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The researches have been made on G-protein coupled receptors (GPCRs) over the long-ago decades. GPCR is also named as 7-transmembrane (7TM) receptor. According to biological prospective GPCRs consist of large protein family with respective subfamilies and are mediated by different physiological phenomena like taste, smell, vision etc. The main functionality of these 7TM receptors is signal transduction among various cells. In human genome, cell membrane plays significant role. All cells are made up of trillion of cells and have dissimilar functionality. Cell membrane composed of different components. GPCRs are reported to be modulated by membrane cholesterol by interacting with cholesterol recognition amino acid consensus (L/V-X (1-5)-Y-X (1-5)-R/K) (CRAC) or reverse orientation of CRAC (R/K-X (1-5)-Y-X (1-5)-L/V) (CARC) motifs present in the TM helices. Among all, cholesterol is one who is regulated by membrane proteins. Here we took GPCR as membrane proteins and this protein modulates membrane cholesterol. According to cell biology, GPCR regulates a wide diversity of vital cellular processes and are targeted by a huge fraction of approved drugs. In this paper we have concentrated our investigation on membrane protein with membrane cholesterol. A hybrid algorithm consisting of spectral clustering and support vector machine is proposed for prediction of membrane cholesterol with GPCR. Spectral clustering uses graph nodes for calculating the cluster points and also it considers other concept such as similarity matrix, low-dimensional space for projecting the data points and upon this parameter at last construct the cluster centre. Supervised learning method is used for solving regression and classification problems. From the analysis we found that our result shows better prediction accuracy in terms of time complexity when compared with two existing models such as fuzzy c-means (FCM) and rough set with FCM model

The performance assessment of strategic levels of an integrated management system (IMS) is critical to policy making phase of executive organizations. Because, the integrity of objectives and strategies in the overall system performance insures the integrity of activities and performance in the subsidiary departments and units. Unfortunately, this approach of assessment has been rarely focused. Here, the Fuzzy Cognitive Mapping-Relative Degree Analysis (FCM-RDA) technique was chosen to assess the performance trend of the health, safety, environment, and energy (HSEE)-IMS in a ministry during 2014-2019. There were 12 HSEE-IMS strategic sub-elements and 6 system sub-performances. The Gephi0.9.2 software was applied to simulate the FCM model. Accordingly, the sub-performance of policy and leadership was placed at the first priority. The development of HSEE management, and review (of policies and programs) were two HSEE-IMS sub-elements, categorized in the second priority. The results showed an ascending trend of the system performance during 2014-2018 and a descending trend during 2018-2019. It was found that the poor performance of HSEE-IMS sub-elements in the strategic and tactical levels resulted in the poor performance of system at the operational levels. There was found a strong relationship between the HSEE-IMS sub-elements and system sub-performances.

Neutrosophic Cognitive Maps (NCM) decision-making system encompasses sequential tasks of resolving the confrontations in finding the ideal solution. This paper introduces neutrosophic sociogram (NS) approach as an alternative to the conventional approach of NCM in finding the fixed point of the dynamical NCM system. A comparative analysis is made between two approaches and the efficiency of the proposed approach is validated with an application to find the interrelationship between the persuading eleven factors of Technopreneurship. It was observed that the results obtained using the proposed approach is more compatible, feasible and simple than the conventional approach in making decisions on the implications between the factors. The two approaches are programmed using SWIFT language to make the computations easier and the results are in consensus with the manual calculations.

Artificial intelligence applications are becoming increasingly popular and are producing better results in many areas of research. The quality of the results depends on the quantity of data and its information content. In recent years, the amount of data available has increased significantly, but this does not always mean more information and therefore better results. The aim of this work is to evaluate the effects of a new data preprocessing method for machine learning. This method was designed for sparce matrix approximation, and it is called semi-pivoted QR approximation (SPQR). To best of our knowledge, it has never been applied to data preprocessing in machine learning algorithms. This method works as a feature selection algorithm, and in this work, an evaluation of its effects on the performance of an unsupervised clustering algorithm is proposed. The obtained results are compared to those obtained using, as preprocessing algorithm, principal component analysis (PCA). These two methods have been applied to various publicly available datasets. The obtained results show that the SPQR algorithm can achieve results comparable to those obtained using PCA without introducing any transformation of the original dataset.

Medical image segmentation plays an indispensable role in the identification of articular cartilage, tibial and femoral bones from magnetic resonance imaging (MRI). There are various image segmentation strategies that can be used to identify the knee structures of interest. Among the most popular are the methods based on non-hierarchical clustering, including the algorithms K-means and fuzzy C-means (FCM). Although these algorithms have been used in many studies for regional image segmentation, they have two essential drawbacks that limit their performance and accuracy of segmentation. Firstly, they rely on a precise selection of initial centroids, which is usually conducted randomly, and secondly, these algorithms are sensitive enough to image noise and artifacts, which may deteriorate the segmentation performance. Based on such limitations, we propose, in this study, two novel alternative metaheuristic hybrid schemes: non-hierarchical clustering, driven by a genetic algorithm, and Particle Swarm Optimization (PSO) with fitness function, which utilizes Kapur’s entropy and statistical variance. The goal of these optimization elements is to find the optimal distribution of centroids for the knee MR image segmentation model. As a part of this study, we provide comprehensive testing of the robustness of these novel segmentation algorithms upon the image noise generators. This includes Gaussian, Speckle, and impulsive Salt and Pepper noise with dynamic noise to objectively report the robustness of the proposed segmentation strategies in contrast with conventional K-means and FCM. This study reveals practical applications of the proposed algorithms for articular cartilage extraction and the consequent classification performance of early osteoarthritis based on segmentation models and convolutional neural networks (CNN). Here, we provide a comparative analysis of GoogLeNet and ResNet 18 with various hyperparameter settings, where we achieved 99.92% accuracy for the best classification configuration for early cartilage loss recognition.

