Search results for: Fuzzy logic method

Authors: Sana Bouzaida, Anis Sakly, Faouzi M'Sahli

Abstract:

In this paper, a TSK-type Neuro-fuzzy Inference System that combines the features of fuzzy sets and neural networks has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA).

Keywords: Identification, Shuffled frog Leaping Algorithm (SFLA), TSK-type neuro-fuzzy model.

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Authors: Jeonghoon Han, Hanjae Jeong, Dongho Won, Seungjoo Kim

Abstract:

As the Internet technology has developed rapidly, the number of identities (IDs) managed by each individual person has increased and various ID management technologies have been developed to assist users. However, most of these technologies are vulnerable to the existing hacking methods such as phishing attacks and key-logging. If the administrator-s password is exposed, an attacker can access the entire contents of the stolen user-s data files in other devices. To solve these problems, we propose here a new ID management scheme based on a Single Password Protocol. The paper presents the details of the new scheme as well as a formal analysis of the method using BAN Logic.

Keywords: Anti-phishing, BAN Logic, ID management.

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Authors: Rafat Alshorman, Walter Hussak

Abstract:

Existing work in temporal logic on representing the execution of infinitely many transactions, uses linear-time temporal logic (LTL) and only models two-step transactions. In this paper, we use the comparatively efficient branching-time computational tree logic CTL and extend the transaction model to a class of multistep transactions, by introducing distinguished propositional variables to represent the read and write steps of n multi-step transactions accessing m data items infinitely many times. We prove that the well known correspondence between acyclicity of conflict graphs and serializability for finite schedules, extends to infinite schedules. Furthermore, in the case of transactions accessing the same set of data items in (possibly) different orders, serializability corresponds to the absence of cycles of length two. This result is used to give an efficient encoding of the serializability condition into CTL.

Keywords: computational tree logic, serializability, multi-step transactions.

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Authors: Suraiya Jabin, Kamal K. Bharadwaj

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This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system.

Keywords: Hierarchical Production Rule, Data Mining, Learning Classifier System, Fuzzy Subsumption Relation, Subsumption matrix, Reinforcement Learning.

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Authors: Shikha Maheshwari, Amit Srivastava

Abstract:

In medical investigations, uncertainty is a major challenging problem in making decision for doctors/experts to identify the diseases with a common set of symptoms and also has been extensively increasing in medical diagnosis problems. The theory of cross entropy for intuitionistic fuzzy sets (IFS) is an effective approach in coping uncertainty in decision making for medical diagnosis problem. The main focus of this paper is to propose a new intuitionistic fuzzy cross entropy measure (IFCEM), which aid in reducing the uncertainty and doctors/experts will take their decision easily in context of patient’s disease. It is shown that the proposed measure has some elegant properties, which demonstrates its potency. Further, it is also exemplified in detail the efficiency and utility of the proposed measure by using a real life case study of diagnosis the disease in medical science.

Keywords: Intuitionistic fuzzy cross entropy (IFCEM), intuitionistic fuzzy set (IFS), medical diagnosis, uncertainty.

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Authors: Tanuj Kumar, Rakesh Kumar Bajaj

Abstract:

In the present paper, we analyze the vague reliability of k-out-of-n : G system (particularly, series and parallel system) with independent and non-identically distributed components, where the reliability of the components are unknown. The reliability of each component has been estimated using statistical confidence interval approach. Then we converted these statistical confidence interval into triangular intuitionistic fuzzy numbers. Based on these triangular intuitionistic fuzzy numbers, the reliability of the k-out-of-n : G system has been calculated. Further, in order to implement the proposed methodology and to analyze the results of k-out-of-n : G system, a numerical example has been provided.

Keywords: Vague set, vague reliability, triangular intuitionistic fuzzy number, k-out-of-n : G system, series and parallel system.

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Authors: Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya

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As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.

Keywords: Data mining, Decision tree, Feature selection, Fuzzyc- means clustering, Intrusion detection.

