Genetic Fuzzy Systems

A very widely used drive strategy for PMSM is the field oriented control (FOC), which was proposed in 1971 for induction motors (IMs). However, the FOC scheme is quite complex due to the reference frame transformation and its high dependence upon the motor parameters and speed. To mitigate these problems, a new control strategy for the torque control of induction motor was developed by Takahashi known as the direct torque control (DTC) and by Depenbrock as the direct self control (DSC). The basic direct torque control (DTC) scheme may cause undesired torque, flux and current ripples because of the small number of applicable voltage vectors. The control system should be able to generate any voltage vector, implying the use of space vector modulation (SVM) which complicates the control scheme. The discrete space vector modulation (DSVM) method was proposed for DTC to overcome this problem which replaces the simple switching table by several switching tables, to apply a combination of three voltage vectors in the same sampling period. In this paper, after a brief review of the primary concept of DSVM DTC technique, a new scheme of DSVM DTC for PMSM is proposed with a new set of switching tables taking into account the motor speed and the absolute values of torque and flux feedback errors. In one fixed sampling time interval, three vectors are applied to the motor including the two null vectors. Comparisons of the basic DTC and the improved DSVM DTC schemes are made based on the system performance and switching loss. For this purpose the DSVM technique uses prefixed time intervals within a sampling cycle to synthesize a higher number of voltage vectors than the basic DTC scheme. A set of switching table is carried out to minimize the torque error. An optimal vector selector is developed to reduce the switching loss and make the system more stable. The sampling period does not need to be doubled in order to achieve a mean switching frequency practically equal to that of the basic DTC scheme. For a comparable performance, the switching loss of the proposed scheme is less than that of the basic DTC method. The vector application sequence is investigated and an optimal algorithm is developed to reduce the switching loss and torque ripple. Simulation and experiments on the improved DSVM DTC are carried out and compared with those on the basic DTC scheme.

The development of probability theory together with the Bayesian approach in the three last centuries is caused by two factors: the variability of the physical phenomena and partial ignorance about them. As now it is standard to believe [Dubois, 2007], the nature of these key factors is so various, that their descriptions are required special uncertainty theories, which differ from the probability theory and the Bayesian credo, and provide a better account of the various facets of uncertainty by putting together probabilistic and set-valued representations of information to catch a distinction between variability and ignorance. Eventology [Vorobyev, 2007], a new direction of probability theory and philosophy, offers the original event approach to the description of variability and ignorance, entering an agent, together with his/her beliefs, directly in the frameworks of scientific research in the form of eventological distribution of his/her own events. This allows eventology, by putting together probabilistic and set-event representation of information and philosophical concept of event as co-being [Bakhtin, 1920], to provide a unified strong account of various aspects of uncertainty catching distinction between variability and ignorance and opening an opportunity to define imprecise probability as a probability of imprecise event in the mathematical frameworks of Kolmogorov's probability theory [Kolmogorov, 1933].
For further development of this topic, see my later works:
https://www.academia.edu/34390291/,
https://www.academia.edu/34373279/,
https://www.academia.edu/34357251/

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very rest clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal systems and demonstrating the capacity to deal with other heterogeneous aatomical structures.

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 study of this paper suggests on dependency problem in fuzzy computational method by using the numerical solution of Fuzzy differential equations(FDEs) in Milne's predictor-corrector method. This method is adopted to solve the dependency problem in fuzzy computation. We solve some fuzzy initial value problems to illustrate the theory. KEYWORDS Fuzzy initial value problem, Dependency problem in fuzzy computation, Milnes predictor-corrector method.

Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated. The feasible region is formed as the intersection of two inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can easily generate a feasible test problem and employ our algorithm to it.

Breast cancer diagnosis is an important real world
medical problem. Fuzzy Rule Based System (FRBS) has been
successfully applied to many medical diagnosis problems. An
important issue in the design of FRBS is the formation of
fuzzy if-then rules and membership functions. This paper
presents a Improved Genetic Algorithm (IGA) approach to
obtain the optimal rule set and the membership function.
Advanced genetic operators are applied to improve the
performance of the GA in designing the fuzzy classifier. The
performance of the proposed approach is demonstrated using
Wisconsin breast cancer data available in the UCI machine
learning repository. From the simulation study, it is found that
the proposed IGFRBS produces a fuzzy diagnostic system,
which has minimum number of rules and whose classification accuracy is better than the results reported in the literature.

