The spider graph obtained by identifying the end points of m internally disjoint paths each of length n denoted P(n,m), and the graph obtained by identifying the other end points of the graph P(n;m) called the closed spider graph denoted C(n;m). This paper describes the algorithms to show that the following graphs; P(2r + 1; m), 1

A linearized implicit finite difference method is devised for K(n, n). The stability and accuracy of the proposed methods are discussed. A compacton wave solution of the equation K(n. n) is used to examine the accuracy and efficiency of the proposed methods and study the effect of the added artificial dissipation term to solve the K(n, n) equation using finite difference method. The dynamics of waves having various initial wavepackets are discussed. (C) 2009 Elsevier B.V. All rights reserved.

The modified variational iteration method is applied for analytical treatment of nonlinear homogeneous initial value problem. The modified variational iteration method accelerates the convergence of the power series solution and reduces the size of work. A comparison between modified variational iteration method (MVIM) and variational iteration method (VIM) was made. The comparison enhances the use of the modified variational iteration method if we wish to obtain an approximate power series solution that converges faster to the closed form solution. The method is very simple and easy. (C) 2009 Elsevier Ltd. All rights reserved.

In this paper a new treatment for the Adomian decomposition method (ADM) is introduced. The new treatment is called the improved Adomian decomposition method (IADM) which improves the results obtained from the known Adomian decomposition method. The improved Adomian decomposition method is applied for the analytic treatment of nonlinear initial value problems. The improved method accelerates the convergence of the series solution, and provides the exact power series solution. It solves the drawbacks in the standard Adomian decomposition method. (C) 2009 Elsevier Ltd. All rights reserved.

This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rules need to be updated. Re-running the temporal mining algorithm every time is ineffective since it neglects the previously discovered rules, and repeats the work done previously. Furthermore, existing incremental mining techniques cannot deal with temporal association rules. In this paper, an incremental algorithm to maintain the temporal association rules in a transaction database is proposed. The algorithm benefits from the results of earlier mining to derive the final mining output. The experimental results on both the synthetic and the real dataset illustrate a significant improvement over the conventional approach of mining the entire updated database. (C) 2010 Elsevier B.V. All rights reserved.

Augmented Reality (AR) is the technology of adding virtual objects to real scenes through enabling the addition of missing information in real life. As the lack of resources is a problem that can be solved through AR, this paper presents and explains the usage of AR technology we introduce Augmented Reality Student Card (ARSC) as an application of AR in the field of education. ARSC uses single static markers combined in one card for assigning different objects, while leaving the choice to the computer application for minimizing the tracking process. ARSC is designed to be a useful low cost solution for serving the education field. ARSC can represent any lesson in a 3D format that helps students to visualize different learning objects, interact with theories and deal with the information in a totally new, effective, and interactive way. ARSC can be used in offline, online and game applications with seven markers, four of them are used as a joystick game controller. One of the novelties in this paper is that experimental tests had been made for the ARTag marker set for sorting them according to their efficiency. The results of those tests were used in this research to choose the most efficient markers for ARSC, and can be used for further research. The experimental work in this paper also shows the constraints for marker creation for an AR application. As we need to work in both online and offline application, merging of toolkits and libraries has been made, as presented in this paper. ARSC was examined by a number of students of both genders with average age between 10 and 17 years and it found great acceptance among them. (C) 2010 Elsevier Ltd. All rights reserved.

The Adomian decomposition method (ADM) cannot deal generally with the non-homogenous differential equations. The method still needs improvements. This paper introduces a qualitative improvement in the method. The improved method is called the improved Adomian decomposition method (IADM). A comparison between ADM and IADM shows that the ADM gives results that is not accepted in some examples and cannot solve the other examples while IADM solves the problems effectively and efficiently. (C) 2011 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

A new application of modified variational iteration method (MVIM) for non-linear non-homogeneous differential equations is introduced. The modified form introduces a change in the formulation of variational iteration relation and provides a qualitative improvement over standard variational iteration method (VIM). This paper is an extension of the previous work in Abassy (2010) [33], Abassy et al. (2007) [31,29,32,30]. Some illustrative numerical examples and Mathematica program codes are introduced. (C) 2011 Elsevier Ltd. All rights reserved.

In the data mining field, data representation turns out to be one of the major factors affecting mining algorithm scalability. Mining Frequent Itemsets (MFI) is a data mining problem that is heavily affected by this fact. The vertical approach is one of the successful data representations adopted for MFI problem. The main advantage of this approach is support for fast frequency counting via joining operations. Recently, an encoding method called prime-encoding is proposed as an enhancement for the vertical approach [10]. The performance study introduced in [10] confirmed the high quality of prime-encoding based vertical mining of frequent sequence over other vertical and horizontal ones in terms of space and time. Though sequence mining is more general than itemset mining, this paper presents a prime-encoding based vertical mining of frequent itemsets with new optimizations and a new re-encoding method that further enhance memory and speed. The experimental results show that prime encoding based vertical itemset mining is suitable for high-dimensional sparse data.