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Authors: T. Benmiloud, A. Omari

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This paper presents a new technique of compensation of the effect of variation parameters in the direct field oriented control of induction motor. The proposed method uses an adaptive tuning of the value of synchronous speed to obtain the robustness for the field oriented control. We show that this adaptive tuning allows having robustness for direct field oriented control to changes in rotor resistance, load torque and rotational speed. The effectiveness of the proposed control scheme is verified by numerical simulations. The numerical validation results of the proposed scheme have presented good performances compared to the usual direct-field oriented control.

Keywords: Induction motor, direct field-oriented control, compensation of variation parameters, fuzzy logic controller.

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Authors: Sanjay K.Srivastava, Bhanu Gupta

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In this paper some results on strict stability heve beeb extended for fuzzy differential equations with impulse effect using Lyapunov functions and Razumikhin technique.

Keywords: Fuzzy differential equations, Impulsive differential equations, Strict stability, Lyapunov function, Razumikhin technique.

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Authors: Emanuele Stomeo, Tatiana Kalganova, Cyrille Lambert

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Evolvable hardware (EHW) refers to a selfreconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). A lot of research has been done in this area several different EA have been introduced. Every time a specific EA is chosen for solving a particular problem, all its components, such as population size, initialization, selection mechanism, mutation rate, and genetic operators, should be selected in order to achieve the best results. In the last three decade a lot of research has been carried out in order to identify the best parameters for the EA-s components for different “test-problems". However different researchers propose different solutions. In this paper the behaviour of mutation rate on (1+λ) evolution strategy (ES) for designing logic circuits, which has not been done before, has been deeply analyzed. The mutation rate for an EHW system modifies values of the logic cell inputs, the cell type (for example from AND to NOR) and the circuit output. The behaviour of the mutation has been analyzed based on the number of generations, genotype redundancy and number of logic gates used for the evolved circuits. The experimental results found provide the behaviour of the mutation rate to be used during evolution for the design and optimization of logic circuits. The researches on the best mutation rate during the last 40 years are also summarized.

Keywords: Evolvable hardware, mutation rate, evolutionarycomputation, design of logic circuit.

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Authors: Tulika Bhattacharjee, A. K.Chakraborty

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Due to the deregulation of the Electric Supply Industry and the resulting emergence of electricity market, the volumes of power purchases are on the rise all over the world. In a bid to meet the customer-s demand in a reliable and yet economic manner, utilities purchase power from the energy market over and above its own production. This paper aims at developing an optimal power purchase model with two objectives viz economy and environment ,taking various functional operating constraints such as branch flow limits, load bus voltage magnitudes limits, unit capacity constraints and security constraints into consideration.The price of purchased power being an uncertain variable is modeled using fuzzy logic. DEMO (Differential Evolution For Multi-objective Optimization) is used to obtain the pareto-optimal solution set of the multi-objective problem formulated. Fuzzy set theory has been employed to extract the best compromise non-dominated solution. The results obtained on IEEE 30 bus system are presented and compared with that of NSGAII.

Keywords: Deregulation, Differential Evolution, Multi objective Optimization, Pareto Optimal Set, Optimal Power Flow

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Authors: Hamed Rafiei, Masoud Rabbani

Abstract:

This paper addresses one of the most important issues have been considered in hybrid MTS/MTO production environments. To cope with the problem, a mathematical programming model is applied from a tactical point of view. The model is converted to a fuzzy goal programming model, because a degree of uncertainty is involved in hybrid MTS/MTO context. Finally, application of the proposed model in an industrial center is reported and the results prove the validity of the model.

Keywords: Fuzzy sets theory, Hybrid MTS/MTO, Order penetration point, Quadratic programming.

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Authors: Zhu Jiaming, Yu Mengna

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In this article, models based on quantitative analysis, physical geometry and regression analysis are established, by using analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy photographic and data fitting. The reasons of various leaf shapes among different species and the differences between the leaf shapes on same tree have been solved by using software, such as Eviews, VB and Matlab. We also successfully estimate the leaf mass of a tree and the correlation with the tree profile.