A structural health monitoring (SHM) methodology is developed for composite rotor blades. An aeroelastic analysis of composite rotor blades based on the finite element method in space and time and with implanted matrix cracking and debonding/delamination damage is used to obtain measurable system parameters such as blade response, loads and strains. A rotor blade with a two-cell airfoil section and ½0= AE 45=90 s family of laminates is used for numerical simulations. The model based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems (GFS) are developed for global online damage detection using displacement and force-based measurement deviations between damaged and undamaged conditions and for local online damage detection using strains. It is observed that the success rate of the GFS depends on number of measurements, type of measurements and training and testing noise level. The GFS work quite well with noisy data and is recommended for online SHM of composite helicopter rotor blades. r

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a framework to evolve rules automatically. The objective of the framework is to reduce the development effort and guide the development procedure in user friendly fashion. The architecture presents a novel approach which integrates fuzzy logic with system design as well as back end of the framework is used to evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed framework, a system to measure multiple intelligence is selected. The paper describes the detail methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve fuzzy rules for the selected system. The initial population of rules, evolved generations and output results are also described. The paper concludes with the scope and applications of the work to other domains.

To find a lawyer becomes a headache for business organization or general people. Sometimes people face many difficulties to find proper lawyer as their requirements, because of information gap. In addition, it is also difficult to find experienced lawyer. Usually, in order to hire a lawyer people need to meet him physically which is time-consuming and expensive. The delivery of legal services remains largely fragmented, outdated, and inefficient.The main purpose of this paper is to do empirical research about how the Internet is currently failing laypeople who are searching online for the legal help to their life problems and what a future agenda of user-centered standards and practices for better legal help on the Internet could be. It initially looks at the existing literature about how the Internet can best be used as legal asset and the situation quo of lawful help websites. Then it metering and looks at negative client reports and Investigates of legal help websites. Finally, it presents the primary study of how people search for resources to solve a legal problem, how they scorn and counts legal aid services online, and their recompose on which existing lawful help websites they consider to be the most usable, the most faithful, and the most valued. This data is necessary to propose new best exercises about how these tech-based services can best serve people, in terms of usability, characteristic of service, and safeguard of the users' interests. It also ensures the importance of the Internet as a lawful help service and highlights the need for more experiment and development on better online legal service help websites that fit general people needs and preferences.

Structural damage detection is an inverse problem of structural engineering having three main parts: finding the existence, location and extent of damage. In this study, a genetic fuzzy system is used to find the location and extent of damage. A finite element model of a cantilever beam is used to calculate the change in beam frequencies because of structural damage. Using these changes in frequencies, a fuzzy system is generated and the rule-base and membership functions optimized by genetic algorithm. The output faults of the fuzzy system are four levels of damage (undamaged, slight, moderate, and severe) at five locations along the beam (root, inboard, center, outboard and tip). The genetic fuzzy system developed for a noise level of 0.20 in the data gives a fault isolation success rate of 99.81% when the first eight natural frequencies are used. With noise level of 0.15 and 0.10, accuracy rates of 100% are obtained even when only the first four natural frequencies are used. The fuzzy system also shows excellent robustness with missing measurements and degrades gradually in the presence of faulty sensors/measurements. The genetic fuzzy system allows easy rule generation for different structures and when the number of inputs and outputs increase, thereby avoiding the Ôcurse of dimensionalityÕ that plagues fuzzy systems. Results with a non-uniform beam and a finer output set of damage at 10 locations in the beam also show excellent results. The genetic fuzzy system also gives very good results for BO-105 hingeless helicopter rotor blade for frequency as well as mode shape-based data. The genetic fuzzy logic system in this study is proposed as a method for automatic rule generation in fuzzy systems for structural damage detection.

In this paper three intelligent evolutionary optimization approaches to design PID controller for a Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied. The three applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm optimization. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying the four optimization methods mentioned above. After optimization of the objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is better on decreasing of overshoot. On the other hand PSO sounds to perform well on steady-state error only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.

The concept of controlling non-linear systems is one the significant fields in scientific researches for the purpose of which intelligent approaches can provide desirable applicability. Fuzzy systems are systems with ambiguous definition and fuzzy control is an especial type of non-linear control. Inverse pendulum system is one the most widely popular non-linear systems which is known for its specific characteristics such as being intrinsically non-linear and unsteady. Therefore, a controller is required for maintaining stability of the system Present study tries to compare the obtained results from designing fuzzy intelligent controllers in similar conditions and also identify the appropriate controller for holding the inverse pendulum in vertical position on the cart.

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.

A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method.

Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer
system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and
techniques to measure the process matric performance of the operating system but none has incorporated
the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach.
Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
operating system utilizing set of operating system criteria’s while fusing fuzzy logic to handle
impreciseness and genetic for process optimization.