Ex vivo confocal laser scanning microscopy (ex vivo CLSM) is a novel diagnostic tool for a quick bedside evaluation of freshly excised tissue, comparable to histology. We aimed to assess the sensitivity and specificity of ex vivo CLSM in detecting malignant features, to validate its reliability in identifying various skin tumours based on a combination of confocal features and to evaluate the digital staining mode (DS). One-hundred twenty freshly excised skin samples from 91 patients were evaluated. Each lesion was screened for the presence of 23 predefined confocal criteria with ex vivo CLSM, followed by a histopathological examination. The diagnostic agreement between ex vivo CLSM and histology was 89.2%. The diagnostic accuracy of ex vivo CLSM in detecting malignancy reached a sensitivity of 98% and a specificity of 76%. Ex vivo CLSM enabled a rapid identification of the most common skin tumours, the tumour dignity and cytological features. The DS demonstrated a close resemblance to conventional histopathology.

In automatic fingerprint identification system (AFIS), fingerprint segmentation plays a crucial role in improving detection accuracy and reducing the computation time of feature extraction. With the goal of refining performance of AFIS, in this paper, we investigate a novel dynamic fingerprint segmentation algorithm. The proposed algorithm is based on the existed dynamic image segmentation algorithm using fuzzy c-means (FCM) and genetic algorithm. Specifically, relying on different gray level of histogram and improved post-processing method, we establish a well-performed fingerprint segmentation system. The extensive results from our empirical experiments demonstrate the high performance of our proposed dynamic fingerprint segmentation algorithm, and its better performance than other competing approaches.

Segmentation of pulmonary nodule in thoracic computed tomography (CT) plays an important role in the computer-aided diagnosis (CAD) and clinical practices. However, segmentation of pulmonary nodules still remains a challenging task due to the presence of intrinsic noise, low contrast, intensity-profile inhomogeneity, variable sizes and shapes. Many variants and extensions of fuzzy C-mean (FCM) clustering algorithm have been developed to preserve image details as well as suppress image noises. However, these variants overemphasize the importance of the spatial information and neglect the role of the prior knowledge. To address this problem, a GMM fuzzy C-means (GMMFCM) algorithm is proposed for the segmentation of pulmonary nodules in this paper. A novel local similarity measure is defined by using local spatial information and GMM statistical information. A neighboring term is added to the energy function of traditional fuzzy C-mean algorithm. A superpixel-based random walker is proposed to segment pulmonary parenchyma, which reduces the computational complexity and improves the segmentation performance. Experiments performed on the LIDC dataset and the GHGZMCPLA dataset demonstrate that the segmentation performance of proposed GMMFCM algorithm is superior to the state-of-the-art algorithms.

The traditional fuzzy C@hyphenmeans (FCM) algorithm ignores the spatial information of pixels and is sensitive to noise images. To overcome this disadvantage, this paper proposes a fuzzy C@hyphenmeans clustering algorithm based on energy noise detection (ENDFCM) for mammographic image segmentation. Based on the weighted mean filtering, the algorithm employs the information of the spatial pixel points to enhance its noise resistance. By introducing the energy curve and designing the spatial distance, it constrains the objective function to increase its parameter adaptability. Then, it derives the new subordinate function to increase the relevance of its subordinacy, which is updated continuously during the clustering process. The experimental results show that the algorithm has better segmentation accuracy and robustness compared with the traditional FCM and its improved methods, and can accurately segment the noisy breast tumor images.

The on-going offshore outsourcing processes have resulted in complex, global and more vulnerable supply chain to disruptions. However, a good supplier choice would preserve or even improve supply chain resilience. Despite this critical potential effect, this topic remains relatively underdeveloped in the literature. Accordingly, this study proposes a coupled method based on Fuzzy Cognitive Maps (FCM) and Analytic Hierarchy Process (AHP). The final model shows the impact of locational decision in offshore outsourcing process on supply chain resilience. Moreover, it allows simulating locations scenarios over time through an inference process. The simulations foresee the impacts of three alternative locations on capabilities required in a resilient supply chain. The sensitivity analysis of the findings reveals that one location would improve supply chain resilience meanwhile the others would damage it. This FCM-AHP analysis enhances the understanding of academics and practitioners about the importance of locations criteria and their influence in the supply chain resilience capabilities.

