Global competitiveness has recently become a big challenge for many companies around the world, which are forced to seek lower costs and higher quality for what they produce. The prosperity of manufacturing firms depends on selecting and producing products which provide customer satisfaction to meet multiple objectives.If a company is able to produce customer-oriented products at a low price and in minimum time, it can be successful.So, customer need analysis should be paid attention to in product development and the design phase.Also, the technical capabilities of a manufacturing firm and the restrictions of a company should be considered.In this way, they have one big challenge: How can they respond effectively to different and easily changing customer demands? By focusing on customer opinion, Quality Function Deployment (QFD) has been developed.Quality Function Deployment (QFD) is a robust, efficient and powerful tool in the design, development and planning of products. QFD has been used in many industries and companies over the last few decades. The main function of QFD is conversion of the voice of the customer (VOC) to Technical Characteristics (TCs).However, it is not always easy to prioritize and assess TCs during the total mass of information from the different customer attitudes. This paper provides a methodology for the development of an intelligent Quality Function Deployment (IQFD) and points for developing an intelligent system based on a fuzzy inference system, in order to capture information through the House of Quality (HOQ) matrix. The paper describes the need for development of intelligent QFD to make it easier for engineers and managers to choose between TCs and improve the quality of products and systems. This paper is composed of a background of QFD, a review of related research work, and representation of an intelligent system for its analysis. Then, it applies the proposed methodology to a case study of House of Quality for the design of a new undergraduate curriculum in the mechanical engineering department of the university of Wisconsin-Madison.

In this paper, we improve some previous definitions of fuzzy-type Turing machines to obtain degrees of accepting and rejecting in a computational manner. We apply a BFS-based search method and some level’ s upper bounds to propose a computational process in calculating degrees of accepting and rejecting. Next, we introduce the class of Extended fuzzy Turing machines equipped with indeterminacy states. These states are used to characterize the loops of classical Turing machines in a mathematical sense. In the sequel, as well as the notions of acceptable and decidable languages, we define the new notion of indeterminable language. An indeterminable language corresponds to non-halting runs of a machine. Afterwards, we show that there is not any universal extended machine; which concludes that these machines cannot solve the halting problem. Also, we show that our extended machines and classical Turing machines have the same computational power. Then, we define the new notion of semi-universality and prove that there exists a semi-universal extended machine. This machine can indeterminate the complement of classical halting problem. Moreover, to each r. e or co-r. e language, we correspond a language that is related to some extended fuzzy Turing machines.

A finite switchboard state machine is a specialized finite state machine. It is built by binding the concepts of switching state machines and commutative state machines. The main purpose of this paper is to give a specific algorithm for fuzzy finite switchboard state machine and also, investigates the concepts of switching relation, covering, restricted cascade products and wreath products of fuzzy finite switchboard state machines. More precisely, we study that the direct products/Cartesian compositions of two such fuzzy finite switchboard state machines is again a fuzzy finite switchboard state machine. In addition, we introduce the perfect switchboard machine and establish its Cartesian composition. The relations among the products also been examined. Finally, we introduce asynchronous fuzzy finite switchboard state machine and study the switching homomorphic image of asynchronous fuzzy finite switchboard state machine. We illustrate the definition of a restricted product of fuzzy finite switchboard state machine with the single pattern example.

Structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality.Biggest class of the repetitive subsequences is “Transposable Elements” which has its own sub-classes upon contexts’ structures. Many researchers have been performed to criticality determine the structure and function of repetitive subsequences. The sequencing noises and the sequences’ substitutions probability are obstacles of these researches. Some statistical and approximation algorithms have introduced to tackle these obstacles. By introducing conspicuous statistical machine learning methods upon Support Vector machines, machine learning approaches act as potent methods to solve the pattern-finding problem. Support vector machines methods are time efficient approaches, which based on their parameters can be precise and accurate. In this Review, mathematical definition of structural repetitive subsequences are introduced, thereafter proposed algorithm to tackle simple pattern finding problem, which can be applicable on structural patterns are reviewed. Theoretical aspects of Support Vector machines on computational biology platform are considered. Finally, novel evolutionary fuzzy SVM will be introduced, which is applicable on wide range of bioinformatics problems especially the problem of structural repetitive subsequences.