Keywords: Leaf shape; Mass; Fuzzy cluster; Regression analysis; Eviews; Matlab

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Authors: J.L. Castro, K. Benghazi, M.V. Hurtado, M. Navarro, J.M. Zurita

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Nowadays, quick technological changes force companies to develop innovative products in an increasingly competitive environment. Therefore, how to enhance the time of new product development is very important. This design problem often lacks the exact formula for getting it, and highly depends upon human designers- past experiences. For these reasons, in this work, a Casebased reasoning (CBR) system to assist in new product development is proposed. When a case is recovered from the case base, the system will take into account not only the attribute-s specific value and how important it is. It will also take into account if the attribute has a positive influence over the product development. Hence the manufacturing time will be improved. This information will be introduced as a new concept called “adaptability". An application to this method for hearing instrument new design illustrates the proposed approach.

Keywords: Case based reasoning, Fuzzy logic, New product development, Retrieval stage, Similarity.

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Authors: Ahmed Chalak Shakir, GuXuemai, Jia Min

Abstract:

Recently, wireless sensor networks have been paid more interest, are widely used in a lot of commercial and military applications, and may be deployed in critical scenarios (e.g. when a malfunctioning network results in danger to human life or great financial loss). Such networks must be protected against human intrusion by using the secret keys to encrypt the exchange messages between communicating nodes. Both the symmetric and asymmetric methods have their own drawbacks for use in key management. Thus, we avoid the weakness of these two cryptosystems and make use of their advantages to establish a secure environment by developing the new method for encryption depending on the idea of code conversion. The code conversion-s equations are used as the key for designing the proposed system based on the basics of logic gate-s principals. Using our security architecture, we show how to reduce significant attacks on wireless sensor networks.

Keywords: logic gates, code conversions, Gray-code, and clustering.

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Authors: Elizabeth B. Varghese, M. Wilscy

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A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: Nigina Toktasynova, Sholpan Sagyndykova, Zhanat Kenzhebayeva, Maksat Kalimoldayev, Mariya Ishimova, Irbulat Utepbergenov

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To solve these problems, we investigated the management system of heating enterprise, including strategic planning based on the balanced scorecard (BSC), quality management in accordance with the standards of the Quality Management System (QMS) ISO 9001 and analysis of the system based on expert judgment using fuzzy inference. To carry out our work we used the theory of fuzzy sets, the QMS in accordance with ISO 9001, BSC, method of construction of business processes according to the notation IDEF0, theory of modeling using Matlab software simulation tools and graphical programming LabVIEW. The results of the work are as follows: We determined possibilities of improving the management of heat-supply plant-based on QMS; after the justification and adaptation of software tool it has been used to automate a series of functions for the management and reduction of resources and for the maintenance of the system up to date; an application for the analysis of the QMS based on fuzzy inference has been created with novel organization of communication software with the application enabling the analysis of relevant data of enterprise management system. 

Keywords: Balanced scorecard, heat supply, quality management system, the theory of fuzzy sets.

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Authors: Omid Sharifi, Member, ACM, Zeki Bayram, Member, ACM

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We demonstrate through a sample application, Ebanking, that the Web Service Modelling Language Ontology component can be used as a very powerful object-oriented database design language with logic capabilities. Its conceptual syntax allows the definition of class hierarchies, and logic syntax allows the definition of constraints in the database. Relations, which are available for modelling relations of three or more concepts, can be connected to logical expressions, allowing the implicit specification of database content. Using a reasoning tool, logic queries can also be made against the database in simulation mode.

Keywords: Semantic web, ontology, E-banking, database, WSML, WSMO, E-R diagram.

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Authors: M.A. Lazim, M. Suriani

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Knowing consumers' preferences and perceptions of the sensory evaluation of drink products are very significant to manufacturers and retailers alike. With no appropriate sensory analysis, there is a high risk of market disappointment. This paper aims to rank the selected coffee products and also to determine the best of quality attribute through sensory evaluation using fuzzy decision making model. Three products of coffee drinks were used for sensory evaluation. Data were collected from thirty judges at a hypermarket in Kuala Terengganu, Malaysia. The judges were asked to specify their sensory evaluation in linguistic terms of the quality attributes of colour, smell, taste and mouth feel for each product and also the weight of each quality attribute. Five fuzzy linguistic terms represent the quality attributes were introduced prior analysing. The judgment membership function and the weights were compared to rank the products and also to determine the best quality attribute. The product of Indoc was judged as the first in ranking and 'taste' as the best quality attribute. These implicate the importance of sensory evaluation in identifying consumers- preferences and also the competency of fuzzy approach in decision making.

