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Abstract: Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people. Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system. [Copyright &y& Elsevier]

Abstract: In sentiment analysis, a finer-grained opinion mining method not only focuses on the view of the product itself, but also focuses on product features, which can be a component or attribute of the product. Previous related research mainly relied on explicit features but ignored implicit features. However, the implicit features, which are implied by some words or phrases, are so significant that they can express the users’ opinion and help us to better understand the users’ comments. It is a big challenge to detect these implicit features in Chinese product reviews, due to the complexity of Chinese. This paper is mainly centered on implicit features identification in Chinese product reviews. A novel hybrid association rule mining method is proposed for this task. The core idea of this approach is mining as many association rules as possible via several complementary algorithms. Firstly, we extract candidate feature indicators based word segmentation, part-of-speech (POS) tagging and feature clustering, then compute the co-occurrence degree between the candidate feature indicators and the feature words using five collocation extraction algorithms. Each indicator and the corresponding feature word constitute a rule (feature indicator → feature word). The best rules in five different rule sets are chosen as the basic rules. Next, three methods are proposed to mine some possible reasonable rules from the lower co-occurrence feature indicators and non indicator words. Finally, the latest rules are used to identify implicit features and the results are compared with the previous. Experiment results demonstrate that our proposed approach is competent at the task, especially via using several expanding methods. The recall is effectively improved, suggesting that the shortcomings of the basic rules have been overcome to certain extent. Besides those high co-occurrence degree indicators, the final rules also contain uncommon rules. [Copyright &y& Elsevier]

Abstract: This paper presents a novel algorithm to deal with the network design problem, which optimizes the network levels considering their interdependency. The idea is to design a low cost and optimized network providing the number and the geographic location of devices as well as the links among them for each network level, while taking into account the existing dependency among them. In addition, a new database composed of real and georeferenced data is created and make available for the research community. This database contains three datasets that represent distinct projects related to geographic regions of the city of Curitiba (Brazil). The experimental results show that the proposed algorithm provides a significant cost reduction in the network design. The savings of this proposal go from 1% to 40% (depending on the network size, number of levels and demand nodes), making it attractive for companies that spend a considerable amount of resources in network projects and deployment. [Copyright &y& Elsevier]

Abstract: Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, different metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Differential Evolution. In this paper it is proposed a Multi-View Differential Evolution algorithm (MVDE) in which several mutation strategies are applied to the current population to generate different views at each iteration. The views are then merged according to the winner-takes-all paradigm, resulting in automatic exploration/exploitation balance. MVDE was tested to solve a set of well-known constrained engineering design problems and the obtained results were compared to those from many state-of-the-art metaheuristics. Results show that MVDE was very competitive in the considered problems, largely outperforming several of the compared algorithms. [Copyright &y& Elsevier]

Abstract: This paper is in the area of membrane proteins. Membrane proteins make up about 75% of possible targets for novel drugs discovery. However, membrane proteins are one of the most understudied groups of proteins in biochemical research because of technical difficulties of attaining structural information about transmembrane regions or domains. Structural determination of TM regions is an important priority in pharmaceutical industry, as it paves the way for structure based drug design. This research presents a novel evolutionary support vector machine (SVM) based alpha-helix transmembrane region prediction algorithm to solve the membrane helices in amino acid sequences. The SVM-genetic algorithm (GA) methodology is based on the optimisation of sliding window size, evolutionary encoding selection and SVM parameter optimisation. In this research average hydrophobicity and propensity based on skew statistics are used to encode the one letter representation of amino acid sequences datasets. The computer simulation results demonstrate that the proposed SVM-GA methodology performs better than most conventional techniques producing an accuracy of 86.71% for cross-validation and 86.43% for jack-knife for randomly selected proteins containing single and multiple transmembrane regions. Furthermore, for the amino acid sequence 3LVG, the proposed SVM-GA produces better alpha-helix region identification than PRED-TMR2, MEMSATSVM/MEMSAT3 and PSIPRED V3.0. [Copyright &y& Elsevier]

