Showing papers by "Ahmad Taher Azar published in 2015"

Computational Intelligence Applications in Modeling and Control

Abstract: The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 16 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.

Backstepping Controller Design for the Global Chaos Synchronization of Sprott’s Jerk Systems

Abstract: This research work investigates the global chaos synchronization of Sprott’s jerk chaotic system using backstepping control method. Sprott’s jerk system (1997) is algebraically the simplest dissipative chaotic system consisting of five terms and a quadratic nonlinearity. Sprott’s chaotic system involves only five terms and one quadratic nonlinearity, while Rossler’s chaotic system (1976) involves seven terms and one quadratic nonlinearity. This work first details the properties of the Sprott’s jerk chaotic system. The phase portraits of the Sprott’s jerk system are described. The Lyapunov exponents of the Sprott’s jerk system are obtained as L 1 = 0.0525, L 2 = 0 and L 3 = −2.0727. The Lyapunov dimension of the Sprott’s jerk system is obtained as D L = 2.0253. Next, an active backstepping controller is designed for the global chaos synchronization of identical Sprott’s jerk systems with known parameters. The backstepping control method is a recursive procedure that links the choice of a Lyapunov function with the design of a controller and guarantees global asymptotic stability of strict-feedback chaotic systems. Finally, an adaptive backstepping controller is designed for the global chaos synchronization of identical Sprott’s jerk systems with unknown parameters. MATLAB simulations are provided to validate and demonstrate the effectiveness of the proposed active and adaptive chaos synchronization schemes for the Sprott’s jerk systems.

Global chaos synchronisation of identical chaotic systems via novel sliding mode control method and its application to Zhu system

Abstract: Synchronisation of chaotic systems is an important research problem in chaos theory. In this research work, a novel sliding mode control method is proposed for the global chaos synchronisation of identical chaotic systems. The general result derived using novel sliding mode control method is established using Lyapunov stability theory. As an application of the general result, the problem of global chaos synchronisation of identical Zhu chaotic systems (2010) is studied and a new sliding mode controller is derived. Numerical simulations have been shown to illustrate the phase portraits of Zhu chaotic system and the sliding mode controller design for the global chaos synchronisation of identical Zhu chaotic systems.

Dimensionality reduction of medical big data using neural-fuzzy classifier

Abstract: Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Anti-synchronization of Identical Chaotic Systems Using Sliding Mode Control and an Application to Vaidyanathan–Madhavan Chaotic Systems

Abstract: Anti-synchronization is an important type of synchronization of a pair of chaotic systems called the master and slave systems. The anti-synchronization characterizes the asymptotic vanishing of the sum of the states of the master and slave systems. In other words, anti-synchronization of master and slave system is said to occur when the states of the synchronized systems have the same absolute values but opposite signs. Anti-synchronization has applications in science and engineering. This work derives a general result for the anti-synchronization of identical chaotic systems using sliding mode control. The main result has been proved using Lyapunov stability theory. Sliding mode control (SMC) is well-known as a robust approach and useful for controller design in systems with parameter uncertainties. Next, as an application of the main result, anti-synchronizing controller has been designed for Vaidyanathan–Madhavan chaotic systems (2013). The Lyapunov exponents of the Vaidyanathan–Madhavan chaotic system are found as \(L_1 = 3.2226, L_2 = 0\) and \(L_3 = -30.3406\) and the Lyapunov dimension of the novel chaotic system is found as \(D_L = 2.1095\). The maximal Lyapunov exponent of the Vaidyanathan–Madhavan chaotic system is \(L_1 = 3.2226\). As an application of the general result derived in this work, a sliding mode controller is derived for the anti-synchronization of the identical Vaidyanathan–Madhavan chaotic systems. MATLAB simulations have been provided to illustrate the qualitative properties of the novel 3-D chaotic system and the anti-synchronizer results for the identical novel 3-D chaotic systems.

Hybrid Synchronization of Identical Chaotic Systems Using Sliding Mode Control and an Application to Vaidyanathan Chaotic Systems

Abstract: Hybrid phase synchronization is a new type of synchronization of a pair of chaotic systems called the master and slave systems. In hybrid phase synchronization, the odd numbered states of the master and slave systems are completely synchronized (CS), while their even numbered states are anti-synchronized (AS). The hybrid phase synchronization has applications in secure communications and cryptosystems. This work derives a new result for the hybrid phase synchronization of identical chaotic systems using sliding mode control. The main result has been proved using Lyapunov stability theory. Sliding mode control (SMC) is well-known as a robust approach and useful for controller design in systems with parameter uncertainties. As an application of this general result, a sliding mode controller is derived for the hybrid phase synchronization of the identical 3-D Vaidyanathan chaotic systems (2014). MATLAB simulations have been provided to illustrate the Vaidyanathan system and the hybrid synchronizer results for the identical Vaidyanathan systems.

