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Publisher: Springer-Verlag (Total: 2352 journals)

 Applied Intelligence   [SJR: 0.777]   [H-I: 43]   [11 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X    Published by Springer-Verlag  [2352 journals]
• A social recommender system using item asymmetric correlation
Pages: 527 - 540
Abstract: Recommender systems have been one of the most prominent information filtering techniques during the past decade. However, they suffer from two major problems, which degrade the accuracy of suggestions: data sparsity and cold start. The popularity of social networks shed light on a new generation of such systems, which is called social recommender system. These systems act promisingly in solving data sparsity and cold start issues. Given that social relationships are not available to every system, the implicit relationship between the items can be an adequate option to replace the constraints. In this paper, we explored the effect of combining the implicit relationships of the items and user-item matrix on the accuracy of recommendations. The new Item Asymmetric Correlation (IAC) method detects the implicit relationship between each pair of items by considering an asymmetric correlation among them. Two dataset types, the output of IAC and user-item matrix, are fused into a collaborative filtering recommender via Matrix Factorization (MF) technique. We apply the two mostly used mapping models in MF, Stochastic Gradient Descent and Alternating Least Square, to investigate their performances in the presence of sparse data. The experimental results of real datasets at four levels of sparsity demonstrate the better performance of our method comparing to the other commonly used approaches, especially in handling the sparse data.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0973-5
Issue No: Vol. 48, No. 3 (2018)

• Indirect adaptive type-2 bionic fuzzy control
• Authors: Faxiang Zhang; Jing Hua; Yimin Li
Pages: 541 - 554
Abstract: In this study, an indirect adaptive type-2 bionic fuzzy control method is proposed for a class of nonlinear systems. By regarding the niche of each species in an ecosystem as the antecedent, the fuzzy system with biological characteristics is constructed based on the niche “ecostate-ecorole” theory. In the actual system, we design a fuzzy control system using a type-2 bionic fuzzy system and provide both the adaptive law and constraint conditions of the system parameters. The stability of the closed-loop system is proved with all the state variables uniformly bounded in the Lyapunov sense. Additionally, the convergence of the bionic fuzzy control system is analyzed. Finally, the simulation results obtained for a permanent magnet direct current motor demonstrate the effectiveness and superiority of the designed method.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0991-3
Issue No: Vol. 48, No. 3 (2018)

• Ant colony algorithm for automotive safety integrity level allocation
• Authors: Youcef Gheraibia; Khaoula Djafri; Habiba Krimou
Pages: 555 - 569
Abstract: ISO 26262, the new automotive functional safety standard, aims to foster the design and development of safe products by ensuring that the risks posed by hazardous components are reduced to a residual level. Therefore, the standard defines and uses the concept of Automotive Safety Integrity Levels (ASILs) that classify the strictness of safety requirements to be assigned to the failure modes of the system based on the hazard they may cause. ASIL allocation can be described as a hard optimization problem focused on finding the optimal ASIL allocation that maximizes the safety requirements and minimizes cost. However, finding this optimal allocation among a set of possible allocations can represent a difficult task in large systems that contain a large number of components, which subsequently increases the search space. In this paper, we introduce a novel approach that uses the nature-inspired meta-heuristic Ant Colony Optimization (ACO) algorithm to solve the ASIL allocation problem and makes use of strategies that reduce the solution space. The problem was formulated as a construction graph, which the ants use to construct possible ASIL allocations. The search space reduction is accelerated considerably by both the effective performance of the ACO and the convergence of the algorithm on the optimal solution. This approach has been evaluated by applying it to a hybrid braking system and a steer-by-wire system. The results show a significant improvement over genetic-based, penguins search-based and tabu search-based approaches.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1000-6
Issue No: Vol. 48, No. 3 (2018)