Abstract: A Neuro-Fuzzy Performance Evaluation Model (NFPEM) proposed in Akinnuwesi, Uzoka, Olabiyisi, and Omidiora (2012) was reviewed in this work with the view of modifying it and thus making it flexible and scalable. The neuro-fuzzy expert system (NFES) reported in this paper is an enhancement to NFPEM with expert system components. NFES can be used to evaluate the performance of Distributed Software System Architecture (DSSA) with user-centric variables as parameters for performance measurement. The algorithm developed for NFES was implemented using Coldfusion programming language and MySQL relational database management system. The prototype of NFES was simulated using some life data and the performance results obtained point to the DSSA responsiveness to the users’ requirements that are defined at the requirements definition phase of the software development process. Thus the performance value is a qualitative value representing DSSA (i.e. system) responsiveness. [Copyright &y& Elsevier]

Abstract: The Computerized Adaptive Tests (CAT) are common tools for the diagnosis process in Intelligent Tutor System based on Competency education (ITS-C). The item selection process to form a CAT plays a key role because it must ensure the selection of the item that best contributes to student assessment at any time. The item selection mechanisms proposed in the literature present some limitations that decrease the efficiency of CAT and its adaptation to the student profile. This paper introduces a new item selection algorithm, based on a multi-criteria decision model that integrates experts’ knowledge modeled by fuzzy linguistic information that overcomes previous limitations and enhances the accuracy of diagnosis and the adaptation of CAT to student’s competence level. Finally, such an algorithm is deployed in a mobile tool for an ITS-C. [Copyright &y& Elsevier]

Abstract: This paper presents a high payload data-hiding scheme for audio signals following the OFDM concept. It is based on changing the phase component of the audio signal via a reduced-arc M-order Phase Shift Keying (MPSK) modulator on selected frequency samples of the audio signal. This approach allows the data-hiding payload and perceptual distortion to be controlled by the number of altered frequency components of the audio signal and the severity of this alteration. The data-hiding payload can be estimated a priori independently of the audio signal features. In addition, due to the relation of the proposed scheme with the traditional use of MPSK modulation, the BER of the encoded data is analytically derived, therefore, it is known in advance. This allows the transmission of reliable hidden data, in contrast to previous works. Results obtained with representative test cases show that the proposed algorithm provides an increase of one order of magnitude in data payload compared with state-of-the-art works. [Copyright &y& Elsevier]

Abstract: The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data. [Copyright &y& Elsevier]

Abstract: Up to now, more and more online sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join online interest groups where they shall meet people with common tastes. Therefore, in this environment, users might be interested in not only receiving item recommendations (such as music), but also getting friend suggestions so they might put them in the contact list, and group recommendations that they could consider joining. To support such demanding needs, in this paper, we propose a unified framework that provides three different types of recommendation in a single system: recommending items, recommending groups and recommending friends. For each type of recommendation, we in depth investigate the contribution of fusing other two auxiliary information resources (e.g., fusing friendship and membership for recommending items, and fusing user-item preferences and friendship for recommending groups) for boosting the algorithm performance. More notably, the algorithms were developed based on the matrix factorization framework in order to achieve the ideal efficiency as well as accuracy. We performed experiments with two large-scale real-world data sets that contain users’ implicit interaction with items. The results revealed the effective fusion mechanism for each type of recommendation in such implicit data condition. Moreover, it demonstrates the respective merits of regularization model and factorization model: the factorization is more suitable for fusing bipartite data (such as membership and user-item preferences), while the regularization model better suits one mode data (like friendship). We further enhanced the friendship’s regularization by integrating the similarity measure, which was experimentally proven with positive effect. [Copyright &y& Elsevier]

Abstract: This paper presents a new algorithm for fingerprint indexing, which is based on minutia triplets, and it is very tolerant to missing and spurious minutiae. In this sense, a novel representation for fingerprints is proposed by defining a triangle set based on extensions of Delaunay triangulations. Moreover, a set of robust features is used to build indices. Finally, a recovery method based on calculating the recommendation score is introduced, using a new similarity function between geometric transformations. Our proposal was tested on well known databases, showing that it outperforms most of the already reported methods, especially under conditions of distortions. [Copyright &y& Elsevier]

Abstract: In this paper, we consider multiprocessor scheduling problems, where each job (task) must be executed simultaneously by the specified number of processors, but the indices of the processors allotted to each job do not have to be contiguous (i.e., jobs can be fragmentable). Unlike other research in this domain, we analyse the problem under the workspan criterion, which is defined as the product of the maximum job completion time (makespan) and the number of used processors. Moreover, the job processing times can be described by non-increasing or non-decreasing functions dependent on the start times of jobs that model improvement (learning) or degradation (deteriorating), respectively. To solve the problems, we construct some polynomial time algorithms and analyse numerically their efficiency. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc. [Copyright &y& Elsevier]

