Applied Intelligence Journal Impact Factor & Information

Publisher: Springer Science+Business Media, Springer Verlag

Journal description

The international journal of Applied Intelligence provides a medium for exchanging scientific research and technological achievements accomplished by the international community. The focus of the work is on research in artificial intelligence and neural networks. The journal addresses issues involving solutions of real-life manufacturing defense management government and industrial problems which are too complex to be solved through conventional approaches and which require the simulation of intelligent thought processes heuristics applications of knowledge and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The emphasis of the reported work is on new and original research and technological developments rather than reports on the application of existing technology to different sets of data. Earlier work reported in these fields has been limited in application and has solved simplified structured problems which rarely occur in real-life situations. Only recently have researchers started addressing real and complex issues applicable to difficult problems. The journal welcomes such developments and functions as a catalyst in disseminating the original research and technological achievements of the international community in these areas.

Current impact factor: 1.85

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2012 Impact Factor 1.853
2011 Impact Factor 0.849
2010 Impact Factor 0.881
2009 Impact Factor 0.988
2008 Impact Factor 0.775
2007 Impact Factor 0.5
2006 Impact Factor 0.329
2005 Impact Factor 0.569
2004 Impact Factor 0.477
2003 Impact Factor 0.776
2002 Impact Factor 0.686
2001 Impact Factor 0.493
2000 Impact Factor 0.42
1999 Impact Factor 0.291
1998 Impact Factor 0.326
1997 Impact Factor 0.268
1996 Impact Factor 0.139
1995 Impact Factor 0.05

Impact factor over time

Impact factor

Additional details

5-year impact 1.94
Cited half-life 5.90
Immediacy index 0.19
Eigenfactor 0.00
Article influence 0.30
Website Applied Intelligence website
Other titles Applied intelligence (Dordrecht, Netherlands)
ISSN 0924-669X
OCLC 25272842
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification

Publications in this journal

  • Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0733-3

  • Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0730-6

  • Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0729-z
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    ABSTRACT: As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles’ local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0726-2
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    ABSTRACT: One of the challenging issues in TV recommendation applications based on implicit rating data is how to make robust recommendation for the users who irregularly watch TV programs and for the users who have their time-varying preferences on watching TV programs. To achieve the robust recommendation for such users, it is important to capture dynamic behaviors of user preference on watched TV programs over time. In this paper, we propose a topic tracking based dynamic user model (TDUM) that extends the previous multi-scale dynamic topic model (MDTM) by incorporating topic-tracking into dynamic user modeling. In the proposed TDUM, the prior of the current user preference is estimated as a weighted combination of the previously learned preferences of a TV user in multi-time spans where the optimal weight set is found in the sense of the evidence maximization of the Bayesian probability. So, the proposed TDUM supports the dynamics of public users’ preferences on TV programs for collaborative filtering based TV program recommendation and the highly ranked TV programs by similar watching taste user group (topic) can be traced with the same topic labels epoch by epoch. We also propose a rank model for TV program recommendation. In order to verify the effectiveness of the proposed TDUM and rank model, we use a real data set of the TV programs watched by 1,999 TV users for 7 months. The experiment results demonstrate that the proposed TDUM outperforms the Latent Dirichlet Allocation (LDA) model and the MDTM in log-likelihood for the topic modeling performance, and also shows its superiority compared to LDA, MDTM and Bayesian Personalized Rank Matrix Factorization (BPRMF) for TV program recommendation performance in terms of top-N precision-recall.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0720-8
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    ABSTRACT: This article addresses a new dynamic optimization problem (DOP) based on the Predator-Prey (PP) relationship in nature. Indeed, a PP system involves two adversary species where the predator’s objective is to hunt the prey while the prey’s objective is to escape from its predator. By analogy to dynamic optimization, a DOP can be seen as a predation between potential solution(s) in the search space, which represents the predator, and the moving optimum, as the prey. Therefore we define the dynamic predator-prey problem (DPP) whose objective is to keep track of the moving prey, so as to minimize the distance to the optimum. To solve this problem, a dynamic approach that continuously adapts to the changing environment is required. Accordingly, we propose a new evolutionary approach based on three main techniques for DOPs, namely: multi-population scheme, random immigrants, and memory of past solutions. This hybridization of methods aims at improving the evolutionary approaches ability to deal with DOPs and to restrain as much as possible their drawbacks. Our computational experiments show that the proposed approach achieves high performance for DPP and and competes with state of the art approaches.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0727-1
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    ABSTRACT: This paper proposes a modified harmony search (MHS) algorithm with an intersect mutation operator and cellular local search for continuous function optimization problems. Instead of focusing on the intelligent tuning of the parameters during the searching process, the MHS algorithm divides all harmonies in harmony memory into a better part and a worse part according to their fitness. The novel intersect mutation operation has been developed to generate new -harmony vectors. Furthermore, a cellular local search also has been developed in MHS, that helps to improve the optimization performance by exploring a huge search space in the early run phase to avoid premature, and exploiting a small region in the later run phase to refine the final solutions. To obtain better parameter settings for the proposed MHS algorithm, the impacts of the parameters are analyzed by an orthogonal test and a range analysis method. Finally, two sets of famous benchmark functions have been used to test and evaluate the performance of the proposed MHS algorithm. Functions in these benchmark sets have different characteristics so they can give a comprehensive evaluation on the performance of MHS. The experimental results show that the proposed algorithm not only performs better than those state-of-the-art HS variants but is also competitive with other famous meta-heuristic algorithms in terms of the solution accuracy and efficiency.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0721-7
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    ABSTRACT: The greedy randomized adaptive search procedure (GRASP) is an iterative two-phase multi-start metaheuristic procedure for a combination optimization problem, while path relinking is an intensification procedure applied to the solutions generated by GRASP. In this paper, a hybrid ensemble selection algorithm incorporating GRASP with path relinking (PRelinkGraspEnS) is proposed for credit scoring. The base learner of the proposed method is an extreme learning machine (ELM). Bootstrap aggregation (bagging) is used to produce multiple diversified ELMs, while GRASP with path relinking is the approach for ensemble selection. The advantages of the ELM are inherited by the new algorithm, including fast learning speed, good generalization performance, and easy implementation. The PRelinkGraspEnS algorithm is able to escape from local optima and realizes a multi-start search. By incorporating path relinking into GRASP and using it as the ensemble selection method for the PRelinkGraspEnS the proposed algorithm becomes a procedure with a memory and high convergence speed. Three credit datasets are used to verify the efficiency of our proposed PRelinkGraspEnS algorithm. Experimental results demonstrate that PRelinkGraspEnS achieves significantly better generalization performance than the classical directed hill climbing ensemble pruning algorithm, support vector machines, multi-layer perceptrons, and a baseline method, the best single model. The experimental results further illustrate that by decreasing the average time needed to find a good-quality subensemble for the credit scoring problem, GRASP with path relinking outperforms pure GRASP (i.e., without path relinking).
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0724-4
