Showing papers in "Journal of intelligent systems in 2011"

On distance and correlation measures of hesitant fuzzy information

Abstract: A hesitant fuzzy set, allowing the membership of an element to be a set of several possible values, is very useful to express people's hesitancy in daily life. In this paper, we define the distance and correlation measures for hesitant fuzzy information and then discuss their properties in detail. These measures are all defined under the assumption that the values in all hesitant fuzzy elements (the fundamental units of hesitant fuzzy sets) are arranged in an increasing order and two hesitant fuzzy elements have the same length when we compare them. We can find that the results, by using the developed distance measures, are the smallest ones among those when the values in two hesitant fuzzy elements are arranged in any permutations. In addition, the derived correlation coefficients are based on different linear relationships and may have different results. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Intuitionistic fuzzy geometric aggregation operators based on einstein operations

Abstract: Intuitionistic fuzzy information aggregation plays an important part in Atanassov's intuitionistic fuzzy set theory, which has emerged to be a new research direction receiving more and more attention in recent years In this paper, we first introduce some operations on intuitionistic fuzzy sets, such as Einstein sum, Einstein product, Einstein exponentiation, etc, and further develop some new geometric aggregation operators, such as the intuitionistic fuzzy Einstein weighted geometric operator and the intuitionistic fuzzy Einstein ordered weighted geometric operator, which extend the weighted geometric (WG) operator and the ordered weighted geometric (OWG) operator to accommodate the environment in which the given arguments are intuitionistic fuzzy values We also establish some desirable properties of these operators, such as commutativity, idempotency and monotonicity, and give some numerical examples to illustrate the developed aggregation operators In addition, we compare the proposed operators with the existing intuitionistic fuzzy geometric operators and get the corresponding relations Finally, we apply the intuitionistic fuzzy Einstein weighted geometric operator to deal with multiple attribute decision making under intuitionistic fuzzy environments © 2011 Wiley Periodicals, Inc © 2011 Wiley Periodicals, Inc

A new interpretation of complex membership grade

Abstract: Complex fuzzy sets utilize a complex degree of membership, represented in polar coordinates, which is a combination of a degree of membership in a fuzzy set along with a crisp phase value that denotes position within the set. The compound value carries more information than a traditional fuzzy set and enables efficient reasoning. In this paper, we present a new and generalized interpretation of a complex grade of membership, where a complex membership grade defines a complex fuzzy class. The new definition provides rich semantics that is not readily available through traditional fuzzy sets or complex fuzzy sets and is not limited to a compound of crisp cyclical data with fuzzy data. Furthermore, the two components of the complex fuzzy class carry fuzzy information. A complex class is represented either in Cartesian or in polar coordinates where both axes induce fuzzy interpretation. Another novelty of the scheme is that it enables representing an infinite set of fuzzy sets. The paper provides the new definition of complex fuzzy classes along with axiomatic definition of basic operations on complex fuzzy classes. In addition, coordinate transformation as well as an extension from two-dimensional fuzzy classes to n-dimensional fuzzy classes are presented. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Induced intuitionistic fuzzy Choquet integral operator for multicriteria decision making

Abstract: Yager (Fuzzy Sets, Syst 2003;137:59–69) extended the idea of order-induced aggregation to the Choquet aggregation and defined induced Choquet ordered averaging operator. In this paper, an induced intuitionistic fuzzy Choquet (IFC) integral operator is proposed for the multiple criteria decision making. Some of its properties are investigated. Furthermore, an induced generalized IFC integral operator is introduced. It is worth mentioning that most of the existing intuitionistic fuzzy aggregation operators are special cases of this induced aggregation operator. A decision procedure based on the proposed induced aggregation operator is developed for solving the multicriteria decision-making problem in which all the decision information is represented by intuitionistic fuzzy values. An illustrative example is given for demonstrating the applicability of the proposed decision procedure. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Algorithms for estimating missing elements of incomplete intuitionistic preference relations

