C-means Method Research Articles

Model of elemental data flow distribution in the internet of things supporting fog platform

The subject of the research is models and methods for optimizing resource and task management in the fog computing environment of the Internet of Things (IoT). The increasing number of connected devices and the volumes of data collected in IoT networks make it essential to improve management systems that ensure the optimal distribution of tasks and resources. Fog computing addresses this challenge by distributing computational tasks closer to data sources and end-users. The goal of this work is to enhance the efficiency of fog computing technologies to achieve optimal task and resource allocation in IoT networks. The main tasks of this work are as follows. Firstly, considering the diverse requirements of computational resources and tasks in IoT, reviewing existing methods and developments in the field is necessary. Secondly, it is essential to investigate and compare clustering methods, particularly DBSCAN and C-Means, for effective resource management. The DBSCAN clustering method enables efficient task distribution based on their location, while the C-Means method allows grouping resources based on their characteristics. The final task involves developing a mathematical model that considers input parameters such as system response, cluster resource requirements, data proximity to processing, etc. This model will enable the analysis of potential scenarios and decision-making regarding the optimal distribution of tasks and resources in the IoT environment. Conclusion. This research aims to solve the urgent problem of managing resources and tasks in the fog IoT environment. A review of existing methods and developments in resource and task management in IoT is conducted. DBSCAN and C-Means clustering methods are compared to determine their effectiveness in resource management. A set-theoretic model is developed that considers various parameters for making optimal decisions on the distribution of tasks and resources. It is established that the use of clustering methods and the developed model help to improve system performance and ensure more efficient use of fog computing resources in the IoT environment.

Fuzzy-Weighted Echo State Networks

A novel echo state network (ESN), referred to as a fuzzy-weighted echo state network (FWESN), is proposed by using the structural information of data sets to improve the performance of the classical ESN. The information is incorporated into the classical ESN via the concept of Takagi–Sugeno (TS) models/rules. We employ the fuzzy c-mean clustering method to extract the information based on the given data set. The antecedent part of the TS model is determined by the information. Then, we obtain new fuzzy rules by replacing the affine models in the consequent part of each TS rule with a classical ESN. Consequently, the output of the proposed FWESN is calculated through inferring these new fuzzy rules by a fuzzy-weighted mechanism. The corresponding reservoir is consisted of the sub-reservoirs of the new fuzzy rules. Furthermore, we prove that the FWESN has an echo state property by setting the largest spectrum radium of all the internal weight matrices in the sub-reservoirs less than one. Finally, a nonlinear dynamic system and five nonlinear time series are employed to validate the FWESN.

Human brain tumors detection using neutrosophic c-means clustering algorithm

For the last several decades, detecting human brain tumors has evolved into one of the most difficult problems in the field of medical research. In the realm of medical image processing, the categorization of brain tumors is a difficult job to do. In this research, we offer a model for the detection of human brain tumors in magnetic resonance imaging (MRI) images that makes use of the template-depend neutrosophic c-means and is compared with the fuzzy C means method. This model is referred to as the NCM method. In this suggested method, well first of all, the pattern K-means method is used to initialize segmentation markedly through the ideal choice of a template, depending on the gray-level intensity of the image; besides which, the revised membership is calculated by the ranges from the closest centroid to cluster pieces of data by using neutrosophic C-means (NCM) method while it approaches its perfect outcomes; and at last, the NCM clustering method is used for sensing tumor positron emission tomography (PET) imaging The findings of the simulation reveal that the suggested method can produce improved identification of pathological and normal cells in the human brain despite a little separation in the intensity of the grey level.

Research of reserves for increasing profitability of construction enterprises

The article analyzes the trends of financial results of construction companies during 2010 - 2019 by studying the slope of the trend line in the periods before and after 2014. The configuration features and the parameters of the equations of trend dependences of the share of profitable enterprises by type of construction activity. For the slope coefficients of the trend equations, a cluster analysis was performed using the C-means method, and the reliability of the results of the obtained cluster partitioning was proved. The criteria for restoring the profitability of the construction industry are substantiated, which are based on the results of a cluster analysis of the share growth of profitable enterprises in terms of the main areas of construction activity. Authors conducted a study of the segment of consumers that use services of small construction enterprises, represented by urban households. The results of it allowed, first, to structure potential consumers of services for overhaul and construction depending on their resources, and secondly, to quantify the impact of key components of cash flow and current consumer cost on purchasing power of the analyzed segment, thirdly, to establish reserves to increase the income of contractors in the segment and the possibility of reducing them. By constructing and analyzing the configuration of the distribution curve of expenditures on capital repairs and construction of urban households, its inconsistency with the conditions of the Pareto rule was established: instead of 20:80, about 20% of income in the construction market segment is provided by 47% of the poorest households. The other part, almost 80%, is formed by 53% of households with higher incomes. The threshold value of household income per 1 person is substantiated, failure to achieve which leads to savings in costs for repair and construction services. Its value is 6,000 UAH / month in 2019 prices.