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a framework to evolve rules automatically. The objective of the framework is to reduce the development effort and guide the development procedure in user friendly fashion. The architecture presents a novel
approach which integrates fuzzy logic with system design as well as back end of the framework is used to evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed framework, a system to measure multiple intelligence is selected. The paper describes the detail
methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve
fuzzy rules for the selected system. The initial population of rules, evolved generations and output results
are also described. The paper concludes with the scope and applications of the work to other domains.

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 main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

A fixed company of players observes a person selected from a fixed queue. After each observation, players are asked to bet the dollar secret from others, either on the fact that person is bald, or on what is not. A denite formula of the gain is suggested, such that every time after bets the gain of each player from a given company are completely determined by this formula. However, before bets player's gain is an uncertain value. Is it possible for a given company of players and a given queue of people before bets to build a correct mathematical model of uncertaint gain of each player within the framework of Kolmogorov's probability theory? If not, what else do you need to add to the foundations of probability theory so that before bets to be able to use this model for decision making? The paper answers these questions within the framework of the new theory of experience and of chance (the certainty theory) [1] that consists of two dual halves: the believability theory and the probability theory, and that is intended for the mathematical description of experienced-random experiments, the uncertainty in outcomes of which is generated by the observer. A xed company of players observes a person selected from a xed queue. After each observation, players are asked to bet the dollar secret from others, either on the fact that the person is bald, or on what is not. If, as a result of the bets made, the player's choice is in the minority, the player loses the bet. If, on the other hand, in the majority, the player is returned his bet plus an equal share of the loser players' bets. With a draw, the equality of the number of betting on the bald and the number of betting on the not bald, all players remain at their own: they are returned their bets 1. Every time after the bets, the gain of each player from this company is fully certain. But before the bets, the gain is an uncertain value. Is it possible for a given company of players and a given queue of persons before bets to build a correct mathematical model of uncertain gain of each player within the framework of Kolmogorov's probability theory?
c ⃝ 2016 O.Yu.Vorobyev Oleg Vorobyev (ed.), Proc. XV FAMEMS'2016, Krasnoyarsk: SFU

Lateral branching plays an important role in the elaboration of adult plants architecture. Herein, we adopted a modified AFLP approach combined with a degenerate primer amplification to identify and isolate in the underinvestigated ornamental species element of the MAX2 gene (More Axillary Branches), which acts inhibition signaling pathway. A specific gene fragment was cloned and sequenced from nineteen H. rosa-sinensis cultivars and twelve nucleotide polymorphisms among genotypes that were observed. The comparative analysis revealed a high conservation of DNA sequences among cultivars and wild species sexually compatible with H. rosa-sinensi fragments reveal four characteristic repeat regions showing high identity with other F box/Leucine Reach Repeat MAX2 homologous sequences. The cloned fragment is a likely candidate gene to be validated for association with phenotype to release a gene marker" for the compact habit trait.

A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.

Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering number to calculate the optimal clustering number, then the samples of input space is mapped to high-dimensional feature space using gaussian kernel and clustering in the feature space. The Matlab simulation results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results.

Social network is a group of individuals with diverse social interactions amongst them. The network large scale and distributed due to Quantitative analysis of networks is need of and in turn the society. Clustering helps us to group people with similar characteristics dense social networks. We have considered similarity measures for statistical When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social network nodesto simplify network analysis. similaritybetween unstructured data elements extracted from social network.

International Journal of Fuzzy Logic Systems (IJFLS) is an open access peer-reviewed journal that covers all topics in theoretical, experimental and applied fuzzy techniques and systems. It is aimed to bring together researchers and developers from both academia and industry to discuss the latest scientific and theoretical advances in this field, and to demonstrate the state-of-the-art systems. The journal solicits original technical papers that were not previously published and are not currently under review for publication elsewhere.

In this paper we have designed and applied to mixed (continuous and categorical) data in multiobjective partitional clustering problem. external validity indexes Adjusted Rand Index (ARI) and Minkowski Score (MS). performing multiobjective partitional clustering with mixed data, modeled with fuzzy logic, allowing for clusters, was obtained as a result of the resea CCS Concepts • Theory of computation➝ Unsupervised optimization and decision-making • Computing metho designed and implemented multiplepossible stochastic hill climbing alternatives, applied to mixed (continuous and categorical) data in a fuzzy context, as proposed solution partitional clustering problem. To validate the efficacy of this approach we selected the external validity indexes Adjusted Rand Index (ARI) and Minkowski Score (MS). An approach rforming multiobjective partitional clustering with mixed data, which also provides solutions allowing for a better description of the distribution of objects among the was obtained as a result of the research. Unsupervised learning and clustering • Applied computing➝ Multi Computing methodologies➝ Cluster analysis. Partitional clustering; multiobjective hill climbing ;fuzzy domain; mixed data. c hill climbing alternatives, solutions to a efficacy of this approach we selected the n approach capable of also provides solutions of objects among the

International Journal of Artificial Intelligence and Soft Computing (IJAISC) 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. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Soft Computing, Artificial Intelligence and Applications.

