comprised of one or more charged analytes which displace fluorescing ions where its constituent components separate to. Fluorescing ions of the same charge as the charged analyte components cause a displacement. The displacement results in the location of the separated components having a reduced fluorescence intensity to the remainder of the background. Detection of the lower fluorescence intensity areas can be visually, by photographic means and methods, or by automated laser scanning.
Abstract— Classical fuzzy C-means (FCM) clustering is performed in the input space, given the desired number of clusters. Although it has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, a novel kernel-based fuzzy C-means clustering algorithm (KFCM). Its basic idea is to transform implicitly the input data into a higher dimensional feature space via a nonlinear map, which increases greatly possibility of linear separability of the patterns in the feature space, then perform FCM in the feature space. Another good attribute of KFCM is that it can automatically estimate the number of clusters in the dataset. A survey on clustering algorithms and emphasis on kernel based FCM is provided , since through a nonlinear map it wisely increases the linear separability of data points. Hence KFCM provide a suitable solution for segmenting images into subimages. The numerically complex level set method for extracting boundaries has paved a way for proposing Canny Edge Detection Algorithm for accurate results and reduction of computational complexity .
A work on recent approaches of machine learning is reported, which concentrates on the model for disease detection and classification of agriculture products with basic steps from image acquisition to disease recognition . Disease detection in crop is a major setback in attaining quality, there are several work reported on different plant, vegetable and fruits diseases. In one of the review work, advanced imaging techniques of electronics like fluorescence, hyper spectral, infrared and x- ray for plant disease detection was reported . There are many works reported which mainly used machine vision techniques for disease detection, few important works are, A work was reported for identification of diseases in apple fruit, the model consists of three steps, segmentation using K- means, feature extraction and classified apples with disease from normal using multi class SVM classifier . Another approach was reported for detecting disease in pomegranate using color, external surface as features and K-Means for segmenting the disease parts, finally infected fruits were classified from non infected one using SVM . A SVM based disease detection in tomato leaves reported using color, shape and texture features for differentiating the infected leaves from normal . A system for detecting the fungal disease in cereals, fruits, vegetables and commercial crops was built applying different image processing methods . A computer vision method was formulated to detect the maturity of fruit and disease in leaf of tomato using thresholding, k-means techniques . Also on works on disease detection in cucumber using artificial neural networks , pomegranate using k-means, SVM , apple using color k-means segmentation method , papaya using k-means and SVM  was reported. A review work on disease detection of leaf, root and stem of different crops was reported. Only very few works were reported on arecanut disease detection. A work reported on detection and classification of diseased arecanut with normal one, different texture features like wavelets, GLCM, LBP and Gabor filters used on HSI and YCbCr color models of arecanut image with K-NN for classification . Another work was done for classifying and detecting arecanut disease using HSV color model, the work focused on classifying the infected arecanut considering boiled and non boiled classes . All the above work focused on detection and classifying the disease affected from the healthy nuts, no work has been reported on affected regions of the disease arecanut. In our work, we employed pure detection of affected regions of disease arecanut image.
In this paper we present an intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system, based on the combination of One-Class Support Vector Machine (OCSVM) with RBF kernel and recursive k-means clustering. Important parameters of OCSVM, such as Gaussian width σ and parameter ν affect the performance of the classifier. Tuning of these parameters is of great importance in order to avoid false positives and over fitting. The combination of OCSVM with recursive k- means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters σ and ν, making it ideal for real-time intrusion detection mechanisms for SCADA systems. Extensive simulations have been conducted with datasets extracted from small and medium sized HTB SCADA testbeds, in order to compare the accuracy, false alarm rate and execution time against the base line OCSVM method.
7. K means with SVM Classifier the features of melanoma is analysed for the treatment. The division, Feature extraction and grouping process with reasonable calculations. The skin malignancy pictures are first fragmented, at that point from the sectioned pictures highlights are extricated LBP calculation and order is finished utilizing Bolster Vector Machine classifier based along the features expelled.
During the detection of exoplanets one must find a compromise between reasonable choices of the critical value c and the significance level α. If it is preferred to choose a low signifi- cance level: this implies that the critical value - in the context of exoplanet detection, the threshold - increases. This results in detection of less peaks, meaning the probability that the wanted signal is detected is low. However, choosing the critical value too high could result in detection of no peaks at all. Choosing a high significance level could lead to the detection of peaks in the data that are not the wanted signal, leading to a high probability of the occurrence of the Type I error. The correlation between α and c can be clearly seen in the standard normal distribution table , namely c = Φ −1 (1 − α), where Φ −1 is strictly increasing.
Clustering is a new science that work and study is ongoing in this field because it is considered a lot in different science as a solution. In recent years this method is optimized and the results of optimization are provided as papers. The goal of optimization is obtaining to the minimum number of replicates and clusters with the most similar members. In this paper a comparison is done between two common algorithms in order to recognize the DoS attacks. Finally, the results derived from K-means algorithm are better to identify these kinds of attacks. We should mention that these results are not exactly true and by choosing different fields to study; it is clear that Fuzzy k- means acts better than k-means.