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structure of them, initial weights, number of hidden layer neurons, and learning rate. Quantum computing is a new method of information processing based on quantum mechanics, the concepts of which are also used today in applications of artificial intelligence. In the proposed method, the neural network of the extreme learning machine is improved using the concept of the quantum fuzzy clustering c-Means. This clustering helps to find the optimal weight of the input layer connections to the hidden layer of the neural network. It also allows network architecture to be constructively constructed in the hidden layer and improves learning. The performance of the proposed method in terms of accuracy, correct positive rate and correct negative rate shows the superiority of the proposed method in detecting outlier data compared to other methods.

Preventive maintenance (PM) of machines has the critical role in a factory or enterprise. It decreases number of failures, increases reliability, as well as minimizes costs of production systems.  The managers’ duty of maintenance section is to prioritize machines and then, implement PM programs for them. Since machines have the different measures with respect to the maintenance costs, reliability, mean time between failures (MTBF), availability of spare parts, etc., the machines evaluation problem can be considered as a multiple criteria decision-making (MCDM) problem. Accordingly, the MCDM techniques can be applied to solve them. The aim of this paper is to extend the ELECTRE III (eLimination et choix traduisant la realite´– elimination and choice translation reality) method to interval type-2 fuzzy sets (IT2FSs) using curved (such as Gaussian) membership functions (MFs). The extended ELECTRE III methodology is then utilized to a maintenance group MCDM (GMCDM) matrix including the quantitative and qualitative criteria. In the proposed approach, the criteria weights, the assessment of alternatives with respect to criteria, and the thresholds are stated with Gaussian interval type-2 fuzzy sets (GIT2FSs). In order to show the effectiveness and applicability of the proposed approach, a case study and an illustrative example are exhibited using real decision-making problems. Due to the high correlation coefficients between our method and the others, as well as the results obtained by the proposed method, it can be taken into account as a valid and reliable approach to prioritize machines for PM.

Recently, tuning the weights of the rules in fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective function, but also is independent of any order in presenting data patterns or fuzzy rules. It has a global optimum solution and needs only one regularization parameter C to be adjusted. In addition, a rule reduction method is proposed to eliminating low weighted rules and having a compact rule-base. This method is compared with some greedy, reinforcement and local search rule weighting methods on 13 standard datasets. The experimental results show that, the proposed method significantly outperforms the other ones especially from the viewpoint of generalization.

The problem of automatic handwritten context recognition has received considerable attention of many researchers. In this paper, a fusion system is proposed to enhance the recognition accuracy of Farsi handwritten digits. The proposed approach consists of a preparation process and two main phases. In the preparation process, some pre-processing operations are performed on the image. Then some features are extracted, among which a multi-objective particle swarm optimization selects more effective ones. For every image, these optimal features are given as the input data to the classifiers. In the first main phase, training datasets are used to construct three different SVMs. In order to achieve better results, the adaptive best-mass gravitational search algorithm is utilized to adjust the SVMs parameters. In the second main phase, an interval type– II fuzzy inference system receives the SVMs outputs and by combining them, it presents a more accurate estimation of the digit in the image. The results of applying the proposed approach to the problem of scanned Farsi handwritten digits in the standard HODA database demonstrated that this algorithm attains high accuracy, precision and recall performance indices, comparing to other existing methods.

Support Vector machine is one of the most popular and efficient algorithms in machine learning. There are several versions of this algorithm, the latest of which is the fuzzy least squares twin support vector machines. On the other hand, in many machine learning applications input data is continuously generated, which has made many traditional algorithms inefficient to deal with them. In this paper, for the first time, an incremental version of the fuzzy least squares twin support vector algorithm is presented. The proposed algorithmis represented in both online and quasi-online modes. To evaluate the accuracy and precision of the proposed algorithmfirst we run our algorithm on 6 datasets of the UCI repository. Results showthe proposed algorithm is more efficient than other algorithms (even non-incremental versions). In the second phase in the experiments, we consider an application of Internet of Things, and in particular in data related to daily activities which inherently are incremental. According to experimental results, the proposed algorithm has the best performance compared to other incremental algorithms.