Analysis and Control of a 4-D Novel Hyperchaotic System

Abstract: Hyperchaotic systems are defined as chaotic systems with more than one positive Lyapunov exponent. Combined with one null Lyapunov exponent along the flow and one negative Lyapunov exponent to ensure boundedness of the solution, the minimal dimension for a continuous hyperchaotic system is four. The hyperchaotic systems are known to have important applications in secure communications and cryptosystems. First, this work describes an eleven-term 4-D novel hyperchaotic system with four quadratic nonlinearities. The qualitative properties of the novel hyperchaotic system are described in detail. The Lyapunov exponents of the system are obtained as \( L_{1} = 0.7781,L_{2} = 0.2299,L_{3} = 0 \) and \( L_{4} = - 12.5062 \). The maximal Lyapunov exponent of the system (MLE) is \( L_{1} = 0.7781 \). The Lyapunov dimension of the novel hyperchaotic system is obtained as \( D_{L} = 3.0806 \). Next, the work describes an adaptive controller design for the global chaos control of the novel hyperchaotic system. The main result for the adaptive controller design has been proved using Lyapunov stability theory. MATLAB simulations are described in detail for all the main results derived in this work for the eleven-term 4-D novel hyperchaotic system with four quadratic nonlinearities.

Analysis, Control and Synchronization of a Nine-Term 3-D Novel Chaotic System

Abstract: This research work describes a nine-term 3-D novel chaotic system with four quadratic nonlinearities. First, this work describes the dynamic analysis of the novel chaotic system and qualitative properties of the novel chaotic system are derived. The Lyapunov exponents of the nine-term novel chaotic system are obtained as \( L_{1} = 9.45456,\;L_{2} = 0 \) and \( L_{3} = - 30.50532 \). Since the maximal Lyapunov exponent (MLE) of the novel chaotic system is \( L_{1} = 9.45456 \), which is a high value, the novel chaotic system exhibits strong chaotic properties. Next, this work describes the adaptive control of the novel chaotic system with unknown system parameters. Also, this work describes the adaptive synchronization of the identical novel chaotic systems with unknown system parameters. The adaptive control and synchronization results are proved using Lyapunov stability theory. MATLAB simulations are given to demonstrate and validate all the main results derived in this work for the nine-term 3-D novel chaotic system.

A novel hybrid feature selection method based on rough set and improved harmony search

Abstract: Feature selection is a process of selecting optimal features that produce the most prognostic outcome. It is one of the essential steps in knowledge discovery. The crisis is that not all features are important. Most of the features may be redundant, and the rest may be irrelevant and noisy. This paper presents a novel feature selection approach to deal with issues of high dimensionality in the medical dataset. Medical datasets are habitually classified by a large number of measurements and a comparatively small number of patient records. Most of these measurements are irrelevant or noisy. This paper proposes a supervised feature selection method based on Rough Set Quick Reduct hybridized with Improved Harmony Search algorithm. Rough set theory is one of the most thriving methods used for feature selection. The Rough Set Improved Harmony Search Quick Reduct (RS-IHS-QR) algorithm is a relatively new population-based meta-heuristic optimization algorithm. This approach imitates the music improvisation process, where each musician improvises their instrument's pitch by searching for a perfect state of harmony. The quality of the reduced data is measured by the classification performance. The proposed algorithm is experimentally compared with the existing algorithms Rough Set Quick Reduct (RS-QR) and Rough Set Particle Swarm Optimization Quick Reduct (RS-PSO-QR). The number of features selected by the proposed method is comparatively low. The proposed algorithm reveals more than 90 % classification accuracy in most of the cases and the time taken to reduct the dataset also decreased than the existing methods. The experimental result demonstrates the efficiency and effectiveness of the proposed algorithm.