While cognitive radio networks (CRNs) present a promising solution to solve the scarcity of the radio spectrum, they are still susceptible to security threats. Until now, only a few researchers considered the use of intrusion detection systems (IDSs) to combat these threats against CRNs. In this article we describe a CRN based on IEEE wireless regional area network (WRAN) and describe some of the security threats against it. For the secondary users in the CRN to quickly detect whether they are being attacked, a simple yet effective IDS is then presented. Our proposal uses non-parametric cumulative sum (cusum) as the change point detection algorithm to discover the abnormal behavior due to attacks. Our proposed IDS adopts an anomaly detection approach and it profiles the CRN system parameters through a learning phase. So, our proposal is also able to detect new types of attacks. As an example, we present the case of detection of a jamming attack, which was not known to the IDS beforehand. The proposed IDS is evaluated through computer based simulations, and the simulation results clearly indicate the effectiveness of our proposal.

Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c = 3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90 min of short or standard hemodialysis (HD) sessions and after 60 min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (C-int) and post-dialysis urea concentrations (C-post) in order to predict the equilibrated (C-eq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (C-eq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose ((e)Kt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for C-eq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

Breast cancer detection and segmentation of cytological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. This paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The images are enhanced with histogram stretching and contrast-limited adaptive histogram equalization (CLAHE). The locations of the cell nuclei in the image are detected with circular Hough transform (CHT) and local maximum filtering. The elimination of false positive findings (noisy circles and blood cells) is achieved using Otsu's thresholding method and fuzzy C-means clustering technique. The segmentation of the nuclei boundaries is accomplished with the application of the marker controlled watershed transform in the gradient image, using the nuclei markers extracted in the detection step. The proposed method is evaluated using 92 breast cytological images containing 11,502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells noisy circles. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved

Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

This article introduces a hybrid approach that combines the advantages of fuzzy sets, ant-based clustering and multilayer perceptron neural networks (MLPNN) classifier, in conjunction with statistical-based feature extraction technique. An application of breast cancer MRI imaging has been chosen and hybridization system has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: Benign or Malignant. The introduced hybrid system starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by an improved version of the classical ant-based clustering algorithm, called adaptive ant-based clustering to identify target objects through an optimization methodology that maintains the optimum result during iterations. Then, more than twenty statistical-based features are extracted and normalized. Finally, a MLPNN classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether the cancer is Benign or Malignant. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the adaptive ant-based segmentation is superior to the classical ant-based clustering technique and the overall accuracy offered by the employed hybrid technique confirm that the effectiveness and performance of the proposed hybrid system is high. (C) 2013 Elsevier B.V. All rights reserved.

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.

Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies-Bouldin index and Xie-Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.

The purpose of this study is to develop an intelligent remote detection and diagnosis system for breast cancer based on cytological images. First, this paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The locations of the cell nuclei in the image were detected with circular Hough transform. The elimination of false-positive (FP) findings (noisy circles and blood cells) was achieved using Otsu's thresholding method and fuzzy c-means clustering technique. The segmentation of the nuclei boundaries was accomplished with the application of the marker-controlled watershed transform. Next, an intelligent breast cancer classification system was developed. Twelve features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used, namely, multilayer perceptron using back-propagation algorithm, probabilistic neural network (PNN), learning vector quantization, and support vector machine (SVM). The classification results were obtained using tenfold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity, and specificity. Finally, we have merged the proposed computer-aided detection and diagnosis system with the telemedicine platform. This is to provide an intelligent, remote detection, and diagnosis system for breast cancer patients based on the Web service. The proposed system was evaluated using 92 breast cytological images containing 11 502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells and noisy circles. In addition, two benchmark data sets were evaluated for comparison. The results showed that the predictive ability of PNN and SVM is stronger than the others in all evaluated data sets.

In this article, an internal model control plus proportional-integral-derivative (IMC-PID) tuning procedure for cascade control systems is proposed based on the gain and phase margin specifications of the inner and outer loop. The internal model control parameters are adjusted according to the desired frequency response of each loop with a minimum interaction between the inner and outer PID controllers, obtaining a fine tuning and the desired gain and phase margins specifications due to an appropriate selection of the PID controller gains and constants. Given the design specifications for the inner and outer loop, this tuning procedure adjusts the IMC parameter of each controller independently, with no interference between the inner and outer loop obtaining a robust method for cascade controllers with better performance than sequential tuning or other frequency domain-based methods. This technique is accurate and simple, providing a convenient technique for the PID tuning of cascade control systems in different applications such as mechanical, electrical or chemical systems. The proposed tuning method explained in this article provides a flexible tuning procedure in comparison with other tuning procedures because each loop is tuned simultaneously without modifying the robustness characteristics of the inner and outer loop. Several experiments are shown to compare and validate the effectiveness of the proposed tuning procedure over other sequential or cascade tuning methods; some experiments under different conditions are done to test the performance of the proposed tuning technique. For these reasons, a robustness analysis based on sensitivity is shown in this article to analyze the disturbance rejection properties and the relations of the IMC parameters.