• Minimum positive influence dominating set and its application in influence
maximization: a learning automata approach
• Authors: Mohammad Mehdi Daliri Khomami; Alireza Rezvanian; Negin Bagherpour; Mohammad Reza Meybodi
Pages: 570 - 593
Abstract: In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS) problem, and then use the MPIDS for influence maximization in online social networks. We also prove that by proper choice of the parameters of the algorithm, the probability of finding the MPIDS can be made as close to unity as possible. Experimental simulations on real and synthetic networks confirm the superiority of the algorithm for finding the MPIDS Experimental results also show that finding initial target seeds for influence maximization using the MPIDS outperforms well-known existing algorithms.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0987-z
Issue No: Vol. 48, No. 3 (2018)

• Feature clustering based support vector machine recursive feature
elimination for gene selection
• Authors: Xiaojuan Huang; Li Zhang; Bangjun Wang; Fanzhang Li; Zhao Zhang
Pages: 594 - 607
Abstract: In a DNA microarray dataset, gene expression data often has a huge number of features(which are referred to as genes) versus a small size of samples. With the development of DNA microarray technology, the number of dimensions increases even faster than before, which could lead to the problem of the curse of dimensionality. To get good classification performance, it is necessary to preprocess the gene expression data. Support vector machine recursive feature elimination (SVM-RFE) is a classical method for gene selection. However, SVM-RFE suffers from high computational complexity. To remedy it, this paper enhances SVM-RFE for gene selection by incorporating feature clustering, called feature clustering SVM-RFE (FCSVM-RFE). The proposed method first performs gene selection roughly and then ranks the selected genes. First, a clustering algorithm is used to cluster genes into gene groups, in each which genes have similar expression profile. Then, a representative gene is found to represent a gene group. By doing so, we can obtain a representative gene set. Then, SVM-RFE is applied to rank these representative genes. FCSVM-RFE can reduce the computational complexity and the redundancy among genes. Experiments on seven public gene expression datasets show that FCSVM-RFE can achieve a better classification performance and lower computational complexity when compared with the state-the-art-of methods, such as SVM-RFE.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0992-2
Issue No: Vol. 48, No. 3 (2018)

• A temporal modal defeasible logic for formalizing social commitments in
dialogue and argumentation models
Pages: 608 - 627
Abstract: In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0983-3
Issue No: Vol. 48, No. 3 (2018)

• Multiobjective differential evolution using homeostasis based mutation for
application in software cost estimation
• Authors: Shailendra Pratap Singh; Anoj Kumar
Pages: 628 - 650
Abstract: The problem in software cost estimation revolves around accuracy. To improve the accuracy, heuristic/meta-heuristic algorithms have been known to yield better results when it is applied in the domain of software cost estimation. For the sake of accuracy in results, we are still modifying these algorithms. Here we have proposed a new meta-heuristic algorithm based on Differential Evolution (DE) by Homeostasis mutation operator. Software development requires high prediction and low Root Mean Squared Error (RMSE) and mean magnitude relative error(MMRE). The problem in software cost estimation relates to accurate prediction and minimization of RMSE and MMRE, which are used to solve multiobjective optimization. Many versions of DE were proposed, however multi-objective versions where the concept of Pareto optimality is used, are most popular. Pareto-Based Differential Evolution (PBDE) is one of them. Although the performance of this algorithm is very good, its convergence rate can be further improved by minimizing the time complexity of nondominated sorting, and by improving the diversity of solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant multiplies the Homeostasis value with one more vector, named the Homeostasis mutation vector, in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escape the situation of stagnation. The performance of the proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on the Pareto-optimal front. The performance of the proposed algorithm is compared with other state-of-the-art algorithms on five multi-objective evolutionary algorithms (MOEAs). The result verifies that our proposed Homeostasis mutation strategy performs better than other state-of-the-art algorithms. Finally, application of MODE-HBM is applied to solve in terms of Pareto front, representing the trade-off between development RMSE, MMRE, and prediction for COCOMO model.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0980-6
Issue No: Vol. 48, No. 3 (2018)

• Multi-objective service composition model based on cost-effective
optimization
• Authors: Ying Huo; Peng Qiu; Jiyou Zhai; Dajuan Fan; Huanfeng Peng
Pages: 651 - 669
Abstract: The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users’ requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective Artificial Bee Colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0996-y
Issue No: Vol. 48, No. 3 (2018)