Design and SPICE implementation of a 12-term novel hyperchaotic system and its synchronisation via active control

Abstract: This research work describes the design and SPICE implementation of a 12-term novel hyperchaotic system with four quadratic nonlinearities. The Lyapunov exponents of the dissipative novel hyperchaotic system are obtained as L1 = 4.1043, L2 = 0.1571, L3 = 0 and L4 = −34.2529. The maximal Lyapunov exponent (MLE) of the novel hyperchaotic system is L1 = 4.1043. The Lyapunov dimension of the hyperchaotic system has been obtained as DL = 3.1244. The qualitative properties of the novel hyperchaotic system are described in detail. Moreover, the hyperchaotic system has been implemented in LTspice IV (SPICE simulator) and the outputs are obtained in SPICE showing hyperchaos of the novel system. Next, active control method has been applied for the global synchronisation of the novel hyperchaotic systems and the synchronisation result has been proved using Lyapunov stability theory. The active controller for the global synchronisation of the identical novel hyperchaotic systems has been implemented in LTspice IV and the SPICE simulation results have been detailed.

Design and Modeling of Anti Wind Up PID Controllers

Abstract: In this chapter several anti windup control strategies for SISO and MIMO systems are proposed to diminish or eliminate the unwanted effects produced by this phenomena, when it occurs in PI or PID controllers. Windup is a phenomena found in PI and PID controllers due to the increase in the integral action when the input of the system is saturated according to the actuator limits. As it is known, the actuators have physical limits, for this reason, the input of the controller must be saturated in order to avoid damages. When a PI or PID controller saturates, the integral part of the controller increases its magnitude producing performance deterioration or even instability. In this chapter several anti windup controllers are proposed to eliminate the effects yielded by this phenomena. The first part of the chapter is devoted to explain classical anti windup architectures implemented in SISO and MIMO systems. Then in the second part of the chapter, the development of an anti windup controller for SISO systems is shown based on the approximation of the saturation model. The derivation of PID SISO (single input single output) anti windup controllers for continuous and discrete time systems is implemented adding an anti windup compensator in the feedback loop, so the unwanted effects are eliminated and the system performance is improved. Some illustrative examples are shown to test and compare the performance of the proposed techniques. In the third part of this chapter, the derivation of a suitable anti windup PID control architecture is shown for MIMO (multiple input multiple output) continuous and discrete time systems. These strategies consist in finding the controller parameters by static output feedback (SOF) solving the necessary linear matrix inequalities (LMI’s) by an appropriate anti windup control scheme. In order to obtain the control gains and parameters, the saturation is modeled with describing functions for the continuous time case and a suitable model to deal with this nonlinearity in the discrete time case. Finally a discussion and conclusions sections are shown in this chapter to analyze the advantages and other characteristics of the proposed control algorithms explained in this work.

Big Data in Complex Systems: Challenges and Opportunities

Abstract: This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are otherfoundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.

Sliding Modes for Fault Tolerant Control

Abstract: As modern technological systems increase in complexity, their corresponding control systems become more and more sophisticated. In order to increase the reliability, which is crucial topic in industrial applications. The main focus of this chapter will be on the design of fault tolerant control (FTC) strategy. Therefore, FTC has found extensive applications in multiple domains including mechanical engineering, electrical engineering, control engineering, biomedical engineering, and micro-engineering. This chapter gives a brief overview in the field of FTC (definitions, practical requirements and classification). On the other hand, give a brief introduction to the concept of sliding mode control and examine its properties. Sliding surface design and tracking requirements are also discussed. In many ways, this chapter demonstrates the true theoretical and applications depth to which the sliding mode control paradigm has been developed today in the fields of FTC. Also, highlights the benefits and give discussions of some FTC approaches based SMC. At the end, in order to introduce the concept and to prove the effectiveness of the proposed approach a permanent magnet synchronous motor (PMSM) systems case study will be presented.