Abstract: Preference relations as simple and efficient information description tools have been widely used in practical decision-making problems Intuitionistic preference relation, which is often met in real problems, is usually utilized to provide the experts' vague or fuzzy opinions over objects under uncertain circumstances Owing to the limitations of the experts' professional knowledge and experience, the provided preferences in an intuitionistic preference relation are usually incomplete Consequently, how to estimate the missing information in an expert's incomplete intuitionistic preference relation becomes a necessary step in a decision-making process In this paper, we define the concept of multiplicative consistent intuitionistic preference relation and develop two estimation algorithms The first algorithm is used to estimate the missing elements using only the known preference values in an acceptable incomplete intuitionistic fuzzy preference relation with the least judgments The second one is given for the estimation of missing elements of the acceptable incomplete intuitionistic fuzzy preference relations with more known judgments The advantages of the developed algorithms over the existing one are detailedly analyzed, and some examples are provided to illustrate the solution processes of the algorithms and to verify their practicality and superiority © 2011 Wiley Periodicals, Inc © 2011 Wiley Periodicals, Inc

A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services

Abstract: The information overload on the World Wide Web results in the underuse of some existing e-government services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking “one-to-one'' e-services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust-enhanced recommendation approach to provide personalized government-to-business (G2B) e-services, and in particular, business partner recommendation e-services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user-based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust-enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user-based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold-start users. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Extending description logics with uncertainty reasoning in possibilistic logic

Abstract: Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well-known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning-free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results. © 2011 Wiley Periodicals, Inc. (This paper is significantly extended from a conference paper.)

An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems

Abstract: Recently, surveillance, security, patrol, search, and rescue applications increasingly require algorithms and methods that can work automatically in real time. This paper reports a new real-time approach based on three novel techniques for automatic detection, object identification, and tracking in video streams, respectively. The novelty detection and object identification are based on the newly proposed recursive density estimation (RDE) method. RDE is using a Cauchy-type of kernel, which is calculated recursively as opposed to the widely used (in particular in the kernel density estimation (KDE) approach) Gaussian one. The key difference is that the proposed approach works on a per frame basis and does not require a window (usually of size of several dozen) of frames to be stored in the memory and processed. It should be noted that the new RDE approach is free from user- or problem-specific thresholds by differ from the other state-of-the-art approaches. Finally, an evolving Takagi–Sugeno (eTS)-type fuzzy system is proposed for tracking. The proposed approach has been compared with KDE and Kalman filter (KF) and has proven to be significantly (in an order of magnitude) faster and computationally more efficient than RDE and more precise than KF. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Multicriteria methods and logic aggregation in suitability maps

Abstract: In this paper, we identify and describe fundamental logic properties of multicriteria methods for land-use suitability analysis and the design of suitability maps. The existing multicriteria methods can be evaluated from the standpoint of their ability to support the necessary logic properties that affect the expressive power of evaluation methods. The paper investigates and compares simple additive scoring, multiattribute value technique, multiattribute utility technique, analytic hierarchy process, ordered weighted average, outranking methods, and logic scoring of preference (LSP). We introduce canonical forms of logic aggregation in suitability maps and show how to use canonical aggregation structures to design LSP suitability maps that evaluate distributions of points of interests (POIs) in urban areas. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

A multi-agent system for analyzing the effect of information on prediction markets

Abstract: Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information-related parameters on the behavior of the traders in the market. We have developed a multi-agent based system that incorporates different information-related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent-based prediction market for analyzing the effect of information-related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent-based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information-related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Linguistic query answering on data cubes with time dimension

Abstract: In this paper, we propose a methodology for providing linguistic answers to queries involving the comparison of time series obtained from data cubes with time dimension. Time series related to events which are interesting for the user are obtained by querying data cubes using OnLine Analytical Processing (OLAP) operations on the time dimension. The comparison of these query results can be summarized so that an appropriate short linguistic description of the series is provided to the user. Our approach is based on linguistically quantified statements and pointwise definitions of the degree and sign of local change. Our linguistic summaries are well suited to be included in an interface layer of a data warehouse system, improving the quality of human-machine interaction and the understandability of the results. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Bipolar database querying using bipolar satisfaction degrees

