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 2015
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
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we explore prediction intervals and how they can be used for model evaluation and discrimination in the supervised regression setting of medium sized datasets. We review three different methods for making prediction intervals and the statistics used for their evaluation. How the prediction intervals look like, how different methods behave and how the prediction intervals can be utilized for the graphical evaluation of models is illustrated with the help of simple datasets. Afterwards we propose a combined method for making prediction intervals and explore its performance with two voting schemes for combining predictions of a diverse ensemble of models. All methods are tested on a large set of datasets on which we evaluate individual methods and aggregated variants for their abilities of selecting the best predictions. The analysis of correlations between the root mean squared error and our evaluation statistic show that both stability and reliability of the results increase as the techniques get more elaborate. We confirm that the methodology is suitable for the graphical comparison of individual models and is a viable way of discriminating among model candidates.
    Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0632-z
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    ABSTRACT: Link prediction in social networks has attracted increasing attention in various fields such as sociology, anthropology, information science, and computer science. Most existing methods adopt a static graph representation to predict new links. However, these methods lose some important topological information of dynamic networks. In this work, we present a method for link prediction in dynamic networks by integrating temporal information, community structure, and node centrality in the network. Information on all of these aspects is highly beneficial in predicting potential links in social networks. Temporal information offers link occurrence behavior in the dynamic network, while community clustering shows how strong the connection between two individual nodes is, based on whether they share the same community. The centrality of a node, which measures its relative importance within a network, is highly related with future links in social networks. We predict a node’s future importance by eigenvector centrality, and use this for link prediction. Merging the typological information, including community structure and centrality, with temporal information generates a more realistic model for link prediction in dynamic networks. Experimental results on real datasets show that our method based on the integrated time model can predict future links efficiently in temporal social networks, and achieves higher quality results than traditional methods.
    Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0631-0
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    ABSTRACT: Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this paper, we describe and analyze the best-order recombination operator for permutation-based encoding. The proposed operator uses genetic information from the two parents and from the best individual obtained up to the current generation. These sources of information are integrated to determine the best order of values in the new permutation. In order to evaluate the performance of best-order crossover, we address three well-known \(\mathcal {NP}\) -hard optimization problems i.e. Travelling Salesman Problem, Vehicle Routing Problem and Resource-Constrained Project Scheduling Problem. For each of these problems, a set of benchmark instances is considered in a comparative analysis of the proposed operator with eight other crossover schemes designed for permutation representation. All crossover operators are integrated in the same standard evolutionary framework and using the same parameter setting to allow a comparison focused on the recombination process. Numerical results emphasize a good performance of the proposed crossover scheme which is able to lead to overall better quality solutions.
    Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0623-0
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    ABSTRACT: In data-mining algorithms contingency tables are frequently built from ADtrees, as ADtrees have been demonstrated to be an efficient data structure for caching sufficient statistics. This paper introduces three modifications. The first two use a one-dimensional array and a hash map for representing contingency tables, and the third uses the non-recursive approach to build contingency tables from sparse ADtrees. We implement algorithms to construct contingency tables with a two-dimensional array, a tree, a one-dimensional array, and a hash map using recursion and non-recursive approaches in Python. We empirically test these algorithms in five aspects with a large number of randomly generated datasets. We also apply the modified algorithms to Bayesian networks learning and test the performance improvements using three real-life datasets. We demonstrate experimentally that all three of these modifications improve algorithm performance. The improvements are more significant with higher arities and larger arity values.
    Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0624-z
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0676-8
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    ABSTRACT: Semi-supervised clustering techniques have been proposed in the literature to overcome the problems associated with unsupervised and supervised classification. It considers a small amount of labeled data and the whole data distribution during the process of clustering a data. In this paper, a new approach towards semi-supervised clustering is implemented using multiobjective optimization (MOO) framework. Four objective functions are optimized using the search capability of a multiobjective simulated annealing based technique, AMOSA. These objective functions are based on some unsupervised and supervised information. First three objective functions represent, respectively, the goodness of the partitioning in terms of Euclidean distance, total symmetry present in the clusters and the cluster connectedness. For the last objective function, we have considered different external cluster validity indices, including adjusted rand index, rand index, a newly developed min-max distance based MMI index, NMMI index and Minkowski Score. Results show that the proposed semi-supervised clustering technique can effectively detect the appropriate number of clusters as well as the appropriate partitioning from the data sets having either well-separated clusters of any shape or symmetrical clusters with or without overlaps. Twenty four artificial and five real-life data sets have been used in the evaluation. We develop five different versions of Semi-GenClustMOO clustering technique by varying the external cluster validity indices. Obtained partitioning results are compared with another recently developed multiobjective semi-supervised clustering technique, Mock-Semi. At the end of the paper the effectiveness of the proposed Semi-GenClustMOO clustering technique is shown in segmenting one remote sensing satellite image on the part from the city of Kolkata.
    Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0656-z
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    ABSTRACT: With increasing globalization, supplier selection has become more and more important than before. In the process of determining the best supplier, the expert judgements might be vague or incomplete due to the inherent uncertainty and imprecision on their perception. In addition to that, the sub-criteria are relevant to each other in the selection of right supplier. In this paper, a novel methodology based on fuzzy set theory and analytic network process (FEANP) is developed to address both the uncertain information involved and the interrelationships among the attributes. This paper concludes with a case study describing the implementation of this model at a real-world supplier selection scenario. At last, by in comparison with existing methods, we demonstrate the effectiveness of the proposed model.
    Applied Intelligence 05/2015;
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0666-x
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0669-7
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0670-1
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0671-0
  • Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0662-1
  • [Show abstract] [Hide abstract]
    ABSTRACT: The least square twin support vector machine (LS-TSVM) obtains two non-parallel hyperplanes by directly solving two systems of linear equations instead of two quadratic programming problems (QPPs) as in the conventional twin support vector machine (TSVM), which makes the computational speed of LS-TSVM faster than that of the TSVM. However, LS-TSVM ignores the structural information of data which may contain some vital prior domain knowledge for training a classifier. In this paper, we apply the prior structural information of data into the LS-TSVM to build a better classifier, called the structural least square twin support vector machine (S-LSTSVM). Since it incorporates the data distribution information into the model, S-LSTSVM has good generalization performance. Furthermore, S-LSTSVM costs less time by solving two systems of linear equations compared with other existing methods based on structural information. Experimental results on twelve benchmark datasets demonstrate that our S-LSTSVM performs well. Finally, we apply it into Alzheimer’s disease diagnosis to further demonstrate the advantage of our algorithm.
    Applied Intelligence 04/2015; 42(3). DOI:10.1007/s10489-014-0611-4
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    ABSTRACT: As is well known, the Greedy Ensemble Pruning (GEP) algorithm, also called the Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm, possesses relatively good performance and high speed. However, because the algorithm only explores a relatively small subspace within the whole solution space, it often produces suboptimal solutions of the ensemble pruning problem. Aiming to address this drawback, in this work, we propose a novel Randomized GEP (RandomGEP) algorithm, also called the Randomized DHCEP (RandomDHCEP) algorithm, that effectively enlarges the search space of the classical DHCEP while maintaining the same level of time complexity with the help of a randomization technique. The randomization of the classical DHCEP algorithm achieves a good tradeoff between the effectiveness and efficiency of ensemble pruning. Besides, the RandomDHCEP algorithm naturally inherits the two intrinsic advantages that a randomized algorithm usually possesses. First, in most cases, its running time or space requirements are smaller than well-behaved deterministic ensemble pruning algorithms. Second, it is easy to comprehend and implement. Experimental results on three benchmark classification datasets verify the practicality and effectiveness of the RandomGEP algorithm.
    Applied Intelligence 04/2015; 42(3). DOI:10.1007/s10489-014-0605-2