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The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Geophysical methods depend on a range of physical and chemical mechanisms, and each method has different sensitivities and resolution of Earth parameters, and over different scale lengths. Additionally, such geophysical methods will also have their own statistical characteristics. Thus, it is a significant challenge to combine different methods through a single inversion framework. Rather than attempting to find an optimal and single model for different geophysical responses, an alternative approach is to use FCM clustering to identify clusters of parameters from two or more different geophysical data sets or models that have similar statistical properties. In this paper, we apply the FCM approach to integrate data and model sets for an array of 100 broadband magnetotelluric (MT) and 100 passive seismic receivers spaced 1 km apart on a 10 by 10 grid above the Vulcan IOCG prospect in the Olympic Cu–Au Province, southern Australia. The challenge for exploration of the Vulcan prospect is that it lies beneath 750 m of regolith sedimentary cover, no single geophysical method provides a unique characterization of the deposit geometry, and drilling is very expensive. Fuzzy c-mean cluster analyses are undertaken in 2D for gravity data and shear-wave velocity model data for basement depths beneath cover and in 3D for shear-wave velocity and electrical resistivity model data. Fuzzy c-mean clustering is shown to provide a simple and efficient method of integrating different geophysical measurements to produce a geological framework that can be verified with drilling information. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

Agriculture is essential to any country's economy. Agriculture is crucial not only for feeding a country's population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources. [ABSTRACT FROM AUTHOR]

In this paper an improved multiple adaptive neuro-fuzzy inference system (MANFIS) to solve the wheeled mobile robots path planning in unstructured environments is proposed. The fuzzy C-means (FCM) clustering method is used in ANFIS to decrease the input data size, which leads to predict the efficiency of the proposed robot path planning model. The FCM clustering method allow classifying the robot sensors extracted input data into clusters; each cluster has similar properties that assists to develop the correlation between data and as a result simplify the proposed model. The design MANFIS architecture takes both the advantages of artificial neural network which has self-learning ability and fuzzy system to describe the uncertain phenomena of the data. Finally, by combining cluster data and MANFIS an optimum velocity for left and right wheel of the robot is determined, which safely navigate the robot in an optimized route. The simulation and experimental results show that the proposed path planning method is effective and can be implemented for any complex environments. The results obtained by the simulation and in physical experiments are equated with each other and noticed a good promise between both the results as the difference in results is less than 9%. The simulation results consistently demonstrate the superiority of the proposed FCM-ANFIS, manifesting performance improvements of up to 4.4% for path length reduction and up to 8.9% for mobile robot time duration reduction when compared to heuristic methods. This paper identifies and describes a new development on path planning technique that will help the robots to navigate in any kind of uncertain navigation environments. [ABSTRACT FROM AUTHOR]

Restaurant reviews play a pivotal role in shaping consumer decisions and perceptions. Analyzing these reviews through sentiment analysis provides valuable insights into customer sentiments towards various aspects of dining experiences, such as food quality, service, ambiance, and pricing. By leveraging sentiment analysis techniques, businesses can better understand customer preferences, identify areas for improvement, and enhance overall customer satisfaction. This research focuses on utilizing aspect-based sentiment analysis to predict restaurant survival, leveraging customer-generated content from online reviews. The proposed methodology encompasses data acquisition, pre-processing, feature extraction, and unsupervised approaches-based classification. Data pre-processing involves tokenization, stop word removal, lemmatization, punctuation removal, and filtering short and long words to standardize the format. Feature extraction includes lexicon-based and word encoding methods, leveraging Term Frequency-Inverse Document Frequency (TF-IDF) vectors, Ngram, Bag of Words, and Word Embedding. Unsupervised approachesbased classification entails Fuzzy C-Means (FCM), K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical Method, Hybrid Binary Particle Swarm-Optimized FCM, and HBPSO-Optimized Kmeans. Evaluation parameters are defined to assess the performance of each approach. The results showcase the effectiveness of aspect-based sentiment analysis in predicting restaurant survival, with HBPSO-Optimized FCM demonstrating the highest accuracy at 89.50%. These findings underscore the significance of leveraging customergenerated content for informed decision-making in the restaurant industry. [ABSTRACT FROM AUTHOR]