Abstract: When expressing their information needs in a (database) query, users sometimes prefer to state what has to be rejected rather than what has to be accepted. In general, what has to be rejected is not necessarily the complement of what has to be accepted. This phenomenon is commonly known as the heterogeneous bipolar nature of expressing information needs. Satisfaction degrees in regular fuzzy querying approaches are based on the “symmetric'' assumption that the extent to which a database record, respectively, satisfies and does not satisfy a given query are complements of each other and are therefore less suited to adequately handle heterogeneous bipolarity in query specifications and query processing. In this paper, we present a bipolar query satisfaction modeling framework which is based on pairs that consist of an independent degree of satisfaction and degree of dissatisfaction. The use and advantages of the framework are illustrated in the context of fuzzy query evaluation in regular relational databases. More specifically, the evaluation of heterogeneous bipolar queries that contain both positive, negative, and bipolar criteria is studied. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Word Extraction and Character Segmentation from Text Lines of Unconstrained Handwritten Bangla Document Images

Abstract: In this paper, a novel approach for word extraction and character segmentation from the handwritten Bangla document images is reported. At first, a modified Run Length Smoothing Algorithm (RLSA), called Spiral Run Length Smearing Algorithm (SRLSA), is applied for the extraction of words from the text lines of unconstrained handwritten Bangla document images. This technique has helped to overcome some of the drawbacks of standard horizontal and vertical RLSA techniques. SRLSA technique has been applied on the Bangla handwritten document image database CMATERdb1.1.1 and the success rate of the word extraction is found to be 86.01%. In the second part of the work, we have presented a useful solution to the problem on how best word images of handwritten Bangla script can be segmented into constituent characters. Moreover, the technique can segment the words having discontinuity in Matra, a prominent feature of Bangla script. It also optimizes the trade-off between under/over segmentation as Matra region and segmenta- tion points are estimated more precisely. As a result, better word segmentation accuracy is achieved with minimal data loss. Here, a success rate of 92.48% is observed on a dataset of 750 handwritten Bangla words which is 3.35% higher than that of our earlier techniques.

Probabilistic rule induction with the LERS data mining system

Abstract: Based on classical rough set approximations, the LERS (Learning from Examples based on Rough Sets) data mining system induces two types of rules, namely, certain rules from lower approximations and possible rules from upper approximations. By relaxing the stringent requirement of the classical rough sets, one can obtain probabilistic approximations. The LERS can be easily applied to induce probabilistic positive and boundary rules from probabilistic positive and boundary regions. This paper discusses several fundamental issues related to probabilistic rule induction with LERS, including rule induction algorithm, quantitative measures associated with rules, and the rule conflict resolution method. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

From spreading of behavior to dyadic interaction—A robot learns what to imitate

Abstract: Imitation learning is a promising way to learn new behavior in robotic multiagent systems and in human-robot interaction. However, imitating agents should be able to decide autonomously which behavior, observed in others, is interesting to copy. This paper shows a method for extraction of meaningful chunks of information from a continuous sequence of observed actions by using a simple recurrent network (Elman Net). Results show that, independently of the high level of task-specific noise, Elman nets can be used for learning through prediction a reoccurring action patterns, observed in another robotic agent. We conclude that this primarily robot to robot interaction study can be generalized to human-robot interaction and show how we use these results for recognizing emotional behaviors in human-robot interaction scenarios. The limitations of the proposed approach and the future directions are discussed. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data

Abstract: This work extends the authors' contribution to the International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008) held at the University of Akron in October 2008. It is dedicated to the topic of granular computing, formalized within the theory of rough mereology, as proposed by Polkowski; as an application of the idea of a granular reflection of data and of classifiers induced from it (Polkowski, 2005), we give an account of recent results in this area. A scheme for classifier construction based on factoring a classifier through a granular reflection of data; voting by granules of training objects; voting by granules of decision rules induced from the training set; voting by granules induced from a granular reflection of data, is presented here. In voting cases, voting is based on weights computed by means of rough inclusions induced from residual implications of continuous t-norms. The results show a high effectiveness of this approach as witnessed by the reported tests with some well--known data sets from University of California, at Irvine (UCI) repository whose results are compared against the standard rough set exhaustive classifier whose accuracy is indicated under the radius of “nil.” © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