Abstract: Machine learning support for medical decision making is truly helpful only when it meets two conditions: high prediction accuracy and a good explanation of how the diagnosis was reached. Support vector machines (SVMs) successfully achieve the first target due to a kernel-based engine; evolutionary algorithms (EAs) can greatly accomplish the second owing to their adaptable nature. In this context, the current paper puts forward a two-step hybridized methodology, where learning is accurately performed by the SVMs and a comprehensible emulation of the resulting decision model is generated by EAs in the form of propositional rules, while referring only those indicators that highly influence the class separation. An individual highlighting of the medical attributes that trigger a specific diagnosis for a current patient record is additionally obtained; this feature thus increases the confidence of the physician in the resulting automated diagnosis. Without loss of generality, we aim to model three breast cancer instances, for reasons of both high incidence of the disease and the large application of state of the art artificial intelligence methods for this medical task. As such, the prediction of a benign/malignant condition as well as the recurrence/nonrecurrence of a cancer event are studied on the Wisconsin corresponding data sets from the UCI Machine Learning Repository. The proposed hybridization reached its goals. Rule prototypes evolve against a SVM consistent training data, while diversity among the different classes is implicitly preserved. Feature selection eventually leads to a resulting rule set where only the significant medical indicators together with the discriminating threshold values are referred, while individual relevance of attributes can be additionally obtained for each patient. The gain is thus dual: the EA benefits from a noise-free SVM preprocessed data and the resulting SVM model is able to output rules in a comprehensible, concise format for the physician. [Copyright &y& Elsevier]

Abstract: This paper describes an Internet browsing application based on electrooculography (EOG) aimed at people with a severe motor disability, i.e., people who cannot move their arms. This application is made up of two subsystems: writing and browser. It allows the user to navigate through Internet introducing text, clicking on active elements and moving around the screen using only their eyes. It also includes a word predictor to increase navigation speed. An EOG algorithm is capable of detecting four different directions of the users’ eyes movement: left, right, up and down, and the blink. With these five commands, the user has full control of the application. Six healthy volunteers tested the Internet browsing application by performing three different tests that consisted of writing, searching and mouse controlling tasks. The results show that all users are capable of completing the whole tests in a reasonable time and they improve their speed with training. These results suggest that the Internet browsing application could be used by disabled people, improving their integration in the information society. [Copyright &y& Elsevier]

Abstract: A federation of data warehouses is understood as a set of data warehouses, which can be processed as a whole in the logic level. Physically, the federation does not gather data into one place. This paper presents a formal framework for data and knowledge processing in data warehouse federations. The management system for a data warehouse federation consists of an user interface enabling presentation of user queries, a program for query decomposition and a program for integrating knowledge coming from different data warehouses as the answers to a user query. We propose a model for query decomposition process and knowledge integration. It contains also the algorithm for knowledge inconstancy processing. This kind of inconsistency often occurs since very often the knowledge extracted from different data warehouses refers to the same subject, but is not consistent. [Copyright &y& Elsevier]

Abstract: In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. [Copyright &y& Elsevier]

Abstract: Building a high accuracy classifier for classification is a problem in real applications. One high accuracy classifier used for this purpose is based on association rules. In the past, some researches showed that classification based on association rules (or class-association rules – CARs) has higher accuracy than that of other rule-based methods such as ILA and C4.5. However, mining CARs consumes more time because it mines a complete rule set. Therefore, improving the execution time for mining CARs is one of the main problems with this method that needs to be solved. In this paper, we propose a new method for mining class-association rule. Firstly, we design a tree structure for the storage frequent itemsets of datasets. Some theorems for pruning nodes and computing information in the tree are developed after that, and then, based on the theorems, we propose an efficient algorithm for mining CARs. Experimental results show that our approach is more efficient than those used previously. [Copyright &y& Elsevier]

Abstract: Traditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries. In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results. Our work is supported by (1) the logs generated after query searching, (2) semantic annotations of queries and (3) semantic annotations of web pages. The algorithm makes use of this information to elaborate an appropriate ranking. To validate our approach we have implemented a system that can apply the algorithm to a particular search engine. Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it. [Copyright &y& Elsevier]