Adaptive Sliding Mode Control of the Furuta Pendulum

Abstract: In this chapter an adaptive sliding mode controller for the Furuta pendulum is proposed. The Furuta pendulum is a class of underactuated mechanical systems commonly used in many control systems laboratories due to its complex stabilization which allows the analysis and design of different nonlinear and multivariable controllers that are useful in some fields such as aerospace and robotics. Sliding mode control has been extensively used in the control of mechanical systems as an alternative to other nonlinear control strategies such as backstepping, passivity based control etc. The design and implementation of an adaptive sliding mode controller for this kind of system is explained in this chapter, along with other sliding mode controller variations such as second order sliding mode (SOSMC) and PD plus sliding mode control (PD \(+\) SMC) in order to compare their performance under different system conditions. These control techniques are developed using the Lyapunov stability theorem and the variable structure design procedure to obtain asymptotically stable system trajectories. In this chapter the adaptive sliding mode consist of a sliding mode control law with an adaptive gain that makes the controller more flexible and reliable than other sliding mode control (SMC) algorithms and nonlinear control strategies. The adaptive sliding mode control (ASMC) of the Furuta pendulum, and the other SMC strategies shown in this chapter, are derived according to the Furuta’s pendulum dynamic equations making the sliding variables, position errors and velocity errors reach the zero value in a specified reaching time. The main reason of deriving two well known sliding mode control strategy apart from the proposed control strategy of this chapter (adaptive sliding mode control) is for comparison purposes and to evince the advantages and disadvantages of adaptive sliding mode control over other sliding mode control strategies for the stabilization of the Furuta pendulum. A chattering analysis of the three SMC variations is done, to examine the response of the system, and to test the performance of the ASMC in comparison with the other control strategies explained in this chapter.

Deadbeat Control for Multivariable Discrete Time Systems with Time Varying Delays

Abstract: In this chapter a novel approach for the deadbeat control of multivariable discrete time systems is proposed. Deadbeat control is a well known technique that has been implemented during the last decades in SISO and MIMO discrete time systems due to the ripple free characteristics and the designer selection of the output response. Deadbeat control consist in establishing the minimum number of steps in which the desired output response must be reached, this objective is achieved by placing the appropriate number of closed loop poles at the origin and cancelling the transmission zeros of the system. On the other side, constant time delays in the state or the input of the system is a phenomena found in many continuous and discrete time systems, produced by delays in the communication channels or other kind of sources, yielding unwanted effects on the systems like performance deterioration, or instability on the system. Even when the analysis and design of appropriate controllers with constant time delays in the state or the input has been studied by several researchers applying several control techniques such as state and output feedback, in this chapter the development of a deadbeat control for discrete time systems with constant delays is explained as a preamble of the main topic of this chapter related to the deadbeat control of discrete time systems with time varying delays. This first approach is derived by implementing a state feedback controller, and in opposition of the implementation of traditional techniques such as optimal control where a stable gain is obtained by solving the required Riccati equations, the deadbeat controller is obtained by selecting the appropriate gain matrix solving the necessary LMI’s placing the required number of poles at the origin and eliminating the finite transmission zeros of the system in order to obtain the required deadbeat characteristics in which the desired system response is reached in minimun time steps. After this overview, deadbeat controllers are designed considering the time varying delays, following a similar approach such as the constant time delay counterpart. In order to obtain an appropriate deadbeat controller, a state feedback controller gain is obtained by solving the required LMI’s, placing the required poles in order to obtain the desired response cancelling the finite transmission zeros. The theoretical background is tested by several illustrative examples and finally the discussion and conclusions of this work are shown in the end of this chapter.

Automatic extraction of blood vessels in the retinal vascular tree using multiscale medialness

Abstract: We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering in the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale line-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image maps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are expected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of the image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different scales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is the STARE project's dataset and the second one is the DRIVE dataset. The experimental results, applied on the STARE dataset, show a maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average reaches 91.55%.

Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System

Abstract: Social tagging is one of the vital attributes of WEB2.0. The challenge of Web 2.0 is a gigantic measure of information created over a brief time. Tags are broadly used to interpret and arrange the web 2.0 assets. Tag clustering is the procedure of grouping the comparable tags into clusters. The tag clustering is extremely valuable for researching and organizing the web2. 0 resources furthermore critical for the achievement of Social Bookmarking frameworks. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Firefly TRS-Firefly-K-Means clustering algorithm for clustering tags in social systems. At that stage, the proposed system is contrasted with the benchmark algorithm K-Means clustering and Particle Swarm optimization PSO based Clustering technique. The experimental analysis outlines the viability of the suggested methodology.