An adaptive neuro-fuzzy system for stock portfolio analysis

Abstract: We propose an adaptive neuro-fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection-clustering algorithm. The neuro-fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Text summarization contribution to semantic question answering: New approaches for finding answers on the web

Abstract: As the Internet grows, it becomes essential to find efficient tools to deal with all the available information. Question answering (QA) and text summarization (TS) research fields focus on presenting the information requested by users in a more concise way. In this paper, the appropriateness and benefits of using summaries in semantic QA are analyzed. For this purpose, a combined approach where a TS component is integrated into a Web-based semantic QA system is developed. The main goal of this paper is to determine to what extent TS can help semantic QA approaches, when using summaries instead of search engine snippets as the corpus for answering questions. In particular, three issues are analyzed: (i) the appropriateness of query-focused (QF) summarization rather than generic summarization for the QA task, (ii) the suitable length comparing short and long summaries, and (iii) the benefits of using TS instead of snippets for finding the answers, tested within two semantic QA approaches (named entities and semantic roles). The results obtained show that QF summarization is better than generic (58% improvement), short summaries are better than long (6.3% improvement), and the use of TS within semantic QA improves the performance for both named-entity-based (10%) and, especially, semantic-role-based QA (47.5%). © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Cataloguing/analogizing: A nonmonotonic view

Abstract: Reasoning deductively under incomplete information is nonmonotonic in nature since the arrival of additional information may invalidate or reverse previously obtained conclusions. It amounts to apply generic default rules in an appropriate way to a particular (partially described) situation. This type of nonmonotonic reasoning can only provide plausible conclusions. Analogical reasoning is another form of commonly used reasoning that yields brittle conclusions. It is nondeductive in nature and proceeds by putting particular situations in parallel. Analogical reasoning also exhibits nonmonotonic features, as investigated in this paper when particular situations may be incompletely stated. The paper reconsiders the pattern of plausible reasoning proposed by Polya, “a and b are analogous, a is true, then b true is more credible,'' from a nonmonotonic reasoning point of view. A representation of the statement “a and b are analogous” in terms of nonmonotonic consequences relations is presented. This representation is then related to a logical definition of analogical proportions, i.e. statements of the form “a is to b as c is to d” that has been recently proposed and extended to other types of proportions. Remarkably enough, semantic equivalence between conditional objects of the form “b given a,” which have been shown as being at the root of nonmonotonic reasoning, constitutes another type of noticeable proportions. By offering a parallel between two important forms of commonsense reasoning, this paper enriches the comparison between nonmonotonic reasoning and analogical reasoning that is not often made. © 2011 Wiley Periodicals, Inc. (This paper is a revised and expanded version of an unpublished article with the same title presented at the International Conference celebrating 30 years of Nonmonotonic Reasoning ([email protected]), held in Lexington, KY. Oct. 22–25, 2010. Available at http://sites.google.com/site/nonmonat30/conference-materials; accessed on October 6, 2011.)

Evolution computation based learning algorithms of polygonal fuzzy neural networks

Abstract: We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Density-induced ordered weighted averaging operators

Abstract: We provide a special type of induced ordered weighted averaging (OWA) operator called density-induced OWA (DIOWA) operator, which takes the density around the arguments as the inducing variables to reorder the arguments. The density around the argument, which can measure the degree of similarity between the argument and its nearest neighbors, is associated with both the number of its nearest neighbors and its weighted average distance to these neighbors. To determine the DIOWA weights, we redefine the orness measure, and propose a new maximum orness model under a dispersion constraint. The DIOWA weights generated by the traditional maximum orness model depend upon the order of the arguments and the dispersion degree. Differently, the DIOWA weights generated by the new maximum orness model also depend upon the specific values of the density around the arguments. Finally, we illustrate how the DIOWA operator is used in the decision making, and prove the effectiveness of the DIOWA operator through comparing the DIOWA operator with other operators, i.e., the centered OWA operator, the Olympic OWA operator, the majority additive-OWA (MA-OWA) operator, and the kNN-DOWA operator. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Assessment of the Effects of Sensory Perturbations using Fourier–Bessel Expansion Method for Postural Stability Analysis