Background: Vascular dementia (VD) is the second most common type of dementia after Alzheimer’s disease, but there is a lack of definitive treatment for VD. Acupuncture treatment is effective in improving the cognitive impairment and behavioral capacity of patients with VD. In recent years, more studies indicated that peripheral inflammation and abnormal peripheral immune function may aggravate neuroinflammation and cognitive dysfunction. However, there are few studies about the acupuncture and the abnormal peripheral immune function of VD. Also, few studies concern the regulating effect of acupuncture on peripheral immunity of patients with VD.Objective: The aim of this study was to explore the effect of the “sanjiao” acupuncture method on peripheral immunity of patients with mild to moderate VD.Methods: A total of 30 patients with VD were involved in the acupuncture group (AG), which was treated with the “sanjiao” acupuncture method once a day for six times a week and lasted for 12 weeks, and 30 healthy elderly people were assigned to the normal group (NG), which had no treatment. The distribution of lymphocyte subsets and the levels of some inflammatory cytokines in the peripheral blood of subjects were evaluated using the flow cytometry (FCM) and the enzyme-linked immunosorbent assay (ELISA).Results: A total of 60 subjects were involved in this study, while 58 subjects completed the entire trial. Before treatment, the levels of CD3+ T, CD4+ T cells, CD4+/CD8+, Tregs, B cells, IFN-γ, and IL-10 in patients with VD were significantly decreased compared with the normal group (all P < 0.05 or P < 0.01). The level of TNF-α in peripheral blood of patients with VD was significantly increased (P < 0.01). After acupuncture treatment, the levels of CD3+ T, CD4+ T cells, and IFN-γ were significantly increased (all P < 0.05 or P < 0.01). The level of TNF-α was significantly decreased (P < 0.01). The proportion of Tregs was increased (P < 0.01), but it was still lower than that of the normal group (P < 0.05).Conclusion: The acupuncture method can increase the proportion of CD3+, CD4+ T cells, and Tregs in peripheral blood of patients with VD. And, it reduces the levels of pro-inflammatory factor TNF-α, which achieves the anti-inflammatory effects and immunostimulation. It suggests that acupuncture can improve the peripheral immune dysfunction of patients with VD by regulating the distribution of lymphocyte subsets and the levels of inflammatory cytokines.Clinical Trial Registration: [www.chictr.org.cn], identifier [ChiCTR-IOR-17012052].

The number of people with lung cancer has reached approximately 2.09 million people worldwide. Out of 9.06 million cases of death, 1.76 million people die due to lung cancer. Lung cancer can be automatically identified using a computer-aided diagnosis system (CAD) such as image processing. The steps taken for early detection are pre-processing feature extraction, and classification. Pre-processing is carried out in several stages, namely grayscale images, noise removal, and contrast limited adaptive histogram equalization. This image feature extracted using gray level co-occurrence matrix (GLCM) and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN). This research aims to obtain the best neural network model to classify lung cancer a. Based on training time and accuracy, the best method of FFNN is kernel extreme learning machine (KELM), with a training time of 12 seconds and an accuracy of 93.45%, while the best method of FBNN is Backpropagation with a training time of 18 minutes 04 seconds and an accuracy of 97.5%.

To complete groundwater diversion, the complex flow law of groundwater in rocks must be investigated so that groundwater diversion can be improved. This research uses the computer finite element method (FEM), CT scanning calculation method, Avizo method, and digital core technology combined with the Fluent calculation method (FCM) to reconstruct rocks with microscopic pore structures on a computer. The numerical simulation results under different conditions show that: the total pressure change gradually decreases under different pressure gradients. In a seepage channel, the seepage path does not change with the change in seepage pressure, and the seepage velocity is the largest in the center of the pore. The longer the seepage path is, the greater the decrease in seepage velocity. Different seepage directions have similar seepage laws. The research results provide effective guidance for the project to control groundwater.

WOS: 000474400800003 In this paper, we introduced a novel image clustering method based on combination of the classical Fuzzy C-Means (FCM) algorithm and Backtracking Search optimization Algorithm (BSA). The image clustering was achieved by minimizing the objective function of FCM with BSA. In order to improve the local search ability of the new algorithm, an inertia weight parameter (w) was proposed for BSA. The improvement was accomplished by using w in the steps of the determination of the search-direction matrix of BSA and the new algorithm was named as w-BSAFCM. In order to show the effectiveness of the new algorithm, FCM was also combined with the general forms of BSA in the same manner and three benchmark images were clustered by utilizing these algorithms. The obtained results were analyzed according to the objective function and Davies-Bouldin index values to compare the performances of the algorithms. According to the results, it was shown that w-BSAFCM can be effectively be used for solving image clustering problem. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Recently, passive detection technology has developed the ability to detect surface ships based on the noise emissions recorded by hydrophones, making it possible in some cases to classify surface ships. One of the most concerning issues with ships and underwater targets is the current lack of reliable features for unsupervised classification. To solve this problem, this paper proposes an improved Mel-frequency cepstral coefficients (IMFCC) feature for unsupervised clustering of marine targets. As the feature extraction of hydrophone signals rely on preprocessing, the IMFCC adds cyclic modulation spectrum (CMS) and cross-correlation bispectrum (CCB) in the preprocessing module before traditional the Mel-frequency cepstral coefficient process, and the principal component analysis (PCA) is added as the backend processing module. There are four contributions as follows: First, for IMFCC extraction, it combines the advantages of the CMS, CCB, MFCC, and PCA. Second, the two common unsupervised clusters, Gaussian mixture model (GMM) and fuzzy C-means are used to evaluate the CMS, CCB, and several MFCCs-MFCC-vector quantization (MFCC-VQ), MFCC-Gaussian mixture model (MFCC-GMM), TEO-MFCC (Teager Energy Operator based MFCC), and IMFCC. Third, the performances of traditional MFCC, Teager-energy operator (TEO)-MFCC, IMFCC, MFCC-VQ, and MFCC-GMM are discussed under different dimensions, signal-to-noise ratios, distances, and depths. Finally, the experimental results prove that the IMFCC method has a strong anti-interference ability and is robust and has a high-success rate for clustering multitarget or different depth targets, which enables the IMFCC to be a reliable feature for unsupervised classification.

