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Publisher: Springer-Verlag   (Total: 2353 journals)

 Applied Intelligence   [SJR: 0.777]   [H-I: 43]   [12 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X    Published by Springer-Verlag  [2353 journals]
• Online signature verification by spectrogram analysis
• Authors: Orcan Alpar; Ondrej Krejcar
Abstract: Abstract The concept of electronic signatures emerged decades ago, however they are still not prevalent due to lack of reliable infrastructure. Although the signatures are hard to perfectly imitate, it is simple with an image editing software to copy the original signature and paste on a document. On the other hand, technological developments of touchscreens may create a new era by utilizing simple interfaces which would be recording and validating the electronic signatures with biometric features. Therefore, in this paper, we propose a novel online signature analysis methodology for touchscreens that starts with signing an interface consisting of a signature silhouette. The frequency spectrum along the signing process is stealthily extracted and spectrograms are created by short-time Fourier transforms. Since the spectrograms are found as RGB images, providing valuable information about frequency vs time, grid histograms are formed by quantization for the real signature sample. Given the discrimination purposes, a fuzzified surface is designed for computing closeness of grid histograms.
PubDate: 2017-08-01
DOI: 10.1007/s10489-017-1009-x

• An EM based probabilistic two-dimensional CCA with application to face
recognition
• Authors: Mehran Safayani; Seyed Hashem Ahmadi; Homayun Afrabandpey; Abdolreza Mirzaei
Abstract: 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: 2017-08-01
DOI: 10.1007/s10489-017-1012-2

• Two time-efficient gibbs sampling inference algorithms for biterm topic
model
• Authors: Xiaotang Zhou; Jihong Ouyang; Ximing Li
Abstract: 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: 2017-07-31
DOI: 10.1007/s10489-017-1004-2

• Challenging state-of-the-art move ordering with Adaptive Data Structures
• Authors: Spencer Polk; B. John Oommen
PubDate: 2017-07-28
DOI: 10.1007/s10489-017-1006-0

• Total utility of Z-number
• Authors: Bingyi Kang; Yong Deng; Rehan Sadiq
Abstract: 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: 2017-07-26
DOI: 10.1007/s10489-017-1001-5

• Chaotic antlion algorithm for parameter optimization of support vector
machine
• Authors: Alaa Tharwat; Aboul Ella Hassanien
Abstract: 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: 2017-07-26
DOI: 10.1007/s10489-017-0994-0

• Bio-inspired metaheuristics: evolving and prioritizing software test data
• Authors: Mukesh Mann; Pradeep Tomar; Om Prakash Sangwan
Abstract: 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: 2017-07-26
DOI: 10.1007/s10489-017-1003-3

• Multi-objective service composition model based on cost-effective
optimization
• Authors: Ying Huo; Peng Qiu; Jiyou Zhai; Dajuan Fan; Huanfeng Peng
Abstract: 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: 2017-07-22
DOI: 10.1007/s10489-017-0996-y

• A temporal modal defeasible logic for formalizing social commitments in
dialogue and argumentation models
Abstract: 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: 2017-07-21
DOI: 10.1007/s10489-017-0983-3

• Answer set programming for non-stationary Markov decision processes
• Authors: Leonardo A. Ferreira; Reinaldo A. C. Bianchi; Paulo E. Santos; Ramon Lopez de Mantaras
Abstract: Abstract Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combines Markov Decision Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming (ASP) in a method we call ASP(RL). In this method, Answer Set Programming is used to find the possible trajectories of an MDP, from where Reinforcement Learning is applied to learn the optimal policy of the problem. Results show that ASP(RL) is capable of efficiently finding the optimal solution of an MDP representing non-stationary domains.
PubDate: 2017-07-21
DOI: 10.1007/s10489-017-0988-y

• Ant colony algorithm for automotive safety integrity level allocation
• Authors: Youcef Gheraibia; Khaoula Djafri; Habiba Krimou
Abstract: 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: 2017-07-21
DOI: 10.1007/s10489-017-1000-6

• 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
Abstract: 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: 2017-07-21
DOI: 10.1007/s10489-017-0987-z

• Feature clustering based support vector machine recursive feature
elimination for gene selection
• Authors: Xiaojuan Huang; Li Zhang; Bangjun Wang; Fanzhang Li; Zhao Zhang
Abstract: 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: 2017-07-21
DOI: 10.1007/s10489-017-0992-2

• Multiobjective differential evolution using homeostasis based mutation for
application in software cost estimation
• Authors: Shailendra Pratap Singh; Anoj Kumar
Abstract: 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: 2017-07-21
DOI: 10.1007/s10489-017-0980-6

• Indirect adaptive type-2 bionic fuzzy control
• Authors: Faxiang Zhang; Jing Hua; Yimin Li
Abstract: 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: 2017-07-20
DOI: 10.1007/s10489-017-0991-3