Abstract: Kernel based methods have been widely applied for signal analysis and processing. In this paper, we propose a sparse kernel based algorithm for online time series prediction. In classical kernel methods, the kernel function number is very large which makes them of a high computational cost and only applicable for off-line or batch learning. In online learning settings, the learning system is updated when each training sample is obtained and it requires a higher computational speed. To make the kernel methods suitable for online learning, we propose a sparsification method based on the Hessian matrix of the system loss function to continuously examine the significance of the new training sample in order to select a sparse dictionary (support vector set). The Hessian matrix is equivalent to the correlation matrix of sample inputs in the kernel weight updating using the recursive least square (RLS) algorithm. This makes the algorithm able to be easily implemented with an affordable computational cost for real-time applications. Experimental results show the ability of the proposed algorithm for both real-world and artificial time series data forecasting and prediction. [Copyright &y& Elsevier]

Abstract: Effective data mining solutions have for long been anticipated in Customer Relationship Management (CRM) to accurately predict customer behavior, but from various industry research and case studies we have observed sub-optimal CRM classification models due to inferior data quality inherent to CRM data set. In this paper, one type of CRM data with a distinctive distribution pattern of Reduced Dimensionality is discussed. A new classification framework termed Partial Focus Feature Reduction is proposed to resolve CRM data set with Reduced Dimensionality using a collection of efficient data mining techniques characterizing a specially tailored modality grouping method to significantly improve data quality and feature relevancy after preprocessing, eventually achieving excellent classification performance with the right combination of classification algorithms. [Copyright &y& Elsevier]

Abstract: Thyroid cancers are the most common endocrine carcinomas. Case-based reasoning (CBR) is used in this paper to describe a physician’s expertise, intuition and experience when treating patients with well differentiated thyroid cancer. Various clinical parameters (the patient’s diagnosis, the patient’s age, the tumor size, the existence of metastases in the lymph nodes and the existence of distant metastases) influence a physician’s decision-making in dose planning. The weights (importance) of these parameters are determined here with the Bee Colony Optimization (BCO) meta-heuristic. The proposed CBR–BCO model suggests the I-131 iodine dose in radioactive iodine therapy. This approach is tested on real data from patients treated in the Department of Nuclear Medicine, Clinical Center Kragujevac, Serbia. By comparing the results that are obtained through the developed CBR–BCO model with those resulting from the physician’s decision, it has been found that the developed model is highly reflective of reality. [Copyright &y& Elsevier]

Abstract: This paper presents a new method for calculating the design of roadway lighting. Apart from its accuracy, this method, which is based on a multi-objective evolutionary algorithm, has the added advantage of enhancing the energy efficiency of lighting installations. This is positive because the economic use of energy resources is evidently a priority in the world today. In our study, an exhaustive calibration process was used to fine-tune the accuracy and precision of the new method presented. The results obtained were then compared with those of DIALUX, a well-known free software program that is frequently used for the design of lighting installations. In the second phase of this research, the lighting installation was made more complex in order to verify the applicability of this new method to a wide range of different contexts. [Copyright &y& Elsevier]

Abstract: This paper reports on a system developed to support medical experts in the process of updating clinical guidelines by automatically suggesting new articles suitable to the domain under consideration. It follows a comprehensive process based on several consecutive steps in order to (i) identify which articles from the current guideline are eligible to be updated; (ii) retrieve and filter new related articles from medline; and (iii) select the most relevant resulting articles by applying a scoring algorithm. Extensive validation is based on a set of experiments on 40 guidelines from multiple medical domains. The analysis of results shows a promising prospect as indicated by recall values greater than 90%. [Copyright &y& Elsevier]

Abstract: The mutation score is an important measure to evaluate the quality of the test cases. It is obtained by executing a lot of mutant programs generated by a set of operators. A common problem, however, is that some operators can generate unnecessary and redundant mutants. Because of this, different strategies were proposed to find a set of operators that generates a reduced number of mutants without decreasing the mutation score. However, the operator selection, in practice, may include real constraints and is dependent on diverse factors besides the number of mutants and score, such as: number of test data, execution time, number of revealed faults, number of equivalent mutants, etc. In fact this is a multi-objective problem, which does not have a single solution. Different set of operators exist for multiple objectives to be satisfied, and some restrictions can be used to choose among the existing sets. To make this choice possible, in this paper, we introduce a multi-objective strategy. We investigate three multi-objective algorithms and introduce a procedure to establish a set of operators to prioritize mutation score. Better results are obtained in comparison with traditional strategies. [Copyright &y& Elsevier]