[EN] The cosmic microwave background (CMB) radiation was emitted at very early times, shortly after the Big Bang. It reaches the earth from every direction and, even though it has become weak and has suffered some changes on its way, it gives important information on how the universe was and how it is evolving. The radiation from the CMB is polarized, and different information can be extracted from each polarization mode. In particular, the detection of primordial B-modes would imply the existence of a primordial background of gravitational waves, which is predicted by inflation, and would in turn greatly reinforce this theory. The B mode of polarization is very weak, and its observations are very contaminated from Galactic diffuse emission. This project has focused on creating a component separation method using neural networks (NNs) to recover the signal of the CMB from multi-frequency observations of the full sky containing thermal dust and synchrotron foregrounds. The NN models have been optimized for observations of the satellite LiteBIRD, due to be launched in 2027 and whose goal is precisely to obtain measurements of the primordial B-modes, training the models with simulations following specifications of that instrument. Promising results have been obtained, with relative errors of the predictions around 11% in the region of the sky with less intense foregrounds, better than with a standard technique such as ILC. This study suggests that a robust component separation method could be obtained with NNs; still, more has to be analyzed regarding the models and the residuals from foregrounds and noise that are produced, as well as the response of the networks to different foregrounds., [ES] La radiación del fondo cósmico de microondas (FCM) fue emitida poco después del Big Bang. Llega a la tierra desde todas las direcciones y, aunque se ha hecho más débil y ha sufrido algunos cambios por el camino, da información muy valiosa sobre como era el universo y como esta evolucionando. La radiación del FCM está polarizada, y distinta información se puede obtener de cada modo. En particular, la detección de los modos B primordiales implicaría la existencia de un fondo primordial de ondas gravitacionales, el cual esta predicho por inflación, y reforzaría fuertemente esta teoría. El modo B de polarización es muy débil, y sus observaciones vienen muy contaminadas por emisión galáctica difusa. Este proyecto se ha centrado en crear un método de separación de componentes usando redes neuronales (RNs) para recuperar la señal del FCM de observaciones a todo el cielo a distintas frecuencias que contienen componentes de emisión de polvo térmico y synchrotron. Las RNs han sido optimizadas para observaciones del satélite LiteBIRD, programado para ser lanzado en 2027 y cuyo objetivo es precisamente la medición de los modos B primordiales, entrenando los modelos con simulaciones siguiendo las especificaciones de ese instrumento. Se han obtenido resultados prometedores, con errores relativos de las predicciones cerca del 11 % en la región del cielo con contaminación menos intensa, mejor que con una técnica estándar como el ILC. Este estudio sugiere que un método de separación de componentes robusto se podría obtener con RNs; aun así, los modelos y los residuos de contaminantes y ruido que se producen deben de ser analizados en más profundidad, así como la respuesta de las redes a distintos tipos de contaminantes.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025 2-s2.0-85097677344 Mobile robotics is a valuable tool for exploring environments inaccessible to humans due to their remoteness, cost or danger, and for performing unpleasant or laborious tasks. It is a relatively new field, until recently experimental, but it is already being applied to real problems with satisfactory results, 3D simulation plays a relevant role in both the design and control of mobile robots. It allows to reflect the situation of the real robot or simulate hypothetical scenarios. At the same time, it provides a graphical interface for robot control, in this paper, we present a scheme for guiding a robot into an uncharted area by using the fuzzy c-means segmentation algorithm and k-NN classification to classify the safe and unsafe areas, our method proves to be fast with satisfactory results in terms of both segmentation, classification, and correct path planning. © 2020 IEEE.

For reconstructing CT images in the clinical setting, "effective energy" is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting of the skull, rib bone, and lung tissues was irradiated with CT configuration in GATE/GEANT4. We applied inverse Radon transform to the obtained Sinogram to construct a Pixel-based Attenuation Matrix (PAM). PAM was then used to weight the calculated Hounsfield unit scale (HU) of each interval's representative energy. Finally, we multiplied the associated normalized photon flux of each interval to the calculated HUs. The performance of the proposed method was evaluated in the course of Complexity and Visual analysis. Entropy measurements, Kolmogorov complexity, and morphological richness were calculated to evaluate the complexity. Quantitative visual criteria (i.e., PSNR, FSIM, SSIM, and MSE) were reported to show the effectiveness of the fuzzy C-means approach in the segmenting task.

Infertility is a major problem that directly affects many people worldwide. In recent years, fertility rates decreased up to 15% in young men. Changing live conditions, stress level, environmental factors, nutritional habits play an important role on infertility. This paper suggests a novel method estimating semen quality from lifestyle, environmental factors and daily habits using the radial basis function neural networks. The accuracy of the suggested method measured as 90%. The result shows that the proposed method can be applied as a practical, fast and cheap alternative method to the laboratory tests. © 2018 International Association of Computer Science and Information Technology.

The progressive chemicalization of all areas of everyday life and the development of the industry cause the appearance of various types of pollutants, both in groundwater and surface waters. Kalina Pond (Świętochłowice, Poland) is an example of a degraded water reservoir as a result of many years of activity, among others hard coal mines, storing metallurgical waste by zinc plants, and the activities of the Hajduki Chemical Plants from Chorzów. Inadequate securing of waste heaps resulted in the penetration of pollutants, i.e., phenol, petroleum compounds, PAHs, cyanides, and heavy metals. The aim of the research was to determine the suitability of biopreparations for the removal of pollutants. The research used a bacterial biopreparation from BioArcus, “DBC plus type R5”, to remove petroleum compounds and phenol. Then, in order to restore the microbiological balance, “ACS ODO-1” from the biopreparation was used. The research was carried out in laboratory conditions, using three variants: direct dosing of biopreparations, dosing of biopreparations previously activated by multiplication on the medium, and dosing of biopreparations into water after filtration on a diatomite bed. The optimal method of recultivating water from a reservoir was to filter this water through a diatomite bed and then dose the multiplied bacteria. After the filtration process, the obtained percentage of TOC reduction allowed for the rapid development of microorganisms from the biopreparation, despite the 100 times lower dose used. In addition, the application of lyophilized biopreparation to contaminated water resulted in a very fast biodegradation effect of pollutants, despite the high concentration of numerous toxic compounds.