In this paper, first we define a maximum likelihood objective function for each point in a transformed domain, where the distribution overlap between different tissues can be suppressed to some extent, and then energy functional is defined by integrating the maximum likelihood function over the entire image domain. Then we incorporate this energy functional into a multiphase level set formulation. The segmentation and bias correction are obtained via a level set evolution process. The advantage of our method is that the smoothness of the computed bias field is ensured by the normalized convolution  without extra cost. The evolution is less sensitive to the initialization, thus well suited for automatic applications. 
With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Computer systems and web services have become increasingly centralized, and many applications have evolved to serve millions or even billions of users. Entities that
Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures.
The linear regression model can be expressed in terms of matrices as y = Xβ + where y is the n × 1 vector of observed response values, X is n × p matrix of p regressors (design matrix), β is the p × 1 regression coefficients and is the n × 1 vector of error terms. The most widely used technique to find the best estimates of β is the method of ordinary least squares (OLS) which minimizes the sum of squared distances for all points from the actual observation to the regression surface. When the error terms are not normally and independently distributed (NID), distortion of the fit of the regression model can occur and consequently the parameter estimates and inferences can be flawed. The presence of one or more outliers is one of the common causes of non-normal error terms.
Copyright to IJIRSET www.ijirset.com 1734 Zhiyao Duan¤, Yungang Zhang, Changshui Zhang, Member, IEEE, Zhenwei Shi are proposed Source separation of musical signals is an appealing but difficult problem,especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures, thus sources can be characterized uniquely by harmonic structure models.Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames.The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources and singing voices, show that this algorithm outperforms the general Non negative Matrix Factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side-effect, this algorithm estimates the pitches of the harmonic instrumental sources.The number of concurrent sounds in each frame is also computed, which is a difficult task for general Multi-pitch Estimation (MPE) algorithms.
Performance sensitivity and specificity of the PCR step. The nested PCR primers were designed to be complementary to sequences in the promoter region of the pertussis toxin gene (Fig. 2). This gene has a high degree of homology between the different Bordetella species analyzed and has previously been successfully used for the detection of Bordetella species (2, 8, 13, 16, 27, 28, 30). The specificities of the PCR primers were tested by using lysates from the three Bordetella species (Fig. 3A, lanes 1 to 3) and three frequently occurring non-Bordetella species in nasopharyngeal aspirates: Moraxella catarrhalis, Hae- mophilus influenzae, and Streptococcus pneumoniae (Fig. 3A, lanes 4 to 6, respectively). Figure 3A shows a distinct fragment of 300 bp for the Bordetella species, with no amplification for the other strains. This is in agreement with the results of Reizenstein et al. (27, 28), who have tested several more spe- cies by also amplifying a part of the pertussis toxin promoter. The detection limit of the nested PCR was analyzed with a lysed B. pertussis culture. The culture was quantified by viable counting. A limiting dilution experiment was performed by serial dilutions of the lysate, enabling an estimate of the num- ber of targets (3). The results of the corresponding gel elec- trophoresis analysis after outer and inner amplification are shown in Fig. 4. Triplicate samples of a B. pertussis lysate were diluted 10-fold. After outer amplification, the intensity of the fragment decreased with increasing dilution factors. A weak fragment could still be seen at the dilution corresponding to 6
These Factors were selected based on preliminary experiments. The upper limit to 2-propanol in solvent composition was chosen as 0.8 since, at higher ratios, very little product was obtained and sharp decreases in yield were observed. Higher temperatures resulted in low purity and temperatures above 20°C were found to result in purities too low to be valid for consideration. On the other hand, very low temperatures resulted in viscous products due to poor oleoresin separation, this caused process difficulties and imperfect filtration.
 Francis Bassono, Pare Youssouf, Gabriel Bissanga, and Blaise Some. Application of the adomian decomposition method and the perturbation method to solving a sytem of perturbation equations. Far East Journal of Applied Mathematics, 72(2):91–99, 2012.  M. Hussain and Majid Khan. Modified laplace decomposition method. Applied Math-
To separate salts of metals and non-electrolytes, the approach of dialysis through the composite membranes (CMs) is proposed. CM is a combination of cation and anion exchange areas. In the composite membrane, cations and anions are transferred through the respective exchange areas simultaneously without violation of macroscopic electro-neutrality. This provides a better trans- fer of salts than conventional ion exchange membranes (IEMs). The dialysis of the ethylene glycol aqueous salt solutions through the CMs was investigated. We have shown that the transport of salts through the composite membranes is more intensive (unlike IEM providing no transfer of salts from weakly mineralized aqueous solutions due to the Donnan exclusion) and the ethylene glycol transfer is not very significant, that is the basis of effective separation. The possibility to use of composite membranes for metal salt and other electrolyte separation is discussed.
Among two-hundred articles, published between the years of 2005 and 2010, corresponding to volumes 23 to 54 of the Sudan Journal of Agricultural Research (SJAR), 70% of papers were based on use of randomized complete block designs while 30% were based on factorial designs which include a split plot designs and full factorial combination. Cardellino and Siewerdt (1995) critically reviewed evaluation of the use of tests for comparison of treatment means were tabulated into one of three categories: correct, partially correct and incorrect.