• A social recommender system using item asymmetric correlation
Abstract: 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: 2017-07-20
DOI: 10.1007/s10489-017-0973-5

• δ -equality of intuitionistic fuzzy sets: a new proximity measure and
applications in medical diagnosis
• Authors: Roan Thi Ngan; Mumtaz Ali; Le Hoang Son
Abstract: Abstract Intuitionistic fuzzy set is capable of handling uncertainty with counterpart falsities which exist in nature. Proximity measure is a convenient way to demonstrate impractical significance of values of memberships in the intuitionistic fuzzy set. However, the related works of Pappis (Fuzzy Sets Syst 39(1):111–115, 1991), Hong and Hwang (Fuzzy Sets Syst 66(3):383–386, 1994), Virant (2000) and Cai (IEEE Trans Fuzzy Syst 9(5):738–750, 2001) did not model the measure in the context of the intuitionistic fuzzy set but in the Zadeh’s fuzzy set instead. In this paper, we examine this problem and propose new notions of δ-equalities for the intuitionistic fuzzy set and δ-equalities for intuitionistic fuzzy relations. Two fuzzy sets are said to be δ-equal if they are equal to an extent of δ. The applications of δ-equalities are important to fuzzy statistics and fuzzy reasoning. Several characteristics of δ-equalities that were not discussed in the previous works are also investigated. We apply the δ-equalities to the application of medical diagnosis to investigate a patient’s diseases from symptoms. The idea is using δ-equalities for intuitionistic fuzzy relations to find groups of intuitionistic fuzzified set with certain equality or similar degrees then combining them. Numerical examples are given to illustrate validity of the proposed algorithm. Further, we conduct experiments on real medical datasets to check the efficiency and applicability on real-world problems. The results obtained are also better in comparison with 10 existing diagnosis methods namely De et al. (Fuzzy Sets Syst 117:209–213, 2001), Samuel and Balamurugan (Appl Math Sci 6(35):1741–1746, 2012), Szmidt and Kacprzyk (2004), Zhang et al. (Procedia Eng 29:4336–4342, 2012), Hung and Yang (Pattern Recogn Lett 25:1603–1611, 2004), Wang and Xin (Pattern Recogn Lett 26:2063–2069, 2005), Vlachos and Sergiadis (Pattern Recogn Lett 28(2):197–206, 2007), Zhang and Jiang (Inf Sci 178(6):4184–4191, 2008), Maheshwari and Srivastava (J Appl Anal Comput 6(3):772–789, 2016) and Support Vector Machine (SVM).
PubDate: 2017-07-19
DOI: 10.1007/s10489-017-0986-0

• Hybrid cost and time path planning for multiple autonomous guided vehicles
• Authors: Hamed Fazlollahtabar; Samaneh Hassanli
Abstract: Abstract In this paper, simultaneous scheduling and routing problem for autonomous guided vehicles (AGVs) is investigated. At the beginning of the planning horizon list of orders is processed in the manufacturing system. The produced or semi-produced products are carried among stations using AGVs according to the process plan and the earliest delivery time rule. Thus, a network of stations and AGV paths is configured. The guide path is bi-direction and AGVs can only stop at the end of a node. Two kinds of collisions exist namely: AGVs move directly to a same node and AGVs are on a same path. Delay is defined as an order is carried after the earliest delivery time. Therefore, the problem is defined to consider some AGVs and material handling orders available and assign orders to AGVs so that collision free paths as cost attribute and minimal waiting time as time attribute, are obtained. Solving this problem leads to determine: the number of required AGVs for orders fulfillment assign orders to AGVs schedule delivery and material handling and route different AGVs. The problem is formulated as a network mathematical model and optimized using a modified network simplex algorithm. The proposed mathematical formulation is first adapted to a minimum cost flow (MCF) model and then optimized using a modified network simplex algorithm (NSA). Numerical illustrations verify and validate the proposed modelling and optimization. Also, comparative studies guarantee superiority of the proposed MCF-NSA solution approach.
PubDate: 2017-07-19
DOI: 10.1007/s10489-017-0997-x

• An evolutionary non-linear ranking algorithm for ranking scientific
collaborations
• Authors: Fahimeh Ghasemian; Kamran Zamanifar; Nasser Ghasem-Aghaee
Abstract: Abstract The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.
PubDate: 2017-07-17
DOI: 10.1007/s10489-017-0990-4

• Improved monarch butterfly optimization for unconstrained global search
and neural network training
• Authors: Hossam Faris; Ibrahim Aljarah; Seyedali Mirjalili
Abstract: Abstract This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems.
PubDate: 2017-07-17
DOI: 10.1007/s10489-017-0967-3

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