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.

Impact Factor Rankings

2015 Impact Factor Available summer 2015 1.853 0.849 0.881 0.988 0.775 0.5 0.329 0.569 0.477 0.776 0.686 0.493 0.42 0.291 0.326 0.268 0.139 0.05

Impact factor over time

Impact factor
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Year

5-year impact 1.94 5.90 0.19 0.00 0.30 Applied Intelligence website Applied intelligence (Dordrecht, Netherlands) 0924-669X 25272842 Periodical, Internet resource Journal / Magazine / Newspaper, Internet Resource

Publisher details

• 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 arXiv.org
• 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

• Article: Prediction intervals in supervised learning for model evaluation and discrimination
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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
• Article: Link prediction in dynamic social networks by integrating different types of information
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Applied Intelligence 06/2015; 42(4). DOI:10.1007/s10489-014-0631-0
• Article: Best-order crossover for permutation-based evolutionary algorithms
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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
• Article: Computing contingency tables from sparse ADtrees
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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
• Article: Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0676-8
• Article: A new semi-supervised clustering technique using multi-objective optimization
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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
• Article: A fuzzy extended analytic network process-based approach for global supplier selection
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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;
• Article: Missing data imputation by K nearest neighbours based on grey relational structure and mutual information
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0666-x
• Article: Knowledge discovery of customer purchasing intentions by plausible-frequent itemsets from uncertain data
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0669-7
• Article: A formal proof of the 𝜖-optimality of discretized pursuit algorithms
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0670-1
• Article: Privacy preservation through a greedy, distortion-based rule-hiding method
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0671-0
• Article: Transfer learning for temporal nodes Bayesian networks
Applied Intelligence 05/2015; DOI:10.1007/s10489-015-0662-1
• Article: Structural least square twin support vector machine for classification
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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
• Article: Introducing randomness into greedy ensemble pruning algorithms
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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