Fast and efficient data management is one of the demanding technologies of today's aspect. This paper proposes a system which makes the working procedures of present manual system of storing and retrieving huge citizen's information of Bangladesh automated and increases its effectiveness. The implemented search methodology is user friendly and efficient enough for high speed data retrieval ignoring spelling error in the input keywords used for searching a particular citizen. The main concern in this research is minimizing the total searching time for a given keyword. This can be done if we can pre-establish the idea of getting the data belonging to the searching keyword. The primary and secondary key-code generated by the Double Metaphone Algorithm for each word is used to establish that idea about the word. This algorithm is used for creating the map of the original database, through which the keyword is matched against the data.

Fast and efficient data management is one of the demanding technologies of today’s aspect. This paper proposes a system which makes the working procedures of present manual system of storing and retrieving huge citizen’s information of Bangladesh automated and increases its effectiveness. The implemented search methodology is user friendly and efficient enough for high speed data retrieval ignoring spelling error in the input keywords used for searching a particular citizen. The main concern in this research is minimizing the total searching time for a given keyword. This can be done if we can pre-establish the idea of getting the data belonging to the searching keyword. The primary and secondary key-code generated by the Double Metaphone Algorithm for each word is used to establish that idea about the word. This algorithm is used for creating the map of the original database, through which the keyword is matched against the data

In this paper, the notion α-anti fuzzy new-ideal of a PU-algebra are defined and discussed. The homomorphic images (pre images) of α-anti fuzzy new-ideal under homomorphism of a PU-algebras has been obtained. Some related result have been derived. Keywords PU-algebra, α-anti fuzzy new-ideal, the homomorphic images (pre images) of α-anti fuzzy new-ideal .

Social network is a group of individuals with diverse social interactions amongst them. The network large scale and distributed due to Quantitative analysis of networks is need of and in turn the society. Clustering helps us to group people with similar characteristics dense social networks. We have considered similarity measures for statistical When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social network nodesto simplify network analysis. similaritybetween unstructured data elements extracted from social network.

Rule weights often have been used to improve the classification accuracy without changing the position of antecedent fuzzy sets. Recently, fuzzy versions of confidence and support merits from the field of data mining have been widely used for rules weighting in fuzzy rule based classifiers. This paper proposes an evolutionary approach for learning rule weights and uses more flexible equations, which are evolved by genetic network programming. We perform some experiments using 15 well-known UCI data sets to examine efficiency of these novel equations; then, to analysis the results obtained in this experiments, a nonparametric statistical test is used. The results show that the performance of the fuzzy rule-based classification systems can be improved by using this proposed method for rule weight specification.

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.

To find a lawyer becomes a headache for business organization or general people. Sometimes people face many difficulties to find proper lawyer as their requirements, because of information gap. In addition, it is also difficult to find experienced lawyer. Usually, in order to hire a lawyer people need to meet him physically which is time-consuming and expensive. The delivery of legal services remains largely fragmented, outdated, and inefficient.The main purpose of this paper is to do empirical research about how the Internet is currently failing laypeople who are searching online for the legal help to their life problems and what a future agenda of user-centered standards and practices for better legal help on the Internet could be. It initially looks at the existing literature about how the Internet can best be used as legal asset and the situation quo of lawful help websites. Then it metering and looks at negative client reports and Investigates of legal help websites. Finally, it presents the primary study of how people search for resources to solve a legal problem, how they scorn and counts legal aid services online, and their recompose on which existing lawful help websites they consider to be the most usable, the most faithful, and the most valued. This data is necessary to propose new best exercises about how these tech-based services can best serve people, in terms of usability, characteristic of service, and safeguard of the users' interests. It also ensures the importance of the Internet as a lawful help service and highlights the need for more experiment and development on better online legal service help websites that fit general people needs and preferences.

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a framework to evolve rules automatically. The objective of the framework is to reduce the development effort and guide the development procedure in user friendly fashion. The architecture presents a novel approach which integrates fuzzy logic with system design as well as back end of the framework is used to evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed framework, a system to measure multiple intelligence is selected. The paper describes the detail methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve fuzzy rules for the selected system. The initial population of rules, evolved generations and output results are also described. The paper concludes with the scope and applications of ...