Rising demand for optimized digital marketing strategies has led firms in a hunt to harvest every possible aspect indicating users’ experience and preference. People visit, regularly through the day, numerous websites using both desktop and mobile devices. For businesses to acknowledge device’s usage rates is extremely important. Thus, this research is focused on analyzing each device’s usage and their effect on airline firms’ digital brand name. In the first phase of the research, we gathered web data from 10 airline firms during an observation period of 180 days. We then proceeded in developing an exploratory model using Fuzzy Cognitive Mapping, as well as a predictive and simulation model using Agent-Based Modeling. We inferred that various factors of airlines’ digital brand name are affected by both desktop and mobile usage, with mobile usage having a slightly bigger impact on most of them, with gradually rising values. Desktop device usage also appeared to be quite significant, especially in traffic coming from referral sources. The paper’s contribution has been to provide a handful of time-accurate insights for marketeers, regarding airlines’ digital marketing strategies.

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.

The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.

Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.

Paper and coin are the two most common currencies in all over the world. In Ethiopia also paper and coin currency are used for medium of exchange. This paper presents the comparative study of segmentation techniques towards Ethiopian paper currency classification. Otsu, FCM and K-means segmentation techniques are considered for this study and BPNN is used for classification of currencies. For the classification, images are collected from commercial bank of Ethiopia and Dashen Bank; for our data set, a total of 500 images samples were collected. From these images, 91.2% accuracy is achieved when Otsu segmentation is used on BPNN with TANH learning function.

Paperboard used as packaging, a non-inert material, can transfer chemicals into food. Over the years, endocrine disrupting compounds (EDCs), such as NonylPhenols (NPs), BisPhenol A (BPA) and phthalates have been shown to migrate from packaging materials into food. Due to chronic exposure and mixture effects of these EDCs, they could cause health effects even at very low doses. Many EDCs are still unknown and many more are still unregulated. The ERE-CALUX bioassay was used as a bioanalytical tool to investigate estrogenic activities of paperboard food packaging and its characteristics, including recycling rate and printing ink. A “worst case” scenario with full extraction is compared to a dry food migration experiment. By measuring an overall estrogenic activity, known and unknown estrogenic chemicals and mixture effects are taken into account and the data are compared to molecule specific analysis. Estrogenic activities ranged from 682 ± 66 pg E2 eq./dm2 to 3250 ± 400 pg E2 eq./dm2 for “worst case” extraction and from 347 ± 30 pg E2 eq./dm2 to 1350 ± 70 pg E2 eq./dm2 for migration experiments. A two-factor ANOVA revealed a relationship between estrogenic activity and the recycling rate of the paperboard, but no significant difference with printing ink was observed for these paperboard samples. Bis(2-ethylhexyl)phthalate (DEHP), dibutyl phthalate (DBP), butyl benzyl phthalate (BBP) and 1,2-cyclohexane dicarboxylic acid diisononyl ester (DINCH) were determined in all extraction and migrations experiment samples. A Spearman rank correlation analysis showed a relationship between the estrogenic activity and the total phthalates as well as with each compound individually.

Objective: This experiment was undertaken to assess the scope and cost-effectiveness of the fer¬mented corn mixture (FCM)-based buffalo fattening approach compared to urea molasses straw (UMS) and silage-based approach. Materials and Methods: A completely comparative randomized design experiment was con¬ducted for 90 days with three treatments and five buffalo bulls in each. UMS, silage, and FCM roughage-based fattening diets were attributed as T1, T2, and T3, respectively. Two types of protein supplements, i.e., Type 1 (Bangladesh Livestock Research Institute-developed) for T1 and T2 and Type 2 (prescribed by farmers) for T3 treatments, were used. All the parameters were analyzed through Statistical Package for the Social Sciences, 20 software. Results: Dry matter intake (kg, %live weight) was significantly higher in T1 (2.65), followed by T2 (2.34) and T3 (2.00), respectively. The crude protein intake, digestible crude protein intake, and digestible dry matter intake (kg/d) significantly (p < 0.05) differed between T1 and T3, but not T2. The digest¬ibility of acid detergent fiber (65.97) was significantly higher for T3 than T1 and T2 (54.44 and 58.73, respectively). Neutral detergent fiber digestibility of T3 (70.35) also differed (p < 0.05) with T1 (60.97) but not T2 (64.78). No difference was observed in the case of growth, but feed conversion ration was found to be significantly (p < 0.05) better in T2 (7.10) than T1 (8.35), where T3 (7.24) was neutral. The significantly (p < 0.001) highest expense [216.37 Bangladesh taka (BDT)/kg gain] was required for T1, followed by T2 and T3 (174.47 and 126.33 BDT/kg gain, respectively). Net profit from T3 and T2 (15,877 and 15,175 BDT, respectively) gained significantly (p < 0.05) higher than T1 (11,265 BDT). Conclusion: The FCM-based diet was suitable and cost-effective as a buffalo fattening approach. [J Adv Vet Anim Res 2021; 8(2.000): 195-202]

The aim of this paper was to develop a region based active contour model and Fuzzy C-Means (FCM) technique for segmentation of lung nodules. Ultimately, detection and assisted diagnosis of nodules at earlier stage increase the mortality rate. Among many imaging modalities, Computed Tomography (CT) is being the most sought because of its imaging sensitivity, high resolution and isotropic acquisition in locating the lung lesions. The proposed methodology focuses on acquisition of CT images, reconstruction of lung parenchyma and segmentation of lung nodules. Reconstruction of parenchyma can be employed using selective binary and Gaussian filtering with new signed pressure force function (SBGF-new SPF) and clustering technique was used for nodule segmentation. Comparative experiments demonstrate the advantages of the proposed method in terms of decreased error rate and increased similarity measure.