Vectorized Kernel-Based Fuzzy C-Means: a Method to Apply KFCM on Crisp and Non-Crisp Numbers

Kernel methods are a class of algorithms for pattern analysis to robust them to noise, overlaps, outliers and also unequal sized clusters. In this paper, kernel-based fuzzy c-means (KFCM) method is extended to apply KFCM on any crisp and non-crisp input numbers only in a single structure. The proposed vectorized KFCM (VKFM) algorithm maps the input (crisp or non-crisp) features to crisp ones and applies the KFCM (with prototypes in feature space) on them. Finally the resulted crisp prototypes in the mapped space are influenced by an inverse mapping to obtain the prototypes’ (centers’) parameters in the input features space. The performance of the proposed method has been compared with the conventional FCM and KFCM and other new methods, to show its effectiveness in clustering of gene expression data and segmentation of land-cover using satellite images. Simulation results show good accuracy of proposed method in compare to other methods.

Hyperspectral image clustering with Albedo recovery Fuzzy C-Means

ABSTRACT Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy.

Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm

Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm

Perangkingan dan Pengelompokkan Perwira Calon Penerima Beasiswa S2 (Studi Kasus Perwira Profesi Informasi dan Pengolahan Data TNI Angkatan Laut)

The process of selecting scholarship recipients is a process that must be carried out in the context of providing educational scholarships. For the Navy, education is a very important thing in an effort to increase capability and professionalism in running an organization. The provision of educational scholarships for Navy personnel, especially in the environment of professional informatics, is carried out through selection of candidates' criteria that have been registered. Scholarship recipient data is processed to find the order of the candidates who will be determined as scholarship recipients. Prospective recipient data is considered by looking at certain criteria. Each criterion has a different weight. Based on the weights of each criterion, we can get weights that can be sorted according to certain priorities. For decision makers, many factors influence in determining a decision, so by using the Analytical Hierarchy Process (AHP) method and the Fuzzy C-Means (FCM) method a sequence and grouping of prospective scholarship recipients can be generated from each method. Based on the calculation results, in the AHP data that was entered as many as 747 candidate data, in the initial selection process obtained as many as 107 candidate data whose profession is informatics. All candidate data can be sorted based on the calculation of the assessment of eight criteria and ranking, from rank 1 to ranking 107. For processing time, the AHP method requires a longer time, which is ± 3 minutes, weighting process ± 1 minute, and ranking process ± 1 minute. Whereas in the FCM method, with the same amount of data it takes around ± 5 minutes until the iteration is complete or until the difference in objective function is smaller than epsilon. For the grouping results, this FCM method found that 43 candidates (40.19%) were strongly recommended, 31 candidates (28.97%) were recommended, and 33 candidates (30.84%) were not advised to receive scholarships with cluster validation in this study was 0.604. Hope in the future can simplify the calculation process for ranking and grouping selection, because it is already a computer application that is easy to use, and is expected to accelerate the selection process.

A spatial fuzzy C-means algorithm for segmentation of brain MRI images.

Brain and its structure are extremely complex with deep levels of details. Applying image processing methods of brain image can be very useful in many practical domains. Magnetic Resonance Imaging (MRI) is widely used imaging technique and has particular advantage by possessing the capability of providing highly detailed images of brain soft tissues than any other imaging techniques. The real challenge at hand for researchers is to perform precise segmentation while overcoming the effects of noise and other imaging artifacts like intensity in homogeneity introduced in medical images during image acquisition process. In this research work, a directional weighted optimized Fuzzy C-Means (dwsFCM) method has been proposed for segmentation of brain MR images. This method works by incorporating the spatial information of the pixels of the images and assigning the directional weights to the neighborhood. In order to validate the proposed segmentation framework, a comprehensive set of experiments have been performed on publically available standard simulated as well as real datasets. The experimental results showed 95% of accuracy and the performance of the proposed segmentation framework is much better and the framework suppress the sufficient amount of noise especially rician noise and reproduce good segmentation by overcoming the effect of intensity in homogeneity.

