Applied Intelligence (APPL INTELL)

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

2016 Impact Factor Available summer 2017
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

  • No preview · Article · Feb 2016 · Applied Intelligence
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    ABSTRACT: Techniques for mining rare patