Fuzzy Systems

fuzzy learning algorithms require only a single pass through the training set. This produces a computationally efficient method of learning. The effectiveness of the fuzzy learning algorithms is compared with that of a feedforward neural network trained with back-propagation. P. Allen, "System engineering training at TRW's system development division," presented at 2nd Ann. Conf.

Fuzzy Logic has been proposed for information representation and manipulation in databases, being the current tendency to study mechanisms of flexible querying to traditional databases. SQLf has been one of the efforts in this tendency. On the other hand, there are many different dialects of SQL. There are two major standards: ANSI (American National Standards Institute) SQL and an updated

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation towards solving vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or stateds based on wavelet mutli-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented.

Career guidance for students, particularly in rural areas is a challenging issue in India. In the present era
of digitalization, there is a need of an automated system that can analyze a student for his/her capabilities,
suggest a career and provide related information. Keeping in mind the requirement, the present paper is an
effort in this direction. In this paper, a fuzzy based conceptual framework has been suggested. It has two
parts; in the first part a students will be analyzed for his/her capabilities and in the second part the
available courses, job aspects related to their capabilities will be suggested. To analyze a student, marks
in various subject in 10+2 standards and vocational interest in different fields have been considered and
fuzzy sets have been formed. On example basis, fuzzy inference rules have been framed for analyzing the
abilities in engineering, medical and hospitality fields only. In second part, concept of composition of
relations has been used to suggest the related courses and jobs.

Abstract After decades from introducing and using agile methodologies, project managers realized that no methodology is sufficient by itself. Thus, merging their principles is the solution yet no formal solution has been proposed. Relying on previous work, ATT ...

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are contributed by temperature, gas flow, and pressure. Error compensation is a key problem for these control systems. In recent years, it has been proven in the literature that artificial neural network (ANN) is a reliable and low cost solution to manage errors. Among all the algorithms of ANN, the back propagation is commonly used because of its simplicity and learning methodology is easy to realize. However, it has two notable drawbacks: (a) it is likely to run into local minimum, and (b) convergence is slow. Thermal conductivity gas analyzer often works in adverse surroundings, which requires fast and accurate measurements. Therefore, a strong learning network is needed. This paper proposes a novel thermal gas analyzer using adaptive neuro-fuzzy inference system. The effectiveness and validity of the proposed method is verified by simulation studies using MATLAB. Fuzzy membership rules are created to allow regulation of learning parameters. Further, the fuzzy adaptive network model is constructed to train large data samples while the high precision compensation of sensor error is realized by the improved flow. Simulation results reveal that the convergence speed and output accuracy is improved and the learning parameters in thermal gas analyzer are automatically corrected by the proposed method in comparison with the back propagation algorithm of artificial neural network.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as function approximators.

The evolutionary based computational approach has been matured into an extensive collection of concepts and techniques with the hype in the complexities of the problems which could not be easily dealt by the classical approach. The effectiveness and efficiency of these modern approaches are making them popular in the field of research and development. In this paper, we have focused on the involvement of a multi valued logical evolutionary based computational approach in the development of an effective system. This paper describes the area where fuzzy logic can be implemented effectively in system development life cycle. This paper explicitly mentions the application of Fuzzy Inference System (FIS) in one of the fact finding techniques of requirement analysis viz. rating scale question method to find the percentage level of the satisfaction of the user of an existing system or a new system.

This paper describes the architecture and control design of an autonomous Electric Boat, together with a specific simulation environment for training and testing the Fuzzy Inference Systems. The boat will be in charge to exit and enter from harbors, plan and follow a route, avoid obstacles such as other boats, correct its motion, perform a virtual anchor and switch between these operations autonomously. The boat is equipped with a set of smart sensors such as sonars, a Global Positioning System, a camera-based vision system and an Inertial Measurement Unit. General navigation rules are respected during the route. We propose an architecture integrating several Fuzzy Controller-based modular pipelines. Furthermore, we propose a mathematical formalization of the Fish Schooling Behavior useful for training Fuzzy Controllers through Q-Learning. Our architecture will soon be implemented on a real boat intended for navigating in inland waters.