Computational analysis of blood flow in the retinal arteries and veins using fundus image

Abstract: The retina is the only tissue in which blood vessels can be visualized non-invasively in vivo. Thus, the study of the retinal hemodynamic has special interest for both physiological and pathological conditions. The aim of this study has been to develop a detailed computational model for a quantitative analysis of the blood flow in physiologically realistic retinal arterial and venous networks. The geometrical outlines of both retinal artery and vein have been extracted from the retinal image acquired from a healthy young adult by a retinal camera Topcon TRC-50EX. The microvascular diameter effect (i.e., Fahraeus-Lindqvist effect) and the hematocrit have been considered in determining the viscosity of the blood in the retinal vessel segments. The blood moves at a velocity that is 2 times less in the veins (maximum 5.4 cm/s) than the velocity at which it moves in the arteries (maximum 11 cm/s) which are in good agreement with in vivo measurements reported in the literature. The pressure drop has been in the range of 11-14 mmHg between the inlet and outlets for the arterial network, and 13-14 mmHg for the vein network. The developed method can be used as a tool for continuous monitoring of the retinal circulation for clinical assessments as well as experimental studies.

Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques

Abstract: In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

Image mining framework and techniques: a review

Abstract: Image mining refers to a data mining technique where images are used as data. It supports a large field of applications like medical diagnosis, agriculture, industrial work, space research and obviously the educational field. The image mining technique can extract knowledge and exciting patterns which are not stored in the database by analysing the images using various tools. The new era of advanced technology and high storage capability supports the growth of large and detailed image database. This review paper presents a detailed view on the existing research works in the area of image mining and also summarised the different techniques used.

Impact of retinal vascular tortuosity on retinal circulation

Abstract: The retinal microvasculature is a window to the systemic circulation. Systemic diseases, like diabetes and hypertension, are linked to retinal microvascular structure changes (as width, tortuosity, and branching angle). The latter results in a potentially disadvantageous blood flow. This study has been designed to examine the relationship of a retinal vascular tortuosity to both blood pressure and velocity. The geometrical outlines of realistic retinal vascular trees have been extracted from fundus images. The retinal venular tortuosity has been quantitatively measured. A normal tortuosity value has been found, which has not exceeded 1.2. A computational fluid dynamics study has been conducted to examine the effect of topological changes on the hemodynamics distribution in the retinal circulation. The microvascular diameter effect (i.e., Fahraeus---Lindqvist effect) and the hematocrit have been considered in determining the viscosity of the blood in the retinal vessel segments. The pressure drop and the maximum velocity have been in the order of 15 mmHg and 0.032 m/s for tortuous vessels, and 13 mmHg and 0.054 m/s for normal vessels, respectively. For a clinical case, the maximal velocity falls down to 14 % due to the tortuosity. The current results have shown a decrease in the blood velocity and an increase in the pressure drop with tortuosity, which are in good agreement with in vivo measurements reported in the literature.

Rough set-based meta-heuristic clustering approach for social e-learning systems

Abstract: An imperative challenge of Web 2.0 is the way that an incredible measure of information has been incited over a brief time. Tags are generally used to dig and arrange the Web 2.0 resources. Clustering the tag information is exceptionally dreary since the tag space is significant in a few social tagging sites. Tag clustering is the method of collecting the comparative tags into groups. The tag clustering is truly helpful for searching and arranging the Web 2.0 resources furthermore vital for the achievement of social tagging systems. In this paper, the clustering techniques apply to the social e-learning tagging system http://www.pumrpelearning.com; furthermore, we proposed a hybrid tolerance rough set-based particle swarm optimisation TRS-PSO for clustering tags. At that stage, the proposed technique is contrasted with benchmark clustering algorithm k-means with particle swarm optimisation PSO-based grouping method. The exploratory investigation represents the character of the suggested methodology.

Hybrid Bijective soft set - Neural network for ECG arrhythmia classification

Abstract: Reliable identification of arrhythmias built by digital signal processing of Electrocardiogram (ECG) is significant in providing appropriate and suitable treatment to a cardiac arrhythmia patient. Due to exploitation of ECG signals with numerous frequency noises and the occurrence of various arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a thought-provoking task. The objective of this paper is to construct novel automatic classification system for analysis of ECG signal and decision making purposes. The proposed classification method is a hybridization of Bijective soft set and back propagation neural network. The Hybrid Bijective soft set neural network based classification algorithm (BISONN) is applied to classify the ECG signals into normal and four abnormal heart beats. Initially, discrete wavelet transform is applied be- fore classification for signal De-noising and feature extraction. The experimental results are obtained by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The experimental analysis of the proposed BISONN algorithm is compared with the Multi-layered Perceptron (MLP), Decision table (DT), Naive Bayes (NB) and J48 classification algorithms. The performance of the classifier is measured in terms of sensitivity, specificity, Positive predictive value, negative predictive value, false predictive value, Matthews's correlation coefficients, F-Measure, Folke-mallows Index and Kulcznski Index. The acquired results clearly confirm the superiority of the BISONN algorithm as compared with other classifiers.

