Mean Squared Error = 0.8321; R 2 = 0.8756) predictions. [ABSTRACT FROM AUTHOR]

The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms. And, this paper provides a new method in the application field where there is a lot of noise in the dataset. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantum utility is severely limited in superconducting quantum hardware until now by the modest number of qubits and the relatively high level of control and readout errors, due to the intentional coupling with the external environment required for manipulation and readout of the qubit states. Practical applications in the Noisy Intermediate Scale Quantum (NISQ) era rely on Quantum Error Mitigation (QEM) techniques, which are able to improve the accuracy of the expectation values of quantum observables by implementing classical post‐processing analysis from an ensemble of repeated noisy quantum circuit runs. In this work, a recent QEM technique that uses Fuzzy C‐Means (FCM) clustering to specifically identify measurement error patterns is focused. For the first time, a proof‐of‐principle validation of the technique on a two‐qubit register, obtained as a subset of a real NISQ five‐qubit superconducting quantum processor based on transmon qubits is reported. It is demonstrated that the FCM‐based QEM technique allows for reasonable improvement of the expectation values of single‐ and two‐qubit gates‐based quantum circuits, without necessarily invoking state‐of‐the‐art coherence, gate, and readout fidelities. [ABSTRACT FROM AUTHOR]

With the rise of traffic over wide networks, particularly the internet, and the cloud-based transactions and interactions, database security is important for any organisation. The detection of, and protection from, unauthorised external attacks and insiders abusing privileges is an integral part of database security. To that end, we propose Outlier based Intrusion Detection in Databases for User Behaviour Analysis using Weighted Sequential Pattern Mining (BWSPM), a novel method for the detection of malicious transactions through a sequential flow from outlier detection followed by different behavioural checks at the role-based rule mining component, and finally a user level behavioural check. In the worst case, a transaction has to go through a triple-fold security validation directing the model from generalisation to specification. The Outlier Detection module generates clusters based on the syntactic characteristics of transactions and detects transactions that do not adhere to their closest cluster. Role-level analysis is based upon mining rules that capture dynamic usage of attributes local to every role domain, and the transactions are verified against these rules. Finally, User behaviour profiling models user behaviour based on past transactions, and the incoming transaction is flagged if it diverges from that. Security checks are made at every level to prevent further transaction analysis to reduce false positive rate and achieve a higher degree of optimisation. Encouraging results, with levels of accuracy of around 86.4%, were obtained through our approach after conducting experiments on a dataset generated using the TPC-C (Transaction Processing Performance Council) benchmark. [ABSTRACT FROM AUTHOR]

Purpose. To improve the measurement and information base of ultrasonic measurements rock characteristics to assess their mineralogical varieties. It is proposed to use a combination of measurement results of the acoustic quality factor of the test sample in relation to longitudinal and transverse ultrasonic waves, as well as the characteristic coefficient based on the dispersion and the average amplitude value of the received signal, for fuzzy identification of mineralogical and technological varieties of iron ore. Methods. As elastic waves propagate through the rock mass, they undergo attenuation due to absorption and dissipation of ultrasonic signal energy. The degree of attenuation, as well as the wave propagation velocity, is dependent on the physical mechanical and chemical-mineralogical properties of the medium through which they travel. In this paper, we analyze a rock characterized by a complex structure comprising ore inclusions and surrounding matrix, each of which differs in its physical mechanical and chemical-mineralogical properties. In particular, in iron ore samples, the distribution of mineral grains and aggregates exhibits significant heterogeneity in terms of both amount and size. Findings. An iterative method of fuzzy identification of mineralogical-technological iron ore varieties, based on the analysis of their properties in vector space of features, allows, by minimizing the sums of weighted distances between the analyzed and reference values of ultrasonic measurement results, to attribute them with a certain degree of belonging to the main technological types of ores mined at the deposit, and define them as magnetite quartzite with a confidence probability of 0.93. Originality. As an information base for identification of mineralogical iron ore varieties, the results of measuring the velocity and attenuation of longitudinal and transverse ultrasonic waves of appropriate frequency are used, on the basis of which the acoustic quality factor of the rock sample is calculated, as well as the characteristic parameter S, which is determined by the dispersion and average values of the received ultrasonic signal intensity, which has traveled a certain distance in the studied environment. Practical implications. The results of tests and practical approbation of the method for identifying mineralogical iron ore varieties based on the data of ultrasonic well logging testify to its high efficiency, which allows recommending the developed scientific-technical solutions for wide industrial application at mining enterprises. [ABSTRACT FROM AUTHOR]