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy inference systems (FISs) are introduced in this article. The IT2FFISs, which are outlined in this article, add to the armamentarium of FISs some particular concepts such as interval type-2 fractional membership functions, type-2 fractional translation rule, type-2 fracture index, the concept of switching, the entanglement, the degeneracy concept, and so forth. An IT2FFIS exploits not only the tolerance for the uncertainty in the interpretation of the meaning of a word, but also the relevance between the quality and quantity levels of the given information to infer an answer to an inference query. The IT2FFISs make an increase in machine intelligence quotient possible by an increase in the range of FISs order rather than their type. Moreover, the synergy of the concepts coming with various modes of IT2FFISs such as the aggressive mode opens a gate to a space of fuzzy systems outputs which used to be indiscoverable. Furthermore, it is demonstrated that as the type-2 fracture index approaches zero, the space of IT2FFISs outputs contracts and eventually it coincides the space of IT2FISs output when the fracture index is equal to zero. It is also proved that, provided a particular order of the IT2FFIS is taken into account, independent of the problem in question, a typical IT2FIS never leads to results which are more satisfactory than those obtained by the IT2FFIS corresponding to the typical IT2FIS.

Fuzzy models are regarded as linguistic modeling structures with well-defined functional blocks off inlput and output interfaces along with a processing module. The paper examines the functions of these modules and specifies the relevant optimization tasks emerging in fuzzy system identification. Considering several distinct levels of conceptual memorization realized within the fuzzy imodels ( subsequently resulting in establishing short-, medium-, and long-term memories), the corresponding learning policies are developed. The study includes also detailed simulation studies.

The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. The basic properties and examples of dual numerical means are considered.

Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a T-norm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the Aczel-Alsina operator in terms of the classification accuracy, the second best operator is Dubois-Prade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).

The difficulties associated with translating a set of scores into letter-grades are discussed. A novel method for automating this process, the Fuzzy Grading System, is developed and compared to traditional practices. Letter-grades are recognized to be f u u y descriptors of students' performance. Thus, operations aimed at defining letter-grade bouhdaries are naturally carried out in the context of fuzzy set theory and logic. The Fuzzy Grading System utilizes students' and instructor's performance measures in order to m d i y a set of collectively approved, a priori fuzzy grades, so as to produce a "fair" mark distribution. The validity of grades is increased by compensating for factors that are not directly accessible to simpler, traditional grading methods.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.

An Autonomous Mobile Robot is an artificially intelligent vehicle capable of traveling in unknown and unstructured environments independently. Among the proposed approaches in the literature to handle the navigation problem of a mobile robot is the simple fuzzy reactive approach. This approach, however, occasionally suffers from two major problems, i.e., escaping from trap situations and the combinatorial explosion of the if-then rules in the inference engine. This paper presents a neuro-fuzzy reasoning approach for mobile robot navigation. The proposed approach has the advantage of greatly reducing the number of if-then rules by introducing weighting factors for the sensor inputs, thus inferring the reflexive conclusions from each input to the system rather than putting all the possible states of all the inputs to infer a single conclusion. Four simple neural networks are used to determine the weighting factors. Each neural network is responsible for determining the weighting facto...

The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.

International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

The control and operation of electronic systems
relies and depends on the availability of  the  power supply.
Rechargeable batteries have been more pervasively used as
the energy storage and power source for various electrical and electronic systems and devices, such as communication
systems, electronic devices, renewable power systems, electric vehicles, etc. However, the rechargeable batteries are
subjected to the availability of the external power source when it is drained out. Because of the concern of battery life,
environmental pollution and a possible energy crisis, the
renewable solar energy has received an increasing attention
in recent years. A fuzzy logic control based grid tied
uninterruptible power supply integrating renewable solar
energy can be used for electrical and electronic systems to
produce power generation. This paper presents the design and implementation of fuzzy logic control based grid tied
uninterruptible power supply integrating the renewable  solar power energy system. The uninterruptible power supply (UPS) system is characterized by the rechargeable battery that is connected  with  the Photovoltaic Panel through the DC/DC converter,  the  utility AC through the AC/DC  converter  and the load is connected through the DC/AC converter. The whole operation is controlled by the fuzzy logic algorithm. A complete hardware prototype system model of the fuzzy logic control based  on the  grid tied uninterruptible power supply integrating with the  renewable solar energy is designed and implemented. The operation and effectiveness of the proposed system is  then  demonstrated by  the  actual and  real-time implementation of  the  fuzzy logic control grid tied operation 
uninterruptible power supply integrating renewable  solar
energy connected to the rechargeable battery bank and a PIC
microcontroller platform for fuzzy logic control and operation.