A Rough Set Based Total Quality Management Approach in Higher Education

Abstract: Contrary to the popular belief that TQM is a poor fit in higher education sector, this research proposes a Rough Set Theory (RST) based model for grading educational institution using TQM parameters. It is a well established fact that TQM needs major reshaping before it can be effectively applied in higher education sector for quality assessment and improvement. This chapter takes a balance view by employing RST approach in TQM architecture and eliminating the much publicized shortcomings of TQM approach. RST theory has advantage of working on a small size of data containing vague and imprecise information which is widely prevalent in education sector. A carefully drafted questionnaire, containing nine attributes, is used for generating research data from the different stake holders in higher educational institutes of India. Nine modified condition attributes are selected on the basis of literature survey and expert views which are subsequently treated with RST analysis. One decision parameter ‘Grade’ depends on nine independent condition attributes. The resultant model contains only two significant attributes namely, ‘Effective Learning and Teaching’ and ‘Administrative Setup’ which can effectively determine the grading of educational institutions. Results of this study may be utilized to improve the higher education quality through appropriate grading mechanism based on self assessment of quality parameters by the different stakeholders of the education sector. The study confirms that TQM can be useful to enhance both quasi-academic areas such as ‘administrative setup’ along with core academic area ‘effective teaching and learning’.

Mathematical Modeling and Analysis of Soft Computing

Abstract: 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China 2Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China 3Centre for Mathematical Imaging Techniques, Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK 4Faculty of Computers and Information, Benha University, Benha 13511, Egypt

Inductive learning based on rough set theory for medical decision making

Abstract: This paper proposes an algorithm that uses inductive learning and rough set theory (ILRS) to analyze the clinical data available in a patient file (records). A typical patient file has unstructured (both descriptive and quantitative) information that is also uncertain and sometimes incomplete. Successful clinical treatments depend on correct medical diagnosis which determines the correct set of variables or features causing a certain pathology. Clinical applications are by no means the only applications that require decision-making with reasoning from a large and incomplete amount of information. We show that the proposed ILRS technique is able to reduce the available number of features into a smaller core set that precisely describes the information system. We can also quantitatively evaluate the level of dependence of the considered pathology, or decision feature, on a given set of condition features or attributes. Moreover, we show that the proposed algorithm is able to cope with uncertain and incomplete information. We consider a case study of an incomplete information system obtained during cannulation of radial and dorsalis pelis arteries. We show how ILRS succeeds to remove redundancy and determine the most significant condition attributes for a given set of decision attributes from contaminated data with uncertainty. A multi-class classification with preference relations is presented through a set of decision rules. Unlike statistical analysis of clinical data, the reliability of the proposed ILRS algorithm is independent of the data size.

Research and development of advanced computing technologies.