Fuzzy clustering is useful clustering technique which partitions the data set in fuzzy partitions and this technique is applicable in many technical applications like crime hot spot detection, tissue differentiation in medical images, software quality prediction etc. In this review paper, we have done a comprehensive study and experimental analysis of the performance of all major fuzzy clustering algorithms named: FCM, PCM, PFCM, FCM-σ, T2FCM, KT2FCM, IFCM, KIFCM, IFCM-σ, KIFCM-σ, NC, CFCM, DOFCM. To better analysis their performance we experimented with standard data points in the presents of noise and outlier. This paper will act as a catalyst in the initial study for all those researchers who directly or indirectly deal with fuzzy clustering in their research work and ease them to pick a specific method as per the suitability to their working environment.

The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

Digital forensics faces several challenges in examining and analyzing data due to an increasing range of technologies at people’s disposal. The investigators find themselves having to process and analyze many systems manually (e.g. PC, laptop, Smartphone) in a single case. Unfortunately, current tools such as FTK and Encase have a limited ability to achieve the automation in finding evidence. As a result, a heavy burden is placed on the investigator to both find and analyze evidential artifacts in a heterogenous environment. This paper proposed a clustering approach based on Fuzzy C-Means (FCM) and K-means algorithms to identify the evidential files and isolate the non-related files based on their metadata. A series of experiments using heterogenous real-life forensic cases are conducted to evaluate the approach. Within each case, various types of metadata categories were created based on file systems and applications. The results showed that the clustering based on file systems gave the best results of grouping the evidential artifacts within only five clusters. The proportion across the five clusters was 100% using small configurations of both FCM and K-means with less than 16 % of the non-evidential artifacts across all cases – representing a reduction in having to analyze 84% of the benign files. In terms of the applications, the proportion of evidence was more than 97%, but the proportion of benign files was also relatively high based upon small configurations. However, with a large configuration, the proportion of benign files became very low less than 10%. Successfully prioritizing large proportions of evidence and reducing the volume of benign files to be analyzed, reduces the time taken and cognitive load upon the investigator.

In this paper, a review about different algorithm is proposed efficiently to segment the satellite images. Segmentation of Image is one of the promising and active researches in recent years. As literature prove that region segmentation will produce better results. Human visual perception is more effective than any machine vision systems for extracting semantic information from image. There are various segmentation techniques are available. Fuzzy C Means (FCM), Expectation Minimization (EM) and K-Means algorithm is developed to estimate parameters of the prior probabilities and likelihood probabilities. Finally Peak Signal to Noise Ratio (PSNR) is calculated for all the algorithms and reviewed.

With the rapid advancement of IT, more and more attempts have been made to incorporate this modern technology into education. Of the newest teaching and learning devices and approaches, the use of microlectures and the idea of flipped classroom model(known as FCM) have caught the attention of many teachers. While the new teaching device and the innovative teaching approach succeed in some teachers’ classes, they are doomed to failure in others’ for this new approach involves prerequisites of which many teachers might not be fully aware of before their teaching practice, the lack of which might become barriers. This paper presents a post hoc analysis of a failed case with FCM and attempts to discuss, at a macroscopic level, problems with preconditions for FCM that currently exist, in the hope that teachers who are making an attempt at FCM can be aware of these problems and thus would make better preparation for it so as not to repeat the failure should they conduct FCM in their teaching. As are revealed by a detailed analysis of the reasons and a further deliberation on the failure with regard to a successful case, students’ inadaptability to unmonitored e-learning, teachers’ inadequate knowledge of computer science and information technology, the lack of a desirable communication platform and restrictive syllabuses may all hinder the smooth application of FCM, among which, the lack of a desirable communication platform and teachers’ lack of adequate knowledge of computer science and information technology have become a bottleneck in the application of FCM.

The Sinos River basin (SRB) is one of the most polluted river basins in Brazil, leading to considerable efforts to mitigate the impacts and achieve their recovery, is possible through adequate integral management. Aiming at the need for water quality management through the analysis of the interrelationships among the different factors.

En la época en la que vivimos las aplicaciones móviles y web responsivas van ganando terreno sobre las aplicaciones tradicionales y páginas pensadas sólo para ordenadores. Hoy en día el teléfono móvil se ha convertido en un imprescindible en la vida de la gente y las aplicaciones que nos facilitan el acceso de forma cómoda a todo tipo de servicios, restaurantes, cosmética, viajes, etc. han ido creciendo de manera exponencial. Los taxistas de “Taxi Piloña S. COOP.”, han pensado que ha llegado el momento de subirse al carro de las nuevas tecnologías de una manera más activa y eficiente, facilitando el acceso a sus servicios a clientes y la gestión de estos a ellos mismos. Por esto, desean una aplicación móvil en las que los clientes puedan registrarse y en su perfil de cliente solicitar un servicio. Por otra parte, un servidor se encargará de geolocalizar a los taxistas que estén disponibles y enviarles una petición de servicio empezando por los más cercanos al cliente. La aplicación móvil para el taxista, aparte de poder rechazar o aceptar la solicitud, podrá ver en un mapa cuál es su posición respecto al cliente y podrá ver la ruta y ser guiado. También dispondrá de un botón de socorro que en caso de peligro enviará un aviso a sus compañeros. En cuanto al cliente, podrá escoger en un mapa en que ubicación quiere que el taxista le recoja y podrá saber cuánto tiempo aproximado le espera para ser recogido ya que cada cierto tiempo el taxista enviará su posición a un servidor que mostrará esta ubicación en la aplicación del cliente.