This paper is devoted to the development of a fast industrial design of an embedded fuzzy controller for an OL unstable plant starting from an initial fuzzy system developed in conjunction with an expert. Through prototyping and implementation on a general-purpose microprocessor the CL plant can then be tested and used to iteratively identify and control the plant until the desired control system criteria is achieved. This paper describes the design, implementation and testing of an embedded fuzzy controller as a simple, practical solution to a regulation problem in reefer refrigeration systems. Fuzzy logic was utilized for the ease with which it enables expert opinion expressed within the framework of a natural language to be converted into a PID-like control algorithm. By using the Motorola HC12 MCU with its inbuilt fuzzy logic instruction set a rapid prototype design was implemented and the application of fuzzy logic and iterative identification and control to general industrial refrigeration systems was found to be a possible economically feasible solution.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.

The attributes of fuzzy theory have been successfully explored and implemented in this paper for performing the power flow analysis in a fuzzy environment. A fuzzy system is being proposed in this paper to solve power flow problem under different loading and under contingency conditions. Power flow analysis is carried out for computing bus voltage magnitude, voltage angle, real & reactive power of the system. The fuzzy system has many advantageous features such as optimized system complexity, control of power flow, control of non-linear system etc. The composition of the input variable for the proposed fuzzy logic based power flow method has been selected to emulate the solution process of a conventional power flow. The effectiveness of the fuzzy based power flow is demonstrated by computation of bus voltage magnitude & voltage angle for different loading condition & for single line outage contingencies in 3-bus and 6bus systems.

Powell-Sabin splines are piecewise quadratic polynomials with global C 1 -continuity. They are defined on conformal triangulations of two-dimensional domains, and admit a compact representation in a normalized B-spline basis. Recently, these splines have been used successfully in the area of computer-aided geometric design for the modelling and fitting of surfaces. In this paper, we discuss the applicability of Powell-Sabin splines for the numerical solution of partial differential equations defined on irregular domains. A Galerkin-type PDE discretisation is derived, and elaborated for the variable coefficient diffusion equation. Special emphasis goes to the treatment of Dirichlet and Neumann boundary conditions. Finally, an error estimator is developed and an adaptive mesh refinement strategy is proposed. We will illustrate the effectiveness of the approach by means of some numerical experiments.

In recent decades, intuitionistic fuzzy sets have been applied to many different fields; however, few current studies have used the ELECTRE method to solve multi-criteria decision-making problems with intuitionistic fuzzy information. The purpose of this paper is to develop a new method, the intuitionistic fuzzy ELECTRE method, for solving multi-criteria decision-making problems. Atanassov's intuitionistic fuzzy set (A-IFS) characteristics are simultaneously concerned with the degree of membership, degree of non-membership, and intuitionistic index, and people can use A-IFS to describe uncertain situations in decision-making problems. We use the proposed method to rank all alternatives and determine the best alternative. The proposed method can also use imperfect or insufficient knowledge of data to deal with decision-making problems. Finally, two practical examples are given that illustrate the procedure of the proposed method.

11 th International Conference on Cryptography and Information Security (CRYPIS 2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of Cryptography, Coding and Information security. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Decision making both on individual and organizational level is always accompanied by the search of other's opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

We report on a fuzzy logic-based language understanding system applied to speech recognition. This system acquires conceptual knowledge from corpus data and organizes such knowledge into fuzzy logic inference rules. The system parses speech recognition results into conceptual structures in a robust manner, and thus is able to tolerate noise caused by speech recognition errors. We discuss the fuzzy inference rule learning method and explain its organization. Experimental results that demonstrate the ability of the system to deal with complex speech input instances are reported

