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Compressed images are frequently used to accomplish computer vision tasks. There is an extensive use of traditional image compression standards including JPEG 2000. However, they would not consider the present solution. We determined a new image compression model that was inspired by the existing research on the medical image compression model. Here, the images are filtered at the preprocessing step to eradicate the noises that exist. The images are then decomposed using discrete wavelet transform (DWT). The outcome is then vectored quantized. In this step, we employ optimisation-assisted fuzzy $c$-means clustering for vector quantisation (VQ) with codebook generation. Considering this as an optimisation issue, a new hybrid optimisation algorithm called Bald Eagle Updated Pelican Optimization with Geometric Mean weightage (BUPOGM) is introduced to solve it. The algorithm is a combination of pelican optimisation and bald eagle optimisation, respectively. Quantised coefficients are finally encoded via the Huffman encoding process, and the compressed image is represented by the resultant bits. The outcome of the proposed work is satisfactory as it performs better than the other state-of-the-art methods.

Previous studies have demonstrated that the total alkaloids of Sophora alopecuroides (TASA), which contains many different ingredients like sophocarpine, matrine, oxymatrine, sophoridine, sophoramine, aloperine and cytosine, were able to protect colon against ulcers caused by 2,4,6-trinitrobenze sulphonic acid (TNBS)/ethanol treated models. In order to elucidate the mechanisms by which TASA exerts its effect of anti-inflammation and immunoregulation on rats with colitis, DAI (disease activity index) and histological grading of colitis were evaluated in the animal model. Moreover, the expression of CD4⁺CD25⁺ regulatory T cells (Tregs) and IL-10 in rats with experimental colitis were observed by FCM, ELISA and RT-PCR in this study. Results showed that TASA (15, 30 or 60 mg/kg/day) significantly up-regulated CD4⁺CD25⁺Tregs (P = 0.02, P = 0.02, P = 0.03) and IL-10 levels (ELISA: P = 0.03, P = 0.02, P = 0.00; RT-PCR: P = 0.04, P = 0.02, P = 0.01) respectively and decreased the DAI and histological grading of colitis in the peripheral blood (PB) and colon of rat colitis models (3.44 ± 1.53, 4.25 ± 1.27, 4.42 ± 1.24 and 3.50 ± 1.42, 4.05 ± 1.32, 4.51 ± 1.55 vs. 7.18 ± 1.32 and 7.38 ± 1.52, P < 0.05, P < 0.01, respectively). Most interestingly, a negative correlation was demonstrated between the expression of CD4⁺CD25⁺ Tregs and DAI (Pearson r_PB = -0.677, P < 0.01; Pearson r_COLON = -0.663, P < 0.01, n = 60), or histological grading of colitis (Pearson r_PB = -0.725, P < 0.01; Pearson r_COLON = -0.623, P < 0.01, n = 60). Simultaneously, a positive correlation existed between CD4⁺CD25⁺ Tregs and IL-10 cytokine (IL-10 mRNA) in the colon and PB of rats (Pearson r_PB = 0.789, P < 0.01, n = 60; Pearson r_COLON = 0.678, P < 0.01, n = 60). These results may explain to some extent the mechanisms of TASA on treating rats with experimental colitis.

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

Image segmentation is a classical problem in the field of computer vision. Fuzzy $c$-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We proposed a new clustering method based on Fuzzy C-Mean (FCM) and user specified data. In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to inhomogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are no such clusters. Then, the clusters contain training data for a target class assigned to that target class; mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method.

In this research paper, we propose an automatic segmentation method of multispectral magnetic resonance image (MRI) of the human brain using an information fusion approach through the framework of the possibility theory. The fusion process is summarized into three essential steps. First, a data is extracted from the various images and modeled in a common mathematical framework, in this step the fuzzy C-means (FCM) algorithm is chosen. The combination rule is used to combine this information in the second step. A final segmented image is the result of the last phase. Our experimental results using simulated brain MRI datasets show that the proposed approach overcome the impact of the noise and substantially improve the accuracy of image segmentation.