• Chaotic antlion algorithm for parameter optimization of support vector
machine
• Authors: Alaa Tharwat; Aboul Ella Hassanien
Pages: 670 - 686
Abstract: Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion Optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Social Emotional Optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0994-0
Issue No: Vol. 48, No. 3 (2018)

• Bio-inspired metaheuristics: evolving and prioritizing software test data
• Authors: Mukesh Mann; Pradeep Tomar; Om Prakash Sangwan
Pages: 687 - 702
Abstract: Software testing is both a time and resource-consuming activity in software development. The most difficult parts of software testing are the generation and prioritization of test data. Principally these two parts are performed manually. Hence introducing an automation approach will significantly reduce the total cost incurred in the software development lifecycle. A number of automatic test case generation (ATCG) and prioritization approaches have been explored. In this paper, we propose two approaches: (1) a pathspecific approach for ATCG using the following metaheuristic techniques: the genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony optimization (ABC); and (2) a test case prioritization (TCP) approach using PSO. Based on our experimental findings, we conclude that ABC outperforms the GA and PSO-based approaches for ATC.G Moreover, the results for PSO on TCP arguments demonstrate biased applicability for both small and large test suites against random, reverse and unordered prioritization schemes. Therefore, we focus on conducting a comprehensive and exhaustive study of the application of metaheuristic algorithms in solving ATCG and TCP problems in software engineering.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1003-3
Issue No: Vol. 48, No. 3 (2018)

• Total utility of Z-number
• Authors: Bingyi Kang; Yong Deng; Rehan Sadiq
Pages: 703 - 729
Abstract: Z-numbers, combined with “constraint” and “reliability”, has more power to express human knowledge. How to determine the ordering of Z-numbers and how to make a decision with Z-numbers are both meaningful and open issues. In this paper, a new notion of the total utility of Z-number is proposed to measure the total effects of a Z-number. The proposed total utility of Z-number can be used to determine the ordering of Z-numbers, and can also be simply applied in the application of multi-criteria decision making under uncertain environments. Two particular cases of Z-number (Gaussian and triangular), and some mathematical properties of the total utility of Z-number are discussed in this paper. Several applications and comparative analyses are shown to demonstrate the effectiveness of the proposed total utility of Z-number in the application of ordering Z-numbers and multi-criteria decision making.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1001-5
Issue No: Vol. 48, No. 3 (2018)

• Two time-efficient gibbs sampling inference algorithms for biterm topic
model
• Authors: Xiaotang Zhou; Jihong Ouyang; Ximing Li
Pages: 730 - 754
Abstract: Biterm Topic Model (BTM) is an effective topic model proposed to handle short texts. However, its standard gibbs sampling inference method (StdBTM) costs much more time than that (StdLDA) of Latent Dirichlet Allocation (LDA). To solve this problem we propose two time-efficient gibbs sampling inference methods, SparseBTM and ESparseBTM, for BTM by making a tradeoff between space and time consumption in this paper. The idea of SparseBTM is to reduce the computation in StdBTM by both recycling intermediate results and utilizing the sparsity of count matrix $$\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$$ . Theoretically, SparseBTM reduces the time complexity of StdBTM from O( B K) to O( B K w ) which scales linearly with the sparsity of count matrix $$\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$$ (K w ) instead of the number of topics (K) (K w < K, K w is the average number of non-zero topics per word type in count matrix $$\mathbf {N}^{\mathbf {T}}_{\mathbf {W}}$$ ). Experimental results have shown that in good conditions SparseBTM is approximately 18 times faster than StdBTM. Compared with SparseBTM, ESparseBTM is a more time-efficient gibbs sampling inference method proposed based on SparseBTM. The idea of ESparseBTM is to reduce more computation by recycling more intermediate results through rearranging biterm sequence. In theory, ESparseBTM reduces the time complexity of SparseBTM from O( B K w ) to O(R B K w ) (0 < R < 1, R is the ratio of the number of biterm types to the number of biterms). Experimental results have shown that the percentage of the time efficiency improved by ESparseBTM on SparseBTM is between 6.4% and 39.5% according to different datasets.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1004-2
Issue No: Vol. 48, No. 3 (2018)