Fast and efficient data management is one of the demanding technologies of today’s aspect. This paper proposes a system which makes the working procedures of present manual system of storing and retrieving huge citizen’s information of Bangladesh automated and increases its effectiveness. The implemented search methodology is user friendly and efficient enough for high speed data retrieval ignoring spelling error in the input keywords used for searching a particular citizen. The main concern in this research is minimizing the total searching time for a given keyword. This can be done if we can pre-establish the idea of getting the data belonging to the searching keyword. The primary and secondary key-code generated by the Double Metaphone Algorithm for each word is used to establish that idea about the word. This algorithm is used for creating the map of the original database, through which the keyword is matched against the data.

Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly enhances user overall objective across the system. Related literatures have try in different methods and techniques to measure the process matric performance of the operating system but none has incorporated the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach. Extending the work of Michalis, this research focuses on measuring the process matrix performance of an operating system utilizing set of operating system criteria's while fusing fuzzy logic to handle impreciseness and genetic for process optimization.

Fast and efficient data management is one of the demanding technologies of today's aspect. This paper proposes a system which makes the working procedures of present manual system of storing and retrieving huge citizen's information of Bangladesh automated and increases its effectiveness. The implemented search methodology is user friendly and efficient enough for high speed data retrieval ignoring spelling error in the input keywords used for searching a particular citizen. The main concern in this research is minimizing the total searching time for a given keyword. This can be done if we can pre-establish the idea of getting the data belonging to the searching keyword. The primary and secondary key-code generated by the Double Metaphone Algorithm for each word is used to establish that idea about the word. This algorithm is used for creating the map of the original database, through which the keyword is matched against the data.

To find a lawyer becomes a headache for business organization or general people. Sometimes people face many difficulties to find proper lawyer as their requirements, because of information gap. In addition, it is also difficult to find experienced lawyer. Usually, in order to hire a lawyer people need to meet him physically which is time-consuming and expensive. The delivery of legal services remains largely fragmented, outdated, and inefficient.The main purpose of this paper is to do empirical research about how the Internet is currently failing laypeople who are searching online for the legal help to their life problems and what a future agenda of user-centered standards and practices for better legal help on the Internet could be. It initially looks at the existing literature about how the Internet can best be used as legal asset and the situation quo of lawful help websites. Then it metering and looks at negative client reports and Investigates of legal help websites. Finally, it presents the primary study of how people search for resources to solve a legal problem, how they scorn and counts legal aid services online, and their recompose on which existing lawful help websites they consider to be the most usable, the most faithful, and the most valued. This data is necessary to propose new best exercises about how these tech-based services can best serve people, in terms of usability, characteristic of service, and safeguard of the users' interests. It also ensures the importance of the Internet as a lawful help service and highlights the need for more experiment and development on better online legal service help websites that fit general people needs and preferences.

The quest to develop software of great quality with timely delivery and tested components gave birth to reuse. Component reusability entails the use (re-use) of existing artefacts to improve the quality and functionalities of software. Many approaches have been used by different researchers and applied to different metrics to assess software component reusability level. In addition to the common quality factors used by many authors, such as customisability, interface complexity, portability and understandability, this study introduces and justifies stability, in the context of volatility as a factor that determines the reusability of software components. Sixty-nine software components were collected from third party software vendors and data extracted from their features were used to compute the metric values of the five (5) selected quality factors. Genetic-Fuzzy System (GFS) was used to predict the level of the components' reusability. The performance of the GFS was compared with that of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach using their corresponding average RMSE (Root Mean Square Error), in order to ascertain the level of accuracy of the prediction. The results of the findings showed that, GFS with an RMSE of 0.0019 provides better reusability prediction accuracy compare to ANFIS with an RMSE of 0.1480.

Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated. The feasible region is formed as the intersection of two inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can easily generate a feasible test problem and employ our algorithm to it.

We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multiobjective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.

We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multiobjective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.

Fuzzy rule-based model is a powerful tool for imitating the human way of thinking and solving uncertaintyrelated problems as it allows for understandable and interpretable rule bases. The objective of this paper is
to study the applicability of fuzzy rule-based modelling to quantify soil classification for engineering
purposes by qualitatively considering soil index properties. The classification system of the Highway
Research Board is considered to illustrate a fuzzy rule-based model. The soil's index properties are
fuzzified using triangular functions, and the fuzzy membership values are calculated. Fuzzy arithmetical
operators are then applied to the membership values obtained for classification. Fuzzy decision tree
classification algorithm is used to derive fuzzy if-then rules to quantify qualitative soil classification. The
proposed system is implemented in MATLAB. The results obtained are checked and the implementation of
the proposed model is measured against the outcomes of the laboratory tests.