Wireless Sensor Networks (WSNs) are used for data collection from the surrounding environment using the enormous amount of sensor nodes. Energy saving is the most fundamental challenge of WSNs which primarily depends on the Cluster Head (CH) selection and packet routing strategy. In this paper, we are proposing an Energy Aware Distance-based Cluster Head selection and Routing (EADCR) protocol to extend the lifetime of WSN using the FCM technique, residual energy of the nodes, their relative Euclidean distances from the Base Station (BS) and cluster centroid. Since the nodes consume a considerable amount of energy during the clustering phase, thus to avoid this, here, we are introducing a new clustering approach where the CH selection is now based on the newly proposed fitness function. We are also providing a new strategy for packet routing using the shortest path technique for routing between node and its destination which reduces the energy consumption of the CHs by employing the multi-hop communication. We also save the energy of the nodes using their Euclidean distances among them, from their CH and from BS. The simulation results exhibit that the EADCR enhances the network lifetime as compared to the other similar algorithms, e.g., FCM, REHR, UCRA–GSO and CCA–GWO under different scenarios. It also saves the residual energy of the network and enhances the network coverage.

GPS receivers have a wide range of applications, but are not always secure. A spoofing attack is one source of conscious errors in which the counterfeit signal overcomes the authentic GPS signal and takes control of the receiver’s operation. Recently, GPS spoofing attack detection based on computational algorithms, such as machine learning, classification, wavelet transform and clustering, has been developing. This paper proposes multiple clustering algorithms for accurately clustering the authentic and spoofing signals, called subtractive, FCM and DBSCAN clustering. The spoofing attack is recognized using two distinct features: moving phase detector variance and norms of correlators. Spoofing and authentic signals have different patterns in the proposed features. According to the Dunn and Silhouette indexes, the validation of the results is investigated. The Dunn values for the proposed approaches are 0.8592, 0.5285 and 0.6039 for DBSCAN, FCM and subtractive clustering, respectively. Also, the DBSCAN algorithm is implemented at the RTL level because of its highest value for the Dunn index and algorithm verifiability. Using the Vivado tools, this algorithm is implemented and designed on a Xilinx Virtex 7 xc7vx690tffg1930-3 hardware device for two-dimensional data with 32-bit accuracy and 130 data points.

Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

A new method of MRI brain segmentation integrates fuzzy $c$-means (FCM) clustering and rough set theory. In this paper, we use rough set algorithm to find the suitable initial clustering number to initial clustering centers for FCM. Then we use FCM to MRI brain segmentation, but the algorithm of FCM has the limitation of converging to local infinitesimal point in medical segmentation. While avoiding being trapped in a local optimum, we use the particle swarm optimization algorithm to restrict convergence of FCM which can reduce calculation. The final experiment results show that improved algorithm not only retains the advantages of rapid convergence but also can control the local convergence and improve the global search ability. The method in this paper is better than that of cluttering performance.

Diabetes is the most prevalent disease that affects the retina and leads to blindness without any symptoms. An adverse change in retinal blood vessels that leads to vision loss is called as Diabetic Retinopathy (DR). DR is one among the leading causes of blindness worldwide. There is an increasing interest to design the medical system for screening and diagnosis of DR. Segmentation of exudates is essential for diagnostic purpose. In this regard, Optic Disc (OD) center is detected by template matching technique and then it is masked to avoid misclassification in the results of exudates detection. In this paper, we proposed a novel K-Means nearest neighbor algorithm, combining K-means with morphology and Fuzzy to segment exudates. The main advantage of the proposed approach is that it does not depend upon manually selected parameters. Performances of these algorithms are compared with existing algorithms like Fuzzy C means (FCM) and Spatially Weighted Fuzzy C Means (SWFCM). These different segmentation algorithms are applied to publically available STARE data set and it is found that mean sensitivity, specificity and accuracy values for the fuzzy algorithm is 91%, 94% and 93% respectively and considerably higher than other algorithms.

Fuzzy clustering is playing a more and more important role in text clustering because of text diversity and abundance. As the most popular fuzzy clustering algorithm, FCM, however, is rather sensitive in its initial clustering centers. This paper presents a new GA-based FCM approach (GFCM for short) to overcome this drawback, which optimizes the initial clustering centers of FCM with the global searching ability of GA. Related operators are improved to enhance clustering quality and accelerate searching process. Besides experiment results not only prove its feasibility but also reveal more effective performance, compared with previous FCM algorithm.

Clustering is primarily used to uncover the true underlying structure of a given data set. Most algorithms for clustering often depend on initial guesses of the cluster centers and assumptions made as to the number of subgroups presents in the data. In this paper, we propose a method for fuzzy clustering without initial guesses on cluster number in the data set. Our method assumes that clusters will have the normal distribution. Our method can automatically estimate the cluster number and form the clusters according to the number. In it, Genetic Algorithms (GAs) with two chromosomic coding techniques are evaluated. Graph structured coding can derive high fitness value. Linear structured can save the number of generation.