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    ABSTRACT: The host-seeking behavior of mosquitoes is very interesting. This paper is motivated by the following general observation on mosquito groups and their host-seeking behavior in nature: (1) Mosquitoes’ behavior has possession of the parallelism, openness, local interactivity and self-organization. (2) Mosquito groups seek host very fast. (3) The host-seeking behavior is similar to the producer-scrounger process, which assumes that group members search either for “finding” (producer) or for “joining” (scrounger) opportunities. It stimulates us to extend a mosquito system model in nature to group mosquito host-seeking model (GMHSM) and algorithm (GMHSA) for intelligent computing. In this paper, we propose GMHS approach and show how to use it. By GMHSM, the TSP is transformed into the kinematics and dynamics of mosquito groups host-seeking process. The properties of GMHSM and GMHSA, including the correctness, convergence and stability, have been discussed in this paper. The GMHS approach has many advantages in terms of multiple objective optimization, large-scale distributed parallel optimization, effectiveness of problem-solving and suitability for complex environment. Via simulations, we test the GMHS approach and compare it with other state-of-art algorithms.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0718-2
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    ABSTRACT: This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.
    Applied Intelligence 11/2015; DOI:10.1007/s10489-015-0722-6
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    ABSTRACT: Dempster’s combination rule can only be applied to independent bodies of evidence. One occurrence of dependence between two bodies of evidence is when they result from a common source. This paper proposes an improved method for combining dependent bodies of evidence which takes the significance of the common information sources into consideration. The method is based on the significance weighting operation and the “decombination” operation. A numerical example is illustrated to show the use and effectiveness of the proposed method.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0723-5
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    ABSTRACT: Signed graphs or networks are effective models for analyzing complex social systems. Community detection from signed networks has received enormous attention from diverse fields. In this paper, the signed network community detection problem is addressed from the viewpoint of evolutionary computation. A multiobjective optimization model based on link density is newly proposed for the community detection problem. A novel multiobjective particle swarm optimization algorithm is put forward to solve the proposed optimization model. Each single run of the proposed algorithm can produce a set of evenly distributed Pareto solutions each of which represents a network community structure. To check the performance of the proposed algorithm, extensive experiments on synthetic and real-world signed networks are carried out. Comparisons against several state-of-the-art approaches for signed network community detection are carried out. The experiments demonstrate that the proposed optimization model and the algorithm are promising for community detection from signed networks.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0716-4
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    ABSTRACT: Naïve Bayes learners are widely used, efficient, and effective supervised learning methods for labeled datasets in noisy environments. It has been shown that naïve Bayes learners produce reasonable performance compared with other machine learning algorithms. However, the conditional independence assumption of naïve Bayes learning imposes restrictions on the handling of real-world data. To relax the independence assumption, we propose a smooth kernel to augment weights for the likelihood estimation. We then select an attribute weighting method that uses the mutual information metric to cooperate with the proposed framework. A series of experiments are conducted on 17 UCI benchmark datasets to compare the accuracy of the proposed learner against that of other methods that employ a relaxed conditional independence assumption. The results demonstrate the effectiveness and efficiency of our proposed learning algorithm. The overall results also indicate the superiority of attribute-weighting methods over those that attempt to determine the structure of the network.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0719-1
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    ABSTRACT: This paper presents a highly scalable modular bottleneck neural network and its application to image dimensionality reduction and image transformation. The network is a three-dimensional lattice of modules that implements a complex mapping with full connectivity between two high-dimensional datasets. These datasets correspond to input and output pixel-based images of three airplanes with various spatial orientations. The modules are multilayer perceptrons trained with Levenberg-Marquardt method on GPUs. They are locally connected together in an original manner that allows the gradual elaboration of the global mapping. The lattice of modules is squeezed in its middle into a bottleneck, thereby reducing the dimensionality of images. Afterward, the bottleneck itself is stretched to enforce a specific transformation directly on the reduced data. Analysis of the neural values at the bottleneck shows that we can extract from them robust and discriminative descriptors of the airplanes. The approach compares favorably to other dimensionality reduction techniques.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0715-5
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    ABSTRACT: With the popularity of wireless networks and mobile devices, we have seen rapid growth in mobile applications and services, especially location-based services. However, most existing location-based services like Google Maps and Wikimapia rely on crowd-sourcing or business-data providers to maintain their points-of-interest (POI) databases, which are slow and insufficient. Because most updated information can be found on the Web, the insufficiency of current POI databases can be complemented by automatically extracting POIs and their descriptions from general webpages. In this study, we enhance location-based search on maps via online address extraction and associated information segmentation. Given a POI query that cannot be found on a map, we propose a method for extracting the address from search snippets of the query to exploit information from the Web. We demonstrate the application of sequence labeling to Chinese postal-address extraction and compare the performance with and without Chinese word segmentation. Meanwhile, we also present a novel algorithm for associated information segmentation by making use of a document-object model (DOM) tree structure based on the farthest distinguishable ancestor (FDA) of each address. The FDA algorithm is able to locate associated information for each Chinese address resulting in an improvement from an F-measure of 0.811 to 0.964.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0707-5
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    ABSTRACT: In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0717-3
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    ABSTRACT: We developed a variational Bayesian learning framework for the infinite generalized Dirichlet mixture model (i.e. a weighted mixture of Dirichlet process priors based on the generalized inverted Dirichlet distribution) that has proven its capability to model complex multidimensional data. We also integrate a “feature selection” approach to highlight the features that are most informative in order to construct an appropriate model in terms of clustering accuracy. Experiments on synthetic data as well as real data generated from visual scenes and handwritten digits datasets illustrate and validate the proposed approach.
    Applied Intelligence 10/2015; DOI:10.1007/s10489-015-0714-6