The problem of energy conservation in buildings is a multidimensional one. Researchers from a variety of disciplines have been working on this problem. It remains a challenging and yet rewarding study. In the past three decades, a plethora of scientific and technological publications on energy conservation in buildings have been presented in international journals. In this work, we discuss the potentiality of artificial intelligence (AI) as a design tool in building an automation system. The application of contemporary AI techniques creates intelligent buildings with the following main goals: energy efficiency, comfort, health and productivity in living spaces. Two modern domains of AI that are widely used in buildings are computational intelligence (CI) or soft computing and distributed artificial intelligence (DAI). DAI includes intelligent agents (IAs), multiagent systems (MASs) and ambient intelligence. However, there is a lack of systematic review of research efforts and achievements mainly on IA and MAS domains. This chapter briefly presents expert systems and CI techniques and outlines how they operate. The major objective of this chapter is to illustrate how IAs and MASs may play an important role in conserving energy in buildings.

Cloud Computing is an emerging technology. It is a growing technology which can change traditional IT systems. It plays a major role in today’s technology sector. People are using it every day through one way or another. Education sector is not out of this phenomenon. At the present time the teaching method is changing and students are becoming much technology based and therefore it is necessary that we think about the most recent technologies to incorporate in the teaching and learning methods. By sharing Information technology related services in the cloud, educational institutions can better concentrate on offering students, teachers, faculty and staff the essential instruments. Bangladesh is a developing country. So applying this technology on education sector is a huge challenge for Bangladesh. In this paper it is discussed that how Bangladesh can be benefited by applying cloud in education and its challenges followed by some case studies and success stories.

International Journal of Wireless Network Security (IJWNS) 
http://flyccs.com/jounals/IJWNS/Home.html
Scope
International Journal of Wireless Network Security (IJWNS) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.
Topics of interest include, but are not limited to, the following
• Cryptographic protocols
• Cryptography and Coding
• Untraceability
• Privacy and authentication
• Key management
• Authentication
• Trust Management
• Quantum cryptography
• Computational Intelligence in Security
• Artificial Immune Systems
• Biological & Evolutionary Computation
• Intelligent Agents and Systems
• Reinforcement & Unsupervised Learning
• Autonomy-Oriented Computing
• Coevolutionary Algorithms
• Fuzzy Systems
• Biometric Security
• Trust models and metrics
• Regulation and Trust Mechanisms
• Data Integrity
• Models for Authentication, Trust and Authorization
• Information Hiding • Information Hiding
• Data & System Integrity
• E- Commerce
• Access Control and Intrusion Detection
• Intrusion Detection and Vulnerability Assessment
• Authentication and Non-repudiation
• Identification and Authentication
• Insider Threats and Countermeasures
• Intrusion Detection & Prevention
• Secure Cloud Computing
• Security Information Systems Architecture and Design and Security Patterns
• Security Management
• Cryptography and Information Security
• Security Requirements (threats, vulnerabilities, risk, formal methods, etc.)
• Sensor and Mobile Ad Hoc Network Security
• Service and Systems Design and QoS Network Security
• Software Security
• Security and Privacy in Mobile Systems
• Security and Privacy in Pervasive/Ubiquitous Computing
• Security and Privacy in Web Sevices
• Security and Privacy Policies
• Security Area Control
• Security Deployment•
Paper Submission
Authors are invited to submit papers for this journal through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For paper format download the template in this page Manuscript Template
Important Dates

Submission Deadline : October 27, 2018
Notification :November 27, 2018
Final Manuscript Due :December 05, 2018
Publication Date : Determined by the Editor-in-Chief

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A counter challenge authentication method is presented for authentication of online users of web applications. The authentication method involves a counter challenge from a user to a web application asking to provide certain information from one or more user details recorded at the time of registration. The user enters his password and logs into the web application only in case the correct answer is received from the web application. This advanced authentication method protects online application users from phishing attacks. An incorrect answer or inability of the web application to provide the correct answer to the challenge is a clear indication of a phishing attack, thereby alerting the user and stopping submission of password to phishers. The authentication method is computer independent and eliminates dependency on two-factor authentication, hardware tokens, client software installations, digital certificates, and user defined seals.

This paper presents a Lyapunov-based switching controller design method for non-linear systems using Takagi-Sugeno fuzzy models. The basic idea of the proposed approach is to represent the fuzzy model as a set of uncertain linear systems. The controller is obtained by solving the corresponding set of Algebraic Ricatti Equations (AREs). A simulation example is given to illustrate the effectiveness of this approach.