Keywords: fuzzy decision making, fuzzy linguistic, membership function, sensory evaluation,

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Authors: B. Dora Arul Selvi, .N. Kamaraj

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Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.

Keywords: Fuzzy Support Vector Machine (FSVM), Incremental Cost, Preventive Control, Transient stability

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Authors: V. S. Meenakshi, G. Padmavathi

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Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person-s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. Therefore, providing security to the stored biometric template is very crucial. Crypto biometric systems are authentication systems, which blends the idea of cryptography and biometrics. Fuzzy vault is a proven crypto biometric construct which is used to secure the biometric templates. However fuzzy vault suffer from certain limitations like nonrevocability, cross matching. Security of the fuzzy vault is affected by the non-uniform nature of the biometric data. Fuzzy vault when hardened with password overcomes these limitations. Password provides an additional layer of security and enhances user privacy. Retina has certain advantages over other biometric traits. Retinal scans are used in high-end security applications like access control to areas or rooms in military installations, power plants, and other high risk security areas. This work applies the idea of fuzzy vault for retinal biometric template. Multimodal biometric system performance is well compared to single modal biometric systems. The proposed multi modal biometric fuzzy vault includes combined feature points from retina and fingerprint. The combined vault is hardened with user password for achieving high level of security. The security of the combined vault is measured using min-entropy. The proposed password hardened multi biometric fuzzy vault is robust towards stored biometric template attacks.

Keywords: Biometric Template Security, Crypto Biometric Systems, Hardening Fuzzy Vault, Min-Entropy.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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This paper introduces new algorithms (Fuzzy relative of the CLARANS algorithm FCLARANS and Fuzzy c Medoids based on randomized search FCMRANS) for fuzzy clustering of relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd) in which the within cluster dissimilarity of each cluster is minimized in each iteration by recomputing new medoids given current memberships, FCLARANS minimizes the same objective function minimized by FCMdd by changing current medoids in such away that that the sum of the within cluster dissimilarities is minimized. Computing new medoids may be effected by noise because outliers may join the computation of medoids while the choice of medoids in FCLARANS is dictated by the location of a predominant fraction of points inside a cluster and, therefore, it is less sensitive to the presence of outliers. In FCMRANS the step of computing new medoids in FCMdd is modified to be based on randomized search. Furthermore, a new initialization procedure is developed that add randomness to the initialization procedure used with FCMdd. Both FCLARANS and FCMRANS are compared with the robust and linearized version of fuzzy c-medoids (RFCMdd). Experimental results with different samples of the Reuter-21578, Newsgroups (20NG) and generated datasets with noise show that FCLARANS is more robust than both RFCMdd and FCMRANS. Finally, both FCMRANS and FCLARANS are more efficient and their outputs are almost the same as that of RFCMdd in terms of classification rate.

Keywords: Data Mining, Fuzzy Clustering, Relational Clustering, Medoid-Based Clustering, Cluster Analysis, Unsupervised Learning.

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Authors: Noura Boudiaf, Allaoua Chaoui

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One major difficulty that faces developers of concurrent and distributed software is analysis for concurrency based faults like deadlocks. Petri nets are used extensively in the verification of correctness of concurrent programs. ECATNets [2] are a category of algebraic Petri nets based on a sound combination of algebraic abstract types and high-level Petri nets. ECATNets have 'sound' and 'complete' semantics because of their integration in rewriting logic [12] and its programming language Maude [13]. Rewriting logic is considered as one of very powerful logics in terms of description, verification and programming of concurrent systems. We proposed in [4] a method for translating Ada-95 tasking programs to ECATNets formalism (Ada-ECATNet). In this paper, we show that ECATNets formalism provides a more compact translation for Ada programs compared to the other approaches based on simple Petri nets or Colored Petri nets (CPNs). Such translation doesn-t reduce only the size of program, but reduces also the number of program states. We show also, how this compact Ada-ECATNet may be reduced again by applying reduction rules on it. This double reduction of Ada-ECATNet permits a considerable minimization of the memory space and run time of corresponding Maude program.