Abstract: E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs. [Copyright &y& Elsevier]

Abstract: Constructing virtual 3D historical scenes from literature and records is a very challenging problem due to the difficulty in incorporating different types of domain knowledge into the modeling system. The domain knowledge comes from different experts, including: architects, historians, rendering artists, user interface designers and computer engineers. In this paper we investigate the problem of automatically generating drawings of ancient scenes by ontologies extracted from these domains. We introduce a framework called onto-draw to generate semantic models of desired scenes by constructing hierarchical ontology concept domains. Inconsistencies among them are resolved via an iterative refinement algorithm. We implement the onto-draw based ontology design approach and inconsistency removal technique in the virtual Jing-Hang Grand Canal construction project () and achieve encouraging results. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel robust digital color image watermarking algorithm which combines color image feature point extraction, shape image normalization and QPCA (quaternion principal component algorithm) based watermarking embedding (QWEMS) and extraction (QWEXS) schemes. The feature point extraction method called Mexican Hat wavelet scale interaction is used to select the points which can survive various attacks and also be used as reference points for both watermarking embedding and extraction. The normalization shape image of the local quadrangle image of which the four corners are feature points of the original image is invariant to translation, rotation, scaling and skew, by which we can obtain the relationship between the feature images of the original image and the watermarked image which has suffered with geometrical attacks. The proposed QWEMS and QWEXS schemes which denote the color pixel as a pure quaternion and the feature image as a quaternion matrix can improve the robustness and the imperceptibility of the embedding watermarking. To simplify the eigen-decomposition procedure of the quaternion matrix, we develop a calculation approach with which the eigen-values and the corresponding eigen-vectors of the quaternion matrix can be computed. A binary watermark image is embedded in the principal component coefficients of the feature image. Simulation results demonstrate that the proposed algorithm can survive a variety of geometry attacks, i.e. translation, rotation, scaling and skew, and can also resist the attacks of many signal processing procedures, for example, moderate JPEG compression, salt and pepper noise, Gaussian filtering, median filtering, and so on. [Copyright &y& Elsevier]

Abstract: High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms. [Copyright &y& Elsevier]

Abstract: The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (λ-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of λ-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions). [Copyright &y& Elsevier]

Abstract: This work implements a new text document classifier by integrating the K-nearest neighbor (KNN) classification approach with the support vector machine (SVM) training algorithm. The proposed Nearest Neighbor-Support Vector Machine hybrid classification approach is coined as SVM-NN. The KNN has been reported as one of the widely used text classification approaches due to its simplicity and efficiency in handling various types of text classification tasks. However, there exists a major problem of the KNN in determining the appropriate value for parameter K in order to guarantee high classification effectiveness. This is due to the fact that the selection of the value of parameter K has high impact on the accuracy of the KNN classifier. Other than determining the optimal value of parameter K, the KNN is also a lazy learning method which keeps the entire training samples until classification time. Hence, the computational process of the KNN has become intensive when the value of parameter K increases. In this paper, we propose the SVM-NN hybrid classification approach with the objective that to minimize the impact of parameter on classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs). The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the Euclidean distance function is used to calculate the average distance between the testing data point to each set of SVs of different categories. The classification decision is made based on the category which has the shortest average distance between its SVs and the testing data point. The experiments on several benchmark text datasets show that the classification accuracy of the SVM-NN approach has low impact on the value of parameter, as compared to the conventional KNN classification model. [Copyright &y& Elsevier]

Abstract: A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets. [Copyright &y& Elsevier]

Abstract: Web usage mining is the process of extracting useful information from users history databases associated to an e-commerce website. The extraction is usually performed by data mining techniques applied on server log data or data obtained from specific tools such as Google Analytics. This paper presents the methodology used in an e-commerce website of extra virgin olive oil sale called www.OrOliveSur.com. We will describe the set of phases carried out including data collection, data preprocessing, extraction and analysis of knowledge. The knowledge is extracted using unsupervised and supervised data mining algorithms through descriptive tasks such as clustering, association and subgroup discovery; applying classical and recent approaches. The results obtained will be discussed especially for the interests of the designer team of the website, providing some guidelines for improving its usability and user satisfaction. [Copyright &y& Elsevier]