• An EM based probabilistic two-dimensional CCA with application to face
recognition
• Authors: Mehran Safayani; Seyed Hashem Ahmadi; Homayun Afrabandpey; Abdolreza Mirzaei
Pages: 755 - 770
Abstract: Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognition tasks, it lacks a probabilistic interpretation. In this paper, we present a probabilistic framework for 2DCCA called probabilistic 2DCCA (P2DCCA) and an iterative EM based algorithm for optimizing the parameters. Experimental results on synthetic and real data demonstrate superior performance in loading factor estimation for P2DCCA compared to 2DCCA. For real data, three subsets of AR face database and also the UMIST face database confirm the robustness of the proposed algorithm in face recognition tasks with different illumination conditions, facial expressions, poses and occlusions.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1012-2
Issue No: Vol. 48, No. 3 (2018)

• Discrete particle swarm optimization algorithms for two variants of the
static data segment location problem
• Authors: Goutam Sen; Mohan Krishnamoorthy
Pages: 771 - 790
Abstract: We consider the static data segment location problem in information networks. This problem was introduced by Sen et al. (Comput Oper Res, 62:282–295 2015). We consider the problem of optimally locating large volumes of digital content that is accessed via a distributed network. A database is pre-partitioned into multiple segments and the problem is one of placing these segments at servers located in different regions. We need to jointly consider four specific subproblems: (1) the problem of locating servers in the network, (2) the problem of allocating specific data segments to each of the servers, (3) the problem of assigning users to the servers based on their query patterns, and, (4) routing queries through the network. We consider two variants of this problem depending on the topology of the network through which the servers are connected: a mesh topology and a tree topology. In this paper, we develop a solution approach based on a discrete particle swarm optimization approach. We demonstrate the superiority of our approach by comparing its performance against solutions to benchmark instances obtained previously using a simulated annealing approach (Networks, 68(1):4–22 2016b).
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-0995-z
Issue No: Vol. 48, No. 3 (2018)

• Data depth based support vector machines for predicting corporate
bankruptcy
• Authors: Sungdo Kim; Byeong Min Mun; Suk Joo Bae
Pages: 791 - 804
Abstract: In financial distress analysis, the diagnosis of firms at risk for bankruptcy is crucial in preparing to hedge against any financial damage the at-risk firms stand to inflict. Some pre-alarm signals that indicate a potential financial crisis exist when a firm faces a default risk. Early studies on corporate bankruptcy prediction include parametric and nonparametric approaches, such as artificial intelligence (AI), for detecting pre-alarm signals. Among nonparametric techniques, the methods involving support vector machine (SVM) have shown potential in predicting corporate bankruptcy. We propose a hybrid method that combines data depths and nonlinear SVM for the prediction of corporate bankruptcy. We employed data depth functions to condense multivariate financial data with nonlinear and non-normal characteristics into one-dimensional space. The SVM method was introduced to classify the data points on a depth versus depth plot (DD-plot). Based on data set that records failed and non-failed manufacturing firms in Korea over 10 years, the empirical results demonstrated that the proposed method offers a higher level of accuracy in corporate bankruptcy prediction than existing methods. The proposed method is expected to provide a guidance in corporate investing for investors or other interested parties.
PubDate: 2018-03-01
DOI: 10.1007/s10489-017-1011-3
Issue No: Vol. 48, No. 3 (2018)

• Skip-connection convolutional neural network for still image crowd
counting
• Authors: Luyang Wang; Baoqun Yin; Aixin Guo; Hao Ma; Jie Cao
Abstract: In recent years, crowd counting in still images has attracted many research interests due to its applications in public safety. However, it remains a challenging task for reasons of perspective and scale variations. In this paper, we propose an effective Skip-connection Convolutional Neural Network (SCNN) for crowd counting to overcome the issue of scale variations. The proposed SCNN architecture consists of several multi-scale units to extract multi-scale features. Each multi-scale unit including three convolutional layers builds connections between the input and each convolutional layer. In addition, we propose a scale-related training method to improve the accuracy and robustness of crowd counting. We evaluate our method on three crowd counting benchmarks. Experimental results verify the efficiency of the proposed method, and it achieves superior performance compared with other methods.
PubDate: 2018-02-23
DOI: 10.1007/s10489-018-1150-1