Abstract: Abstract Center of pressure (COP) measurements are often used to identify balance problems. A new method for COP signal analysis using mean frequency is proposed in this paper. The computation of mean frequency is based on the Fourier–Bessel (FB) expansion that is suitable for use in non-stationary COP signals. In addition, FB expansion provides better frequency resolution and low frequency detectability. Seventeen subjects were tested under eyes open and eyes closed conditions, with different vibration frequencies applied for the eyes closed condition in order to further perturb sensory information. More accurate estimates of mean frequency were obtained for 200-point segments, with significant increase in mean frequency observed over time as subjects recovered from the initial perturbation of stepping on to the force plate. Subjects were significantly more stable in the mediolateral direction compared to anteroposterior one. Mean frequency as calculated by FB expansion was able to distinguish between eyes open and eyes closed conditions (p < 0.05), but no effect of vibration could be detected.

On Diverse Approaches to Bipolar Division Operators

Abstract: Introducing preferences inside user queries has gained more and more acceptance during the past decade. Besides, it turns out that the concept of bipolarity is of interest for expressing queries in the sense that some requirements are mandatory and play the role of constraints, whereas other are solely desirable. In this paper, we investigate how bipolarity may impact the division operator in the context of relational databases. Various forms of bipolar divisions can indeed be devised, each of them conveying a specific semantics. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

A noise-resilient collaborative learning approach to content-based image retrieval

Abstract: We propose to combine short-term block-based fuzzy support vector machine (FSVM) learning and long-term dynamic semantic clustering (DSC) learning to bridge the semantic gap in content-based image retrieval. The short-term learning addresses the small sample problem by incorporating additional image blocks to enlarge the training set. Specifically, it applies the nearest neighbor mechanism to choose additional similar blocks. A fuzzy metric is computed to measure the fidelity of the actual class information of the additional blocks. The FSVM is finally applied on the enlarged training set to learn a more accurate decision boundary for classifying images. The long-term learning addresses the large storage problem by building dynamic semantic clusters to remember the semantics learned during all query sessions. Specifically, it applies a cluster-image weighting algorithm to find the images most semantically related to the query. It then applies a DSC technique to adaptively learn and update the semantic categories. Our extensive experimental results demonstrate that the proposed short-term, long-term, and collaborative learning methods outperform their peer methods when the erroneous feedback resulting from the inherent subjectivity of judging relevance, user laziness, or maliciousness is involved. The collaborative learning system achieves better retrieval precision and requires significantly less storage space than its peers. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Using data mining techniques for optimizing traffic signal plans at an urban intersection

Abstract: A key problem in traffic engineering is the optimization of the flow of vehicles through urban intersections by improving the timing policy of traffic signals. Current methods of signal control policy are based on the junction topography and prespecified static traffic volumes. However, the actual daily traffic volumes can be affected by many time-dependent factors making a static policy hardly optimal. In this paper, we induce nonstationary predictive models of traffic flow by applying novel methods of time-series data mining to the traffic sensors data collected from a signalized intersection in Jerusalem over a period of 3 years. Our methodology for modeling dynamic traffic volumes combines clustering and segmentation algorithms. The results of a case study based on real-world traffic data demonstrate that a dynamic signal policy using the data mining approach can produce a decrease of about 33.7% in the total waiting time of drivers during 1 year, in comparison to the existing static traffic policy. The resulting savings for this junction only would be about 13,800 driving hours, which are worth of about $52,000 per annum in terms of Israeli economy. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

A flexible and scalable audio information retrieval system for mixed-type audio signals