Revised Weighted Fuzzy C-Means and Fortified Weiszfeld Hybrid Method for Uncapacitated Multi-Facility Location Problems

In this article, a hybrid method is proposed to solve the uncapacitated planar multi-facility location problems. The new hybrid method consists of a combination of the Revised Weighted Fuzzy C-Means (RWFCM) algorithm proposed by Esnaf and Küçükdeniz (2013) and the Fortified Weiszfeld algorithm developed by Drezner (2015). The cluster centers and the cluster assignments of the RWFCM are fed into the Fortified Weiszfeld Algorithm separately for each cluster and facility-customer allocations are determined. The proposed approach is benchmarked on sample datasets from the facility location literature. Results of the proposed hybrid method show that the newly proposed sequentially-run method achieves better results when compared against the benchmark methods. This paper is a pioneer study of the hybrid use of Revised Weighted Fuzzy C-Means and Fortified Weiszfeld algorithms.

Development of a global positioning system data-based trip-purpose inference method for hazardous materials transportation management

The shipments of hazardous materials (hazmat) which are indispensable for economic and social development have increased; accordingly, a rising number of incidents involving hazmat transportation may inflict more dread damages to both people and environment. This severe situation has prompted the need for deep mining trip purposes using trajectory information in order to enhance the hazmat-transportation regulatory. This paper presents an unsupervised two-phase framework for inferring multiple trip purposes (i.e. loading, unloading, in-yard, and other stops) based on the passive global positioning system (GPS) data during the hazmat-transportation process. In detail, a scalable ordering points to identify the clustering-structure mixture algorithm (SOMA) is first developed to group hazmat vehicles trip ends into hotspot places in phase I; In phase II, a two-stage trip-purpose identification approach is proposed with a combination of the fuzzy c-means (FCM) method and the point-of-interest (POI) information. The effectiveness and efficiency of the designed two-phase framework are evaluated through the real-world datasets, which are generated by more than 12,000 vehicles in Liaoning Province, China. The results demonstrate that the method can infer four types of freight trip purposes with an accuracy of 82.1%. The proposed approach framework can help analyze the vehicle trips associated with the loading states, which will provide effective decision-making support for the hazmat-transportation regulatory.

Determination of Fertilizer Doses of Plants Utilizing the Results of Aerial Photographs Using Fuzzy Tsukamoto

Fertilizer dosage can be known through various methods including using digital image results. This study was taken from the results of aerial photographs with NDVI parameters, c-means and Fuzzy Tsukamoto methods which aimed to determine the dosage of fertilizer. The next step is to establish a rule base which is determined by the number of membership clusters in the c-means method and produces data that the quantity of membership cluster can determine the amount of fertilizer dose. The use of this method results in an accurate estimation of fertilizer dosage time of 97.7% compared to the conventional determination process.

Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter

Combining the search method for fire suppression routes with ant colony algorithms and methods of analyzing twitter events on the highway is the basis of the problems to be studied. The results of the twitter data feature extraction are classified with Support Vector Machine after it is implemented in the Simple Additive Weighting method in calculating path weights with criteria of distance, congestion, multiple branching, and many holes. Line weights are also used as initial pheromone values. The C-means method is used to group the weights of each path and distance so that the path with the lowest weight and the shortest distance that will be simulated using the Ant Colony. The validation results with cross fold on SVM with linear kernels produce the greatest accuracy value is 97.93% for training data distribution: test data 6: 4. The simulation of the selection of the damkar car path from Feather to Pleburan with Ant Colony obtained 50 seconds of computation time, whereas with Ant Colony with Clustering the computation time was 15, resulting in a reduction in computing of 35. Ant colony with MinMax optimization gives the best computation time of 14.47 seconds with 100 iterations and 10 nodes.

Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

Analysis of Selecting Gated Community as Opening Its Micro-Inter-Road Network

The opening of gated community to expand the micro-road network in the urban traffic system is a hot topic on the urban congestion. To satisfy the demand of opening early choosing case, this paper proposed a comprehensive selecting framework on qualified communities and its appropriate opening time. Firstly, the static influential factors on internal road structure, boundary road structure and traffic flow are qualitatively analyzed. Then, an evaluation opening state index system based on describing accurately traffic flow state is obtained, which takes the opening factors into account at the boundary road network. In this structure, the modified fuzzy C-means (FCM) method calculates the fuzzy entropy weight and range of each opening states index. Finally, the simulation results show that the proposed method is capable of selecting qualified community and the optimum opening time.