Abstract: Welcome to this special issue. Advanced computing technologies are one of the hot research fields in artificial intelligence. This special issue aims to promote the research, development, and applications of advanced computing technologies by providing a high-level international forum for researchers and practitioners to exchange research results and share development experiences. The papers in this edition were selected among the highest rated papers in submitted manuscripts. The selection of papers featured here covers the topics of the main advanced computing technologies and experimental studies of some application systems. C.-W. Lin et al. proposed a privacy-preserving data mining method by using a compact prelarge GA-based algorithm to delete transactions for hiding sensitive itemsets. This method solves the limitations of the evolutionary process by adopting both the compact GA-based mechanism and the prelarge concept. A. Adam et al. build a framework for peak detection on EEG signals in time domain analysis using feature selection and classifier parameters estimation based on particle swarm optimization. The proposed framework tries to find the best combination for all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. Y. Pei et al. propose a method to establish a human model in high dimensional search space by kernel classification for enhancing interactive evolutionary computation search. N. Sulaiman et al. propose a modified artificial bee colony algorithm to enhance the convergence speed and improve the ability of the standard artificial bee colony algorithm to reach the global optimum by balancing the exploration and exploitation processes. This method was tested on the reactive power optimization problem and has outperformed other compared algorithms. Aiming at solving the biased feature selection in random forests, T. T. Nguyen et al. propose a modified random forests algorithm to select good features in learning random forests for high dimensional data. L. Li et al. put forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction and least squares support vector machine by using membrane computing optimization algorithm. M. R. Al-Othman et al. propose a heuristic ranking approach on capacity benefit margin (CBM) determination using Pareto-based evolutionary programming technique. This paper introduces a novel multiobjective approach for CBM assessment taking into account tie-line reliability of interconnected systems and presents a new Pareto-based evolutionary programming (EP) technique used to perform a simultaneous determination of CBM for all areas of the interconnected system. In order to build a QoS-aware bufferless network-on-chip scheme for datacenters, J. Fang et al. propose QBLESS, a QoS-aware bufferless NoC scheme for datacenters. QBLESS consists of two components: a routing mechanism (QBLESS-R) that can substantially reduce flit deflection for high-priority applications and a congestion-control mechanism (QBLESS-CC) that guarantees performance for high-priority applications and improves overall system throughput. S. K. Chandrasekaran et al. propose a framework of primary path reservation admission control protocol, which achieves improved QoS by making use of backup route combined with resource reservation. In this paper, a network topology has been simulated and the present approach proves to be a mechanism that admits the session effectively. V. Uc-Cetina et al. propose a Markov decision process model for solving the web service composition problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate the present approach. S. U. Rehman and A. Nadeem propose a novel approach to the testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. M. Bal et al. study 11 machine learning methods (SVM, MLP, C4.5, etc.) for the inference mechanism of medical decision support system based on ALARM network. The performances of 11 machine learning algorithms are tested on 44 synthetic data sets (11 different dependent variables and 4 different dataset sizes). The comparison of algorithms applied two different tests (statistically difference and average rank tests). C4.5 decision tree is the best algorithm according to both of the tests for our 44 datasets. The datasets having more samples can be better predicted than having fewer samples. A comparative study on interaction of form and motion processing streams by applying two different classifiers in mechanism for recognition of biological movement is proposed by B. Yousefi and C. K. Loo. The presented approach has addressed a very substantial interrelevant comparison of the interaction of two processing streams of mammalian brain visual system. For mining data streams, A. O. Diaz et al. present a fast adapting ensemble method which adapts very quickly to both abrupt and gradual concept drifts, and the method has been specifically designed to deal with recurring concepts. After initializing color image by utilizing the unsupervised Orchard and Bouman clustering technique, D. Khattab et al. present a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. M.-L. Huang et al. combine feature selection and SVM recursive feature elimination to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Meanwhile, Taguchi's method was jointly combined with SVM classifier in order to optimize parameters to increase classification accuracy for multiclass classification. For image compression, N. A. Abu and F. Ernawan propose a psychovisual threshold on the large discrete cosine transform (DCT) image block which will be used to automatically generate the much needed quantization tables. G. C. Kim proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. Z. Wang et al. point out that spectral clustering algorithms applied in community detection have two inadequacies and present a novel community detection algorithm based on topology potential and spectral clustering that contains rich structural information of the network. A. Rodan et al. utilize an ensemble of multilayer perceptrons whose training is obtained using negative correlation learning for predicting customer churn in a telecommunication company. S. Chen et al. describe a hybrid approach for forecasting fraudulent financial statements. The authors firstly screen the important variables using the stepwise regression and then use logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. J. D. Gazzano et al. propose a complete grid infrastructure for distributed high-performance computing based on dynamically reconfigurable FPGAs. This infrastructure was tested and significant performance gains have been achieved. P systems are a class of distributed parallel computing models, H. Peng et al. present a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. Q. Luo et al. propose a discrete bat algorithm (DBA) for optimal problem of permutation flow shop scheduling. The authors construct a direct relationship between the job sequence and the vector of individuals in DBA.

Performance evaluation of several anisotropic diffusion filters for fundus imaging

Abstract: In image processing by partial differential equations, the first and simplest models to have and use are based on linear diffusion. The common difficulty of linear filters is the excessive smoothing that makes track edges difficult. Therefore, we can affirm that any improvement of these linear models must be carried out inside the diffusion operator, thus sacrificing their linearity. The work achieved in this context will make the subject of the following paper. We will see how these difficulties can be overcome by the use of the nonlinear models. This document treats the automatic preprocessing of a retinal vascular network in fundus images, using various anisotropic diffusion filters, in order to improve the interpretation of the images for the doctor's diagnosis. To evaluate the chosen methods, we have performed image enhancement parameters, mean preservation and variance reduction, and edge preservation.