Abstract: The content-based classification and retrieval of real-world audio clips is one of the challenging tasks in multimedia information retrieval. Although the problem has been well studied in the last two decades, most of the current retrieval systems cannot provide flexible querying of audio clips due to the mixed-type form (e.g., speech over music and speech over environmental sound) of audio information in real world. We present here a complete, scalable, and extensible content-based classification and retrieval system for mixed-type audio clips. The system gives users an opportunity for flexible querying of audio data semantically by providing four alternative ways, namely, querying by mixed-type audio classes, querying by domain-based fuzzy classes, querying by temporal information and temporal relationships, and querying by example (QBE). In order to reduce the retrieval time, a hash-based indexing technique is introduced. Two kinds of experiments were conducted on the audio tracks of the TRECVID news broadcasts to evaluate the performance of the proposed system. The results obtained from our experiments demonstrate that the Audio Spectrum Flatness feature in MPEG-7 standard performs better in music audio samples compared to other kinds of audio samples and the system is robust under different conditions. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Efficient KNN search by linear projection of image clusters

Abstract: K-nearest neighbors (KNN) search in a high-dimensional vector space is an important paradigm for a variety of applications. Despite the continuous efforts in the past years, algorithms to find the exact KNN answer set at high dimensions are outperformed by a linear scan method. In this paper, we propose a technique to find the exact KNN image objects to a given query object. First, the proposed technique clusters the images using a self-organizing map algorithm and then it projects the found clusters into points in a linear space based on the distances between each cluster and a selected reference point. These projected points are then organized in a simple, compact, and yet fast index structure called array-index. Unlike most indexes that support KNN search, the array-index requires a storage space that is linear in the number of projected points. The experiments show that the proposed technique is more efficient and robust to dimensionality as compared to other well-known techniques because of its simplicity and compactness. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

A hybrid algorithm for finding minimal unsatisfiable subsets in over-constrained CSPs

Abstract: Minimal Unsatisfiable Subsets (MUSes) are the subsets of constraints of an overconstrained constraint satisfaction problem (CSP) that cannot be satisfied simultaneously and therefore are responsible for the conflict in the CSP. In this paper, we present a hybrid algorithm for finding MUSes in overconstrained CSPs. The hybrid algorithm combines the direct and the indirect approaches to finding MUSes in overconstrained CSPs. Experimentation with random CSPs reveals that the hybrid approach is not only quite efficient but when operating under a time bound it finds a more representative set of MUSes. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

CBDL: Context-based distance learning for categorical attributes

Abstract: Distance learning is an important notion and has played a critical role in success of various machine learning algorithms. Any learning algorithm that requires dissimilarity/similarity measures has to assume some forms of distance functions, either explicitly or implicitly. Hence, in recent years a considerable amount of research has been devoted to distance learning. Despite great achievements in this field, a number of important issues need to be further explored for real world datasets mainly containing categorical attributes. Based on these considerations, the current research presents a Context-Based Distance Learning approach (CBDL) to advance the state of the art existing researches on distance metric learning for categorical datasets. CBDL is designed and developed based on the idea that distance between two values of a given categorical attribute can be estimated by using information inherently exists within subset of attributes called context. CBDL composes of two main components: context extraction component and distance learning component. Context extraction component is responsible for extracting the relevant subset of feature set for a given attribute, while distance learning component tries to learn distance between each pair of values based on the extracted context. To have a comprehensive analysis, we conduct wide range of experiments in both supervised and unsupervised environments in the presence of noise. Our experimental results reveal that CBDL is the method of choice distance learning approach by offering a comparable or better performance compared to the state of the art existing distance learning schemes according to studied evaluation measures. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Fuzzy pricing of American options on stocks with known dividends and its algorithm

Abstract: The path-dependent property of American options leads to the complexity of its pricing. Based on the analysis of American options' characteristics and the influence of the stock dividend, the American call option fuzzy pricing method is discussed in this paper. Under the assumption that the price of stock, discount rate, the volatility, and interest rate are all fuzzy numbers, the fuzzy pricing formula of American option is proposed by using the Black–Scholes pricing model. Then the interpolation search algorithm is designed to solve the proposed pricing model. Finally, the validity and accuracy of this model and its algorithm have to be tested with some numerical examples. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.