Inhomogeneity correction and hybrid-based segmentation in cardiac MRI

Image segmentation is an important step in medical image analysis and segmentation of ventricles in Cardiac Magnetic Resonance (CMR) images is challenging due to an in-built artefact called intensity-inhomogeneity. The short-axis cine Magnetic Resonance Images (MRI) recorded under a steady-state free precision protocol were corrected for intensity-inhomogeneity using Bias Corrected Fuzzy C-Means (BCFCM) method, Level Set (LS) and Multiplicative Intrinsic Component Optimisation (MICO) methods. The statistical measures show that bias correction by BCFCM has better performance than MICO and LS. In addition, the original and bias corrected images are validated by Multifractal Analysis (MFA). The results show that in bias corrected images, the low frequency components are removed thereby enhancing the sharpness of the ventricular boundaries. Further, ventricular segmentation is performed using the proposed automatic hybrid Sobel edge detector with optimised level set method. The validation parameters of segmented results show that the ventricular detection in bias corrected images matches better with ground truth.

Identification of Counterfeit Alcoholic Beverages Using Cluster Analysis in Principal-Component Space

A study of 153 brands of commercial vodka products showed that counterfeit samples could be identified by introducing a unified additive at the minimum concentration acceptable for instrumental detection and multivariate analysis of UV-Vis transmission spectra. Counterfeit products were detected with 100% probability by using hierarchical cluster analysis or the C-means method in two-dimensional principal-component space.

Efficient Model for Distributed Computing based on Smart Embedded Agent

Technological advances of embedded computing exposed humans to an increasing intrusion of computing in their day-to-day life (e.g. smart devices). Cooperation, autonomy, and mobility made the agent a promising mechanism for embedded devices. The work aims to present a new model of an embedded agent designed to be implemented in smart devices in order to achieve parallel tasks in a distribute environment. To validate the proposed model, a case study was developed for medical image segmentation using Cardiac Magnetic Resonance Image (MRI). In the first part of this paper, we focus on implementing the parallel algorithm of classification using C-means method in embedded systems. We propose then a new concept of distributed classification using multi-agent systems based on JADE and Raspberry PI 2 devices.

HEALTHY STATE MONITOR OF UPPER LIMB FOR SPACE FLIGHT TASK BASED ON SIGNAL ANALYSES OF MULTIPLE MUSCLE FORCES

A novel healthy state monitor method of upper limb for space flight task is proposed. Without taking other complex diagnosis equipment in orbit, this method only uses the ordinary exercise instruments to collect and analyze the multiple muscle forces of astronauts, and deduces where the serious muscle atrophy occurs in their muscle groups of upper limb. First, the typical multiple muscle forces data of upper limb are accumulated. A 45-day 6-degree head-down tilt bed rest experiment together with a multiple muscle forces test experiment are carried out to collect the corresponding data. These data include both the muscle force data of healthy state and the related data of unhealthy state. Second, the Wavelet Packet Transform (WPT) and the Empirical Mode Decomposition (EMD) methods are used to compute the signal features of these data above. Third, a Support Vector Machine (SVM) classifier is trained by the related signal features. Finally, the trained SVM can be utilized to evaluate the healthy state of upper limb in orbit for astronaut. If the output of SVM is negative, the C-means method and the Euclidean distance can be used to locate the abnormal muscle forces and muscle groups. The concept of typical muscle group health state evaluation for upper limb is emphasized in this paper. The comparisons among the traditional diagnosis-based method, the electromyogram (EMG)-based muscle forces analysis method, and the proposed method are made. Many experiment results on ground have verified the effectiveness of proposed method.

Non‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation

Owing to the existence of noise and intensity inhomogeneity in brain magnetic resonance (MR) images, the existing segmentation algorithms are hard to find satisfied results. In this study, the authors propose an improved fuzzy C-mean clustering method (FCM) to obtain more accurate results. First, the authors modify the traditional regularisation smoothing term by using the non-local information to reduce the effect of the noise. Second, inspired by the mechanism of the Gaussian mixture model, the distance function of FCM is defined by using the form of certain exponential function consisting of not only the distance but also the covariance and the prior probability to improve the robustness. Meanwhile, the bias field is modelled by using orthogonal basis functions to reduce the effect of intensity inhomogeneity. Finally, they use the hierarchical strategy to construct a more flexibility function, which considers the improved distance function itself as a sub-FCM, to make the method more robust and accurate. Compared with the state-of-the-art methods, experiment results based on synthetic and real MR images demonstrate its accuracy and robustness.