This research explores the application of neutrosophy to understand and address the inherent complexity in the oral transmission of knowledge in family agriculture. It leverages its ability to handle contradictions and ambiguities, facilitating a richer analysis of agricultural practices and social interactions. Orality, as the primary form of communication in these communities, is essential for the transmission of agricultural and cultural knowledge. This research highlights how orality acts as a vehicle for the preservation of collective identity and the transmission of ancestral knowledge. The study used Fuzzy Cognitive Maps (FCM) to describe the causal relationships between different variables of interest. Fieldwork was conducted between October 2023 and January 2024, using semi-structured interviews to capture the oral narrative of the farmers in Apagua. The analysis of the FCM showed that the components of cultural preservation and knowledge transmission are the most influential. This study demonstrated that neutrosophy is an effective tool for analyzing the complex dynamics of oral knowledge transmission in family agriculture. The conclusions underline the interdependence between knowledge transmission, social relationships, adaptation to changes, and cultural preservation. [ABSTRACT FROM AUTHOR]

In the past few years, there have been many studies addressing the simulation of COVID-19's spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual's behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform "Susceptible—Exposed—Infectious—Removed" (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions. [ABSTRACT FROM AUTHOR]

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO–GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90. [ABSTRACT FROM AUTHOR]

Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio. [ABSTRACT FROM AUTHOR]

Cancer is an acute disease that kills many people around the world, so early detection is a vital need. This study aims to investigate the effectiveness of techniques used in detecting, isolating and extracting tumors in PET/CT images using clustering techniques: K-means, Fuzzy C-mean, and hybrid technique. The results showed that the applied methods were sufficient to detect, isolate and extract areas of the tumor. The calculated tumor area was compared with the nuclear medicine specialist demarcation area, and the percent relative difference ranged between 0.135%-4.86%. As well as the results indicated that implementing the hybrid technique reduced the elapsed time required, and the reduction percentage ranged between 43.03%-97.45%. [ABSTRACT FROM AUTHOR]

To improve the accuracy of terminal area meteorological scene pattern recognition, this study adopts a clustering model based on Stacked Denoising Autoencoder. Noise is added to the input layer, and a three-layer autoencoder is constructed for greedy layer-wise training. The reduced-dimensional features are used as inputs for clustering to achieve meteorological scene pattern recognition. The method is validated using one year of meteorological data from Tianjin Binhai International Airport. Traditional similarity distance measures such as Euclidean distance, Hamming distance, and Manhattan distance are used with both K-medoids and FCM clustering methods. The results show that the similarity measure based on SDAE performs the best in both K-medoids and FCM clustering, with a difference rate of 22.4%, 12%, 17.7%, and 24.8%, 10.7%, 11.8% compared to other similarity measures, respectively. It also has the shortest computation time, demonstrating that the SDAE-based measure and clustering achieve the best performance. Ultimately, eight meteorological scenes are identified with clear and distinct classifications. [ABSTRACT FROM AUTHOR]

Paper presents an analysis of the effects of high-pressure N2 and enriched N2 injection on oil recovery under multicontact miscible and first contact miscible conditions. Phase equilibrium experiments, slimtube, and core displacement tests were carried out at the reservoir conditions to study miscible N2 flooding. Utilizing the phase equilibrium and PVT data, pseudoternary diagrams were constructed to determine the minimum enrichment required for FCM displacement. It emerged that methane vaporization is one of the main characteristics of N2 flooding, and this characteristic amplifies clearly as more N2 dissolves in the crude oil. Moreover, the coreflood results indicated that the oil recovery by enriched N2 injection is higher than that by N2 injection at the early stage of production. This was probably due to the fact that there is no interfacial tension between the enriched N2 and oil when they contact each other under FCM displacement. [ABSTRACT FROM AUTHOR]