Abstract: Thales’ second theorem can be used for the automatic detection of the vanishing points of an image. This paper explores its reliability and accuracy according to the type of operator used for the detection of edges. An algorithm has been used which processes a photographic image according to the operator selected. The result is a point cloud which is then used to find the desired solution. The comparison between the four discrete gradient operators (Frei-Chen, Prewitt, Roberts and Sobel) has been made taking into account the resolution of the image and the number of vanishing points. The results obtained show that Frei-Chen’s operator shows good performance in determining vanishing points with respect to the spatial X axis, Sobel’s operator is the best for determining the vanishing point with respect to the spatial Y axis, Roberts’ operator gives good results for calculating vanishing points in both spatial axes, and Prewitt’s operator is not appropriate for processing this type of image. [Copyright &y& Elsevier]

Abstract: Graph data have been of common practice in many application domains. However, it is very difficult to deal with graphs due to their intrinsic complex structure. In this paper, we propose to apply Formal Concept Analysis (FCA) to learning from graph data. We use subgraphs appearing in each of graph data as its attributes and construct a lattice based on FCA to organize subgraph attributes which are too numerous. For statistical learning purpose, we propose a similarity measure based on the concept lattice, taking into account the lattice structure explicitly. We prove that, the upper part of the lattice can provide a reliable and feasible way to compute the similarity between graphs. We also show that the similarity measure is rich enough to include some other measures as subparts. We apply the measure to a transductive learning algorithm for graph classification to prove its efficiency and effectiveness in practice. The high accuracy and low running time results confirm empirically the merit of the similarity measure based on the lattice. [Copyright &y& Elsevier]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]

Abstract: Many approaches are proposed to improve Naive Bayes, among which the attribute selection approach has demonstrated remarkable performance. Algorithms for attribute selection fall into two broad categories: filters and wrappers. Filters use the general data characteristics to evaluate the selected attribute subset before the learning algorithm is run, while wrappers use the learning algorithm itself as a black box to evaluate the selected attribute subset. In this paper, we work on the attribute selection approach of wrapper and propose an improved Naive Bayes algorithm by carrying a random search through the whole space of attributes. We simply called it Randomly Selected Naive Bayes (RSNB). In order to meet the need of classification, ranking, and class probability estimation, we discriminatively design three different versions: RSNB-ACC, RSNB-AUC, and RSNB-CLL. The experimental results based on a large number of UCI datasets validate their effectiveness in terms of classification accuracy (ACC), area under the ROC curve (AUC), and conditional log likelihood (CLL), respectively. [Copyright &y& Elsevier]

Abstract: Dimensionality reduction has been applied in the most different areas, among which the data analysis of gene expression obtained with the microarray approach. The data involved in this problem is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of attribute selection and random projection method in microarray data. Experimental results are promising and show that the use of these methods improves the performance of classification algorithms. [Copyright &y& Elsevier]

Abstract: Widespread digitization of information in today’s internet age has intensified the need for effective textual document classification algorithms. Most real life classification problems, including text classification, genetic classification, medical classification, and others, are complex in nature and are characterized by high dimensionality. Current solution strategies include Naïve Bayes (NB), Neural Network (NN), Linear Least Squares Fit (LLSF), k-Nearest-Neighbor (kNN), and Support Vector Machines (SVM); with SVMs showing better results in most cases. In this paper we introduce a new approach called dynamic architecture for artificial neural networks (DAN2) as an alternative for solving textual document classification problems. DAN2 is a scalable algorithm that does not require parameter settings or network architecture configuration. To show DAN2 as an effective and scalable alternative for text classification, we present comparative results for the Reuters-21578 benchmark dataset. Our results show DAN2 to perform very well against the current leading solutions (kNN and SVM) using established classification metrics. [Copyright &y& Elsevier]

Abstract: Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be generally more accurate than single prediction models. The present paper goes one step beyond by introducing composite ensembles that jointly use different strategies for diversity induction. Accordingly, the combination of data resampling algorithms (bagging and AdaBoost) and attribute subset selection methods (random subspace and rotation forest) for the construction of composite ensembles is explored with the aim of improving the prediction performance. The experimental results and statistical tests show that this new two-level classifier ensemble constitutes an appropriate solution for credit scoring problems, performing better than the traditional single ensembles and very significantly better than individual classifiers. [Copyright &y& Elsevier]