This study aimed to investigate the influence of enrofloxacin, florfenicol, ceftiofur and E. coli LPS interaction on T and B subsets in thymus and spleen of newly-hatched chicks. A 126, 1-day-old chicks were administered enrofloxacin, florfenicol or ceftiofur in recommended doses according to the currently treatment schedule advises. E .coli LPS was given intravenously once at the dose of 200 μg kg(-1) BW on the 2nd day of experiment (d. e.). On the 6th and the 14th d. e. thymus and spleens were subjected to flow cytometry investigation.The most significant changes were demonstrated in spleen. The antibiotics administration decreased the percentage of B and T cells subset. Moreover, this suppressive effect was enhanced by E. coli LPS administration. On the 6th d. e. the percentage of CD3(+)TCRγδ(-), CD3(+)TCRγδ(+), CD4(+)CD8(-), CD4(-)CD8(+) decreased significantly after ceftiofur and LPS treatment. A lower percentage of CD3(+)TCRγδ(-), CD4(-)CD8(+) and CD3(+)TCRγδ(+) was observed in enrofloxacine and LPS treated group. The decrease percentage of CD3(+)TCRγδ(+)cells and Bu-1(+) was found after florfenicol and LPS treatment. On the 14th d. e. a decreased percentage of CD4(+)CD8(-) and increased percentage of CD4(-)CD8(+) cells was shown in ceftiofur or enrofloxacine and LPS treated groups. In addition decreased percentage of CD3(+)TCRγδ(+) was found in all antibiotic and LPS treated groups.In this study, it was shown that enrofloxacine, florfenicol, ceftiofur treatment may change the proportions among lymphocytes subset and might have an impact on the immune response to bacterial endotoxins in chicks.

Clustering techniques in wireless sensor networks enables energy efficient coordination among the densely deployed nodes for data delivery till the base station. Many clustering protocols have been suggested in the recent past. The topology of the nodes, mostly seen in the literature, is of random type. This paper analyzes the performance aspects of various centralized clustering techniques for wireless sensor networks. LEACH-Centralized, KMeans-CP, FCM-CP and HSA-CP protocols have been compared with respect to clustering and data delivery process for various realistic topologies. The simulations were performed for these protocols and performance of the protocols has been critically analyzed. HSA-CP clustering method performs better compared to other techniques for almost each topology examined in the paper.

In economy policies, socioeconomic indicators have an important place in determining the development levels. The determination and classification of the current social and economic structure of cities is considerably important in analyzing the development of cities and their probable development tendencies and in developing the regional development policies in parallel with this. In this study, cities are classified by fuzzy clustering c-means method according to their development levels with the help of socio-economic indicators. The aim is to determine the homogeneous city groups having the same characteristics based on these indicators. The data gathered from the study have been compared with some other classification studies in the literature. In this paper, development levels of cities were classified with socioeconomic indicators using FCM. Development Index values used for cities classification upon socioeconomic indicators. Results were also compared with DPT and Is-Bank development index values and interpreted.

A computer-aided detection (CAD) system is introduced in this paper for detection of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed CAD system firstly compensates motion artifacts and segments the breast region. Then, the potential lesion voxels are detected and used as the initial seed points for the seeded region-growing algorithm. A new and robust region-growing algorithm incorporating with Fuzzy C-means (FCM) clustering and vesselness filter is proposed to segment any potential lesion regions. Subsequently, the false positive detections are reduced by applying a discrimination step. This is based on 3D morphological characteristics of the potential lesion regions and kinetic features which are fed to the support vector machine (SVM) classifier. The performance of the proposed CAD system is evaluated using the free-response operating characteristic (FROC) curve. We introduce our collected dataset that includes 76 DCE-MRI studies, 63 malignant and 107 benign lesions. The prepared dataset has been used to verify the accuracy of the proposed CAD system. At 5.29 false positives per case, the CAD system accurately detects 94% of the breast lesions.

In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.

With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger, the simulation results corresponding to MKFCM-MRF clustering results are more realistic.

Flow cytomtery (FCM) has become a standard approach to enumerate viruses in water research. However, the nature of the fluorescent signal in flow cytometric analysis of water samples and the mechanism of its formation, have not been addressed for bacteriophages expected in wastewaters. Here we assess the behaviour of fluorescent DNA-staining dyes in aqueous solutions, as well as sensitivity and accuracy of FCM for enumeration of DNA-stained model bacteriophages λ, P1, and T4. We demonstrate that in aqueous systems fluorescent dyes form a self-stabilized (pseudolyophilic) emulsion of auto-fluorescing colloid particles. Sample shaking and addition of surfactants enhance auto-fluorescence due to increased dispersion and, in the presence of surfactants, stabilization of the dye emulsion. Bacteriophages with genome sizes, Graphical abstract Image 1, Highlights • Fluorescent dyes form auto-fluorescent colloidal particles in aqueous solutions. • Presence of surfactants enhances non-specific dye fluorescence. • Successful virus enumeration relies on correctly identified target population. • Stained virus samples demand gentle handling.