Push notifications are widely used in Android apps to show users timely and potentially sensitive information outside the apps’ regular user interface. Google’s default service for sending push notifications, Firebase Cloud Messaging (FCM), provides only transport layer security and does not offer app developers message protection schemes to prevent access or detect modifications by the push notification service provider or other intermediate systems. We present and discuss an in-depth mixed-methods study of push notification message security and privacy in Android apps. We statically analyze a representative set of 100,000 up-to-date and popular Android apps from Google Play to get an overview of push notification usage in the wild. In an in-depth follow-up analysis of 60 apps, we gain detailed insights into the leaked content and what some developers do to protect the messages. We find that (a) about half of the analyzed apps use push notifications, (b) about half of the in-depth analyzed messaging apps do not protect their push notifications, allowing access to sensitive data that jeopardizes users’ security and privacy and (c) the means of protection lack a standardized approach, manifesting in various developer-defined encryption schemes, custom protocols, or out-of-band communication methods. Our research highlights gaps in developer-centric security regarding appropriate technologies and supporting measures that researchers and platform providers should address.

The frequent occurrence of extreme weather makes people pay more attention to environmental protection. To cope with the global climate problem, various countries re-plan social development through the concept of low-carbon. As greatly popularized by the Internet, the topic of low carbon concept is spread more through online social media, so it is urgent to understand the user’s attention to low carbon topics in a more intelligent way for subsequent relevant publicity and policy guidance. This paper studies the low-carbon topic of attention in the context of social media. First, the BERT (Bidirectional Encoder Representation from Transformers) model is used to complete the word vector feature extraction of acquired data; Secondly, the FCM method was used to complete the clustering analysis of the main topics in the low-carbon concept, and the PSO method was used to optimize the model. After optimization, the accuracy of clustering for various topics was higher than 80%. For the Esse index of cluster center variance, the method proposed in this article is also close to 10% due to other classic methods; Finally, this paper carried out an application test of low-carbon topics in the region, achieved good results, and made a detailed analysis of the distribution of various topics. It can be predicted that this method will provide more public opinion references for low-carbon development paths in various countries and regions in the future, and provide technical support for information dissemination and analysis under social media.

Medical image segmentation is a critical task in medical image analysis, and clustering algorithms can be utilized to achieve this goal. This research work focuses on the segmentation of neuro disorder magnetic resonance images using Otsu, K-means, and FCM coupled with the firefly optimization algorithm. Otsu is a classical thresholding algorithm that relies on a single threshold value to segment the images. K-Means is one of the simplest and most widely used clustering algorithms. It aims to partition data into K clusters, where each data point belongs to the cluster with the nearest mean. Fuzzy C-Means is an extension of K-Means, allowing data points to belong to multiple clusters with varying degrees of membership. In medical image segmentation, FCM was used to classify pixels or voxels into different tissue classes with soft boundaries, accounting for partial volume effects. Firefly Optimization helps in improving the convergence speed of the FCM algorithm. Firefly optimization is good at exploring the solution space and finding global optima. The combination of FCM and Firefly Optimization leads to more accurate clustering results. The performance evaluation was done by Renyi entropy and Shannon entropy, FCM coupled with the firefly optimization was found to exhibit superior results when compared with the Otsu and K-means clustering algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Segmentation of Lung Region is an important step in the clinical diagnosis of lung diseases like Pneumonia, Tuberculosis, and COVID-19. However, most the methods employed Deep learning algorithms without providing any additional information at the segmentation phase. Hence this paper proposes a new segmentation Mechanism based on the Fuzzy C-Means (FCM) and different features. A new Variant of FCM called as Dual FCM is introduced which forms an objective function based on two different Sub-Objective Functions (SOFs). The first SOF segments the images through gray pixel intestines while the second SOF used features for segmentation. Two different features are derived namely morphological features and gradient features from every CXR image. Finally, the segmented results at individual SOFs are fused to get a final segmented result. Extensive experiments have been carried out over the proposed approach through two publicly available datasets namely Montgomery County (MC) Dataset and Shenzhen Dataset. The performance is assessed through two evaluation measures namely Jaccard Index (JI) and Dice Coefficient (DC) and the average obtained is noticed as 92.985% and 95.295% respectively. Further the proposed segmentation mechanism is compared with several past lung image segmentation approaches. The approximate improvement in the JI and DC from past methods is noticed as 4.2210% and 2.3150% respectively. [ABSTRACT FROM AUTHOR]

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines 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.)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping. [ABSTRACT FROM AUTHOR]

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images. [ABSTRACT FROM AUTHOR]