Keywords: Ada tasking, ECATNets, Algebraic Petri Nets, Compact Representation, Analysis, Rewriting Logic, Maude.

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Authors: Lazim Abdullah, N orhanadiah Zam

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Increasing number of vehicles and lack of awareness among road users may lead to road accidents. However no specific literature was found to rank vehicles involved in accidents based on fuzzy variables of road users. This paper proposes a ranking of four selected motor vehicles involved in road accidents. Human and non-human factors that normally linked with road accidents are considered for ranking. The imprecision or vagueness inherent in the subjective assessment of the experts has led the application of fuzzy sets theory to deal with ranking problems. Data in form of linguistic variables were collected from three authorised personnel of three Malaysian Government agencies. The Multi Criteria Decision Making, fuzzy TOPSIS was applied in computational procedures. From the analysis, it shows that motorcycles vehicles yielded the highest closeness coefficient at 0.6225. A ranking can be drawn using the magnitude of closeness coefficient. It was indicated that the motorcycles recorded the first rank.

Keywords: Road accidents, decision making, closeness coefficient, fuzzy number

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Authors: Ayman A. Aly, Abdallah A. Alshnnaway

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The general idea behind the filter is to average a pixel using other pixel values from its neighborhood, but simultaneously to take care of important image structures such as edges. The main concern of the proposed filter is to distinguish between any variations of the captured digital image due to noise and due to image structure. The edges give the image the appearance depth and sharpness. A loss of edges makes the image appear blurred or unfocused. However, noise smoothing and edge enhancement are traditionally conflicting tasks. Since most noise filtering behaves like a low pass filter, the blurring of edges and loss of detail seems a natural consequence. Techniques to remedy this inherent conflict often encompass generation of new noise due to enhancement. In this work a new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of three stages. (1) Define fuzzy sets in the input space to computes a fuzzy derivative for eight different directions (2) construct a set of IFTHEN rules by to perform fuzzy smoothing according to contributions of neighboring pixel values and (3) define fuzzy sets in the output space to get the filtered and edged image. Experimental results are obtained to show the feasibility of the proposed approach with two dimensional objects.

Keywords: Additive noise, edge preserving filtering, fuzzy image filtering, noise reduction, two dimensional mechanical images.

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Authors: Rishabh Sharma, Renu Vig, Neeraj Sharma

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The main design parameter of WSN (wireless sensor network) is the energy consumption. To compensate this parameter, hierarchical clustering is a technique that assists in extending duration of the networks life by efficiently consuming the energy. This paper focuses on dealing with the WSNs and the FIS (fuzzy interface system) which are deployed to enhance the BEENISH protocol. The node energy, mobility, pause time and density are considered for the selection of CH (cluster head). The simulation outcomes exhibited that the projected system outperforms the traditional system with regard to the energy utilization and number of packets transmitted to sink.

Keywords: Wireless sensor network, sink, sensor node, routing protocol, fuzzy rule, fuzzy inference system.

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Authors: Xinhua Zhang, Kelin Li

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In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.

Keywords: Exponential stability, stochastic fuzzy cellular neural networks, time-varying delays, impulses, reaction-diffusion terms.

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Authors: Preeda Sansakorn, Min An

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In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: Safety risks assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects.

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Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Neural network, Load Forecasting, Fuzzy inference, Machine learning, Fuzzy modeling and rule extraction, Support Vector Regression.

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Authors: Hamed Qahri Saremi, Gholam Ali Montazer

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With the proliferation of World Wide Web, development of web-based technologies and the growth in web content, the structure of a website becomes more complex and web navigation becomes a critical issue to both web designers and users. In this paper we define the content and web pages as two important and influential factors in website navigation and paraphrase the enhancement in the website navigation as making some useful changes in the link structure of the website based on the aforementioned factors. Then we suggest a new method for proposing the changes using fuzzy approach to optimize the website architecture. Applying the proposed method to a real case of Iranian Civil Aviation Organization (CAO) website, we discuss the results of the novel approach at the final section.

Keywords: Web content, Web navigation, Website system, Webusage mining.

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