Abstract: Accurate estimation of mass transport parameters is necessary for overall design and evaluation processes of the waste disposal facilities. The mass transport parameters, such as effective diffusion coefficient, retardation factor and diffusion accessible porosity, are estimated from observed diffusion data by inverse analysis. Recently, particle swarm optimization (PSO) algorithm has been used to develop inverse model for estimating these parameters that alleviated existing limitations in the inverse analysis. However, PSO solver yields different solutions in successive runs because of the stochastic nature of the algorithm and also because of the presence of multiple optimum solutions. Thus the estimated mean solution from independent runs is significantly different from the best solution. In this paper, two variants of the PSO algorithms are proposed to improve the performance of the inverse analysis. The proposed algorithms use perturbation equation for the gbest particle to gain information around gbest region on the search space and catfish particles in alternative iterations to improve exploration capabilities. Performance comparison of developed solvers on synthetic test data for two different diffusion problems reveals that one of the proposed solvers, CPPSO, significantly improves overall performance with improved best, worst and mean fitness values. The developed solver is further used to estimate transport parameters from 12 sets of experimentally observed diffusion data obtained from three diffusion problems and compared with published values from the literature. The proposed solver is quick, simple and robust on different diffusion problems. [Copyright &y& Elsevier]

Abstract: The shortening of the design cycle and the increase of the performance are nowadays the main challenges in aerodynamic design. The use of evolutionary algorithms (EAs) seems to be appropriate in a preliminary phase, due to their ability to broadly explore the design space and obtain global optima. Evolutionary algorithms have been hybridized with metamodels (or surrogate models) in several works published in the last years, in order to substitute expensive computational fluid dynamics (CFD) simulations. In this paper, an advanced approach for the aerodynamic optimization of aeronautical wing profiles is proposed, consisting of an evolutionary programming algorithm hybridized with a support vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size and feasibility of the complete approach are discussed and the potential of global optimization methods (enhanced by metamodels) to achieve innovative shapes that would not be achieved with traditional methods is assessed. [Copyright &y& Elsevier]

Abstract: In this paper, we employ a novel two-stage soft computing approach for data imputation to assess the severity of phishing attacks. The imputation method involves K-means algorithm and multilayer perceptron (MLP) working in tandem. The hybrid is applied to replace the missing values of financial data which is used for predicting the severity of phishing attacks in financial firms. After imputing the missing values, we mine the financial data related to the firms along with the structured form of the textual data using multilayer perceptron (MLP), probabilistic neural network (PNN) and decision trees (DT) separately. Of particular significance is the overall classification accuracy of 81.80%, 82.58%, and 82.19% obtained using MLP, PNN, and DT respectively. It is observed that the present results outperform those of prior research. The overall classification accuracies for the three risk levels of phishing attacks using the classifiers MLP, PNN, and DT are also superior. [Copyright &y& Elsevier]

Abstract: A detailed comparative analysis of the Hebbian-like learning algorithms applied to train Fuzzy Cognitive Maps (FCMs) operating as pattern classifiers, is presented in this paper. These algorithms aim to find appropriate weights between the concepts of the FCM classifier so it equilibrates to a desired state (class mapping). For these purposes, six different types of Hebbian learning algorithms from the literature have been selected and studied in this work. Along with the theoretical description of these algorithms and the analysis of their performance in classifying known patterns, a sensitivity analysis of the applied classification scheme, regarding some configuration parameters have taken place. It is worth noting that the algorithms are studied in a comparative fashion, under common configurations for several benchmark pattern classification datasets, by resulting to useful conclusions about their training capabilities. [Copyright &y& Elsevier]

Abstract: Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min–max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new multiattribute decision making method based on the proposed interval-valued intuitionistic fuzzy weighted average operator and the proposed fuzzy ranking method for intuitionistic fuzzy values. First, we briefly review the concepts of interval-valued intuitionistic fuzzy sets and the Karnik–Mendel algorithms. Then, we propose the intuitionistic fuzzy weighted average operator and interval-valued intuitionistic fuzzy weighted average operator, based on the traditional weighted average method and the Karnik–Mendel algorithms. Then, we propose a fuzzy ranking method for intuitionistic fuzzy values based on likelihood-based comparison relations between intervals. Finally, we present a new multiattribute decision making method based on the proposed interval-valued intuitionistic fuzzy weighted average operator and the proposed fuzzy ranking method for intuitionistic fuzzy values. The proposed method provides us with a useful way for multiattribute decision making based on interval-valued intuitionistic fuzzy values. [Copyright &y& Elsevier]