Article history: Received January 8 2013 Received in revised format March 5 2013 Accepted March 14 2013 Available online March 15 2013 Clustering cellular manufacturing plays an important role in many industrial engineering problems. This paper investigates the performance of two methods of heuristic and metaheuristics fuzzy clustering. The proposed method investigates heuristic well-known FCM and particle swarm optimization (PSO) on some well-known benchmarks. We use two criteria of J(P) as well as Xie-Beni to compare the results. Three parameters of PSO method is tuned using design of experiment and then the results of PSO are compared versus FCM method in terms of two mentioned criteria. The proposed models are run for each instance 10 different times and, using ANOVA test, the means of two methods are compared. While the results of ANOVA do not indicate any meaningful difference between PSO and FCM in terms of J(P), we have found some meaningful differences between PSO and FCM in terms of Xie-Beni criterion. In other words, PSO performs better than FCM in terms of Xie-Beni. © 2013 Growing Science Ltd. All rights reserved

Since its inception, Fuzzy c-means (FCM) technique has been widely used in data clustering. The advantages of FCM such as balancing of individual number of cluster points, drifting of small cluster centers to large neighboring cluster centers, and presence of fuzzy factor, make it more popular. However, early trapping at local minima and high sensitivity to the cluster center initialization are the major limitations of FCM. In this paper, a novel Elicit Teaching learning based optimization (ETLBO) approach has been incorporated with the Fuzzy c-means clustering algorithm to obtain the improved fitness values of the cluster centers. The simulation results of the proposed method have been compared with some other existing methods such as GA, PSO and IPSO. Experimental results show that the proposed approach is superior to the other methods in terms of their fitness value calculations. Keywords: FCM, K-means, Elicit TLBO, TLBO, PSO, IPSO

BACKGROUNDThe long-term prognosis for older patients with mantle-cell lymphoma is poor. Chemoimmunotherapy results in low rates of complete remission, and most patients have a relapse. We investigated whether a fludarabine-containing induction regimen improved the complete-remission rate and whether maintenance therapy with rituximab prolonged remission.METHODSWe randomly assigned patients 60 years of age or older with mantle-cell lymphoma, stage II to IV, who were not eligible for high-dose therapy to six cycles of rituximab, fludarabine, and cyclophosphamide (R-FC) every 28 days or to eight cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) every 21 days. Patients who had a response underwent a second randomization to maintenance therapy with rituximab or interferon alfa, each given until progression.RESULTSOf the 560 patients enrolled, 532 were included in the intention-to-treat analysis for response, and 485 in the primary analysis for response. The median age was 70 years. Although complete-remission rates were similar with R-FC and R-CHOP (40% and 34%, respectively; P = 0.10), progressive disease was more frequent with R-FC (14%, vs. 5% with R-CHOP). Overall survival was significantly shorter with R-FC than with R-CHOP (4-year survival rate, 47% vs. 62%; P = 0.005), and more patients in the R-FC group died during the first remission (10% vs. 4%). Hematologic toxic effects occurred more frequently in the R-FC group than in the R-CHOP group, but the frequency of grade 3 or 4 infections was balanced (17% and 14%, respectively). In 274 of the 316 patients who were randomly assigned to maintenance therapy, rituximab reduced the risk of progression or death by 45% (in remission after 4 years, 58%, vs. 29% with interferon alfa; hazard ratio for progression or death, 0.55; 95% confidence interval, 0.36 to 0.87; P = 0.01). Among patients who had a response to R-CHOP, maintenance therapy with rituximab significantly improved overall survival (4-year survival rate, 87%, vs. 63% with interferon alfa; P = 0.005).CONCLUSIONSR-CHOP induction followed by maintenance therapy with rituximab is effective for older patients with mantle-cell lymphoma. (Funded by the European Commission and others; ClinicalTrials.gov number, NCT00209209.)

In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI). For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF) and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM) and kernelized fuzzy C-means with spatial constraints (SKFCM). These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

Aiming at the serious energy imbalance of the traditional clustering routing, a clustering strategy for energy balance based on genetic clustering route algorithm is proposed in this paper. The new algorithm combines genetic algorithm and Fuzzy C-Means clustering algorithm, with genetic algorithm to overcome the sensitivity of the initial value of FCM. It can form the optimal cluster of network, and then select head nodes in each group. The simulation results show that compared with LEACH, the protocol can balance the energy cost of the sensor nodes, prolong the network lifetime efficiently, and perform better than LEACH.

Medical image segmentation has become an essential technique in clinical and research-oriented applications.MRI provides accurate anatomical brain images without use of ionizing radiation. For clinical use, the estimated values must be reliable and accurate,but many techniques fail on these criteria in an unrestricted clinical environment. Automated Magnetic Resonance Imaging (MRI) segmentation systems classify brain voxels into one of three main tissue types such as Gray Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Volumetric analysis of different parts of the brain is useful in assessing the progress or remission of various diseases such as Alzheimer's disease, epilepsy, multiple sclerosis, and schizophrenia. In the existing methods segmentation was done based on the intensity value of the voxels. Thus using intensity information alone has proven to be insufficient for a reliable automated segmentation of the brain tissues. Hence an adaptive mean-shift methodology is utilized in order to classify brain voxels where the MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. This proposed method clusters the joint spatial-intensity feature space thus extracting a representative set of high-density points within the feature space otherwise known as modes. The resultant output of an Adaptive means shift consists of several modes. A mode pruning step is undergone to reduce the number of modes. After the pruning step, intensity based clustering technique such as K-means algorithm is used for the classification of tissues. In this paper instead of K-means algorithm, Fuzzy C-means algorithm is implemented in order to achieve better segmentation. The final output consists of three regions which are used for the analysis of various neural diseases. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.