In blind image deblurring, feasible solutions have been obtained by exploiting image prior information such as dark channel prior, extreme channel prior, and local minimal intensity prior. The performance highly depends on these priors, which may have poor adaptability to different image contents in real-world applications. For example, these priors only consider the changes in local minimal and maximal intensity pixels in the blurring process and ignore the difference between these changes. In this paper, we propose a novel blind image deblurring approach based on dynamic-clustering extreme intensity prior. Specifically, the patch-wise maximal pixels (PMaxP) prior and patch-wise minimal pixels (PMinP) prior are employed and clustered into two by applying fuzzy c-means (FCM) clustering. The regularizations impose the sparsity inducing on either PMaxP prior or PMinP prior in each patch. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art image deblurring algorithms on synthetic and real-world images. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy‐based model into the Fuzzy C‐Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M‐FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. Research Highlights: A novel approach is proposed in the article, integrating a neural network energy‐based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools.By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR.The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M‐FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. [ABSTRACT FROM AUTHOR]

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio.

A converter valve is the core equipment of HVDC transmission system, whose operating temperature threshold is strictly constrained. This paper proposes a novel prediction method for outlet water temperature of converter valve based on F-BP network, which aims to accurately predict the outlet water temperature and assists the operation and maintenance personnel to take measures in time so that the temperature of the converter valve will not exceed its preset threshold when the operation condition has changed. Firstly, the principle and method of the construction of typical operation databases of converter valve is stated, including data standardization, the calculation of the optimal clustering category number and the final clustering process. Then, the steps of using the typical operation databases and BP neural network to make predictions are presented. Using MATLAB, we predicted the outlet water temperature of a converter valve in Chuxiong Converter Station with F-BP method and two other existing methods in comparison. The results indicate that the proposed approach’s prediction accuracy increases by 0.9141 °C and 0.9938 °C respectively compared with the simple BP neural network and linear regression, which contributes to the prediction application of the outlet water of a converter valve.

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries.

In this paper, we propose a method to extract the gallbladder from ultrasound images by applying a method to dynamically adjust clusters of FCM based on histograms in the gallbladder ultrasound images. The proposed method analyzes the number of image features using a histogram in the gallbladder ultrasound image and dynamically sets the number of clusters of FCM using the number of analyzed features. And the fuzzy stretching technique is applied to the image corresponding to the R channel value in the original image. By applying the proposed FCM clustering technique to the stretched image, we improve the problem of gallbladder features being lost or noise being classified as gallbladder features. To analyze the performance of the proposed dynamic-based FCM technique, the performance of gallbladder extraction was analyzed for a total of 70 allbladder stone ultrasound images. In the existing FCM method, 61 gallbladder regions were extracted, and in the proposed method, 67 regions were extracted. [ABSTRACT FROM AUTHOR]

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Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives. [ABSTRACT FROM AUTHOR]

With the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long‐term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi‐order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C‐means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long‐term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue. [ABSTRACT FROM AUTHOR]

Teachers’ information technology application ability plays a crucial role in the long-term sustainable and benign development of colleges and universities. Aiming at the problems of fuzzy C-mean (FCM) clustering algorithm in data mining technology, this paper proposes the FCM algorithm optimized by combining the Canopy algorithm and the density peak algorithm and applies it to detect the level of information technology application ability of teachers in colleges and universities. Taking the University of Q as the research object, the fuzzy set qualitative comparative analysis (fs QCA) was used to explore the multiple driving mechanisms behind teachers’ IT application ability and then to explore the strategies to improve teachers’ IT application ability. The results showed that teachers’ IT application competence as a whole basically showed a decreasing trend with the increase of teaching age, among which teachers with 4-6 years of teaching age had the strongest “planning and preparation competence (3.177)”. In addition, in the qualitative comparative analysis, the consistency of the selected condition variables ranged from 0.618 to 0.886, which was lower than the critical value of 0.9, suggesting that the enhancement of teachers’ IT application ability should be comprehensively considered in terms of the concurrent synergistic effect of multiple conditions. The study aims to promote the steady enhancement of teachers’ information technology application ability, which is helpful in achieving the goal of education informatization enhancement.

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.