Abstract: Constructing a fingerprint database is important to evaluate the performance of an automatic fingerprint recognition system. Because of the difficulty of collecting samples, there are only few benchmark databases available. Moreover, it is hard to evaluate how robust the system is against various environments with those databases. This paper presents a novel method that generates fingerprint images automatically from only a few training samples by using the evolutionary algorithm. Fingerprints generated by the proposed method include similar characteristics of those collected from the corresponding real environment. The proposed method has been verified by comparing with real fingerprints, indicating the usefulness of the method. [Copyright &y& Elsevier]

Abstract: The global optimization problem is not easy to solve and is still an open challenge for researchers since an analytical optimal solution is difficult to obtain even for relatively simple application problems. Conventional deterministic numerical algorithms tend to stop the search in local minimum nearest to the input starting point, mainly when the optimization problem presents nonlinear, non-convex and non-differential functions, multimodal and nonlinear. Nowadays, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. The primary advantage of EAs over other numerical methods is that they just require the objective function values, while properties such as differentiability and continuity are not necessary. In this context, the differential evolution (DE), a paradigm of the evolutionary computation, has been widely used for solving numerical global optimization problems in continuous search space. DE is a powerful population-based stochastic direct search method. DE simulates natural evolution combined with a mechanism to generate multiple search directions based on the distribution of solutions in the current population. Among DE advantages are its simple structure, ease of use, speed, and robustness, which allows its application on several continuous nonlinear optimization problems. However, the performance of DE greatly depends on its control parameters, such as crossover rate, mutation factor, and population size and it often suffers from being trapped in local optima. Conventionally, users have to determine the parameters for problem at hand empirically. Recently, several adaptive variants of DE have been proposed. In this paper, a modified differential evolution (MDE) approach using generation-varying control parameters (mutation factor and crossover rate) is proposed and evaluated. The proposed MDE presents an efficient strategy to improve the search performance in preventing of premature convergence to local minima. The efficiency and feasibility of the proposed MDE approach is demonstrated on a force optimization problem in Robotics, where the force capabilities of a planar 3-RRR parallel manipulator are evaluated considering actuation limits and different assembly modes. Furthermore, some comparison results of MDE approach with classical DE to the mentioned force optimization problem are presented and discussed. [Copyright &y& Elsevier]

Abstract: In this paper, an effective hybrid algorithm based on estimation of distribution algorithm (EDA) is proposed to solve the multidimensional knapsack problem (MKP). With the framework of EDA, the probability model is built with the superior population and the new individuals are generated based on probability model. In addition, an updating mechanism of the probability model is proposed and a mechanism for initializing the probability model based on the specific knowledge of the MKP is also proposed to improve the convergence speed. Meanwhile, an adaptive local search is proposed to enhance the exploitation ability. Furthermore, the influences of parameters are investigated based on Taguchi method of design of experiment and the importance of repair operator is also studied via simulation testing and comparisons. Finally, numerical simulation is carried out based on the benchmark instances, and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel swarm intelligence technique, which is an adaptation of Abbass’s marriage in honey-bee optimization (MBO), with the aim to achieve better overall performance than the original version of the MBO while also lowering the computation time for finding the optimal solution. The original MBO has been proven to be one of the best swarm intelligence algorithms for solving optimization problems. However, many parameters need to be properly set in order for the MBO to perform at its best. Therefore, long computation time caused by a large number of trial and error iterations involved in trying to find the right combination of parameters is unavoidable. The framework of the proposed algorithm is similar to the original MBO, which is based on the marriage behavior of honey-bees. In order to improve the efficiency of the MBO algorithm, several aspects of the original MBO have been adapted, such as (1) the proposed algorithm is adapted to obtain the ability to automatically search for the proper number of queens, (2) the proposed algorithm divides the problem space into several colonies, each of which has its own queen. In order to keep the number of colonies to a minimum, the proposed algorithm, therefore, encourages the queens to compete with each other for a larger colony and also urges the newly-born brood which is fitter than the queen of the colony to overthrow the queen. (3) the fuzzy c-means algorithm is employed to assign the drones to the proper colonies. The proposed algorithm has been evaluated and compared to the original MBO algorithm. The experimental results on six benchmark problems demonstrate the potential of the proposed algorithm in offering an efficient and effective solution to the problem. [Copyright &y& Elsevier]