• Distance measures for connection number sets based on set pair analysis
and its applications to decision-making process
• Authors: Harish Garg; Kamal Kumar
Abstract: Connection number (CN) is one of the key features of a set pair analysis (SPA) theory to describe uncertainties in terms of three degrees, namely “identity”, “discrepancy” and “contrary”. In the present manuscript, the work has been done under environment of the intuitionistic fuzzy set, and some axioms of the distance measures based on Hamming, Euclidean, and Hausdorff metrics have been proposed whose preferences related to the attributes are made in the form of CN. Their desirable relations have also been investigated. Furthermore, based on these measures, an approach to investigating the decision-making problem has been presented. The effectiveness of the approach has been demonstrated through a case study. The comparative study as well as the advantages of the proposed measures over the existing measures has been presented.
PubDate: 2018-02-22
DOI: 10.1007/s10489-018-1152-z

• Pseudo almost periodic solutions of discrete-time neutral-type neural
networks with delays
• Authors: Fanchao Kong; Xianwen Fang
Abstract: This paper is concerned with discrete-time neutral-type neural networks with delays. The existence and uniqueness results of pseudo almost periodic solutions are established by applying the contraction mapping principal. By using some mathematical analysis techniques, we further obtain the boundness, exponential attractivity and global exponential stability of pseudo almost periodic solutions for the considered networks. Finally, a typical example and the corresponding numerical simulations have been carried out to support our analytic findings.
PubDate: 2018-02-22
DOI: 10.1007/s10489-018-1146-x

• Ant colony algorithm for satellite control resource scheduling problem
• Authors: Zhaojun Zhang; Funian Hu; Na Zhang
Abstract: With the increasing number of satellite, the satellite control resource scheduling problem (SCRSP) has been main challenge for satellite networks. SCRSP is a constrained and large scale combinatorial problem. More and more researches focus on how to allocate various measurement and control resources effectively to ensure the normal running of the satellites. However, the sparse solution space of SCRSP leads its complexity especially for traditional optimization algorithms. As the validity of ant colony optimization (ACO) has been shown in many combinatorial optimization problems, a simple ant colony optimization algorithm (SACO) to solve SCRSP is presented in this paper. Firstly, we give a general mathematical model of SCRSP. Then, a optimization model, called conflict construction graph, based on visible arc and working period is introduced to reduce workload of dispatchers. To meet the requirements of TT & C network and make the algorithm more practical, we make the parameters of SACO as constant, which include the bounds, update and initialization of pheromone. The effect of parameters on the algorithm performance is studied by experimental method based on SCRSP. Finally, the performance of SACO is compared with other novel ACO algorithms to show the feasibility and effectiveness of improvements.
PubDate: 2018-02-21
DOI: 10.1007/s10489-018-1144-z

• Nonlinear feature selection using Gaussian kernel SVM-RFE for fault
diagnosis
• Authors: Yangtao Xue; Li Zhang; Bangjun Wang; Zhao Zhang; Fanzhang Li
Abstract: Feature selection can directly ascertain causes of faults by selecting useful features for fault diagnosis, which can simplify the procedures of fault diagnosis. As an efficient feature selection method, the linear kernel support vector machine recursive feature elimination (SVM-RFE) has been successfully applied to fault diagnosis. However, fault diagnosis is not a linear issue. Thus, this paper introduces the Gaussian kernel SVM-RFE to extract nonlinear features for fault diagnosis. The key issue is the selection of the kernel parameter for the Gaussian kernel SVM-RFE. We introduce three classical and simple kernel parameter selection methods and compare them in experiments. The proposed fault diagnosis framework combines the Gaussian kernel SVM-RFE and the SVM classifier, which can improve the performance of fault diagnosis. Experimental results on the Tennessee Eastman process indicate that the proposed framework for fault diagnosis is an advanced technique.
PubDate: 2018-02-21
DOI: 10.1007/s10489-018-1140-3

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