As one of the essential topological structures in complex networks, community structure has significant theoretical and application value and has attracted the attention of researchers in many fields. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the first stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the first stage, appropriate nodes are selected from each individual's community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to find reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results. [ABSTRACT FROM AUTHOR]

The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches. [ABSTRACT FROM AUTHOR]

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances. [ABSTRACT FROM AUTHOR]

We consider the problem of clustering functional data according to their covariance structure. We contribute a soft clustering methodology based on the Wasserstein–Procrustes distance, where the in‐between cluster variability is penalized by a term proportional to the entropy of the partition matrix. In this way, each covariance operator can be partially classified into more than one group. Such soft classification allows for clusters to overlap, and arises naturally in situations where the separation between all or some of the clusters is not well‐defined. We also discuss how to estimate the number of groups and to test for the presence of any cluster structure. The algorithm is illustrated using simulated and real data. An R implementation is available in the Appendix S1. [ABSTRACT FROM AUTHOR]

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Soil heavy metal pollution in farmland has raised widespread concern. Implementing zoning governance is critical for enhancing the efficiency of pollution prevention and control practices. Traditional zoning approaches focus more on the status of soil pollution rather than its sources, potentially resulting in subsequent governance measures being either irrelevant or only having short-term effects. Integrating information about heavy metal sources into zoning strategies is beneficial for effective pollution control. This study analyzed heavy metals in topsoil within a typical industrial park, identifying their primary sources through multivariate statistics and spatial pattern analysis. The contributions of these sources were then quantified using absolute principal component score-multiple linear regression (APCS-MLR), positive matrix factorization (PMF), and ensemble models. Based on the differences in source contributions, the study area was divided into different sub-regions by fuzzy clustering method (FCM), and appropriate management strategies were proposed for each. The results showed that cobalt (Co), chromium (Cr), copper (Cu), and nickel (Ni) were primarily derived from natural sources, with their contributions ranging from 82.82% to 89.38%. Lead–Zinc smelting was identified as a significant contributor, accounting for 65.18% of cadmium (Cd), 73.33% of lead (Pb), and 44.41% of zinc (Zn). Mercury (Hg) emissions were predominantly attributed to power plants, constituting 53.41% of the total. The study area was divided into three sub-regions, each with distinct major pollution sources. A high-risk area covering 1.553 km2 was identified in the sub-region with the most severe pollution, primarily caused by Pb–Zn smelting activities. For this high-risk area, targeted interventions at the source were recommended, such as optimizing smelting processes, increasing recovery efficiencies, reducing waste emissions, and creating buffer zones. This study confirmed the feasibility of adopting source-based zoning strategies, which yielded valuable insights for the effective management of other pollutants. [ABSTRACT FROM AUTHOR]

Methodology is described for fitting a fuzzy partition and a parsimonious consensus hierarchy (ultrametric matrix) to a set of hierarchies of the same set of objects. A model defining a fuzzy partition of a set of hierarchical classifications, with every class of the partition synthesized by a parsimonious consensus hierarchy is described. Each consensus includes an optimal consensus hard partition of objects and all the hierarchical agglomerative aggregations among the clusters of the consensus partition. The performances of the methodology are illustrated by an extended simulation study and applications to real data. A discussion is provided on the new methodology and some interesting future developments are described. [ABSTRACT FROM AUTHOR]

Energy theft poses a significant threat to the power industry, causing financial losses and grid instability. Existing detection methods often struggle with limited labeled data and the emergence of new, unobserved theft patterns. To address these challenges, we propose a novel method for energy theft detection that effectively leverages both partially observed anomalies and unlabeled data. Our approach integrates Discrete Wavelet Transform (DWT) for feature extraction, Fuzzy C-Means clustering for anomaly grouping, and weighted multi-class logistic regression for ensemble learning. Extensive experiments on a realistic dataset demonstrate that our method achieves high detection accuracy, outperforming several state-of-the-art methods, including deep learning models, while maintaining significantly lower computational cost. This robust and efficient approach enables effective detection of unobserved anomaly classes and reduces false positives, making it a valuable tool for developing reliable energy theft detection systems. We further conduct a feature importance analysis to identify influential features for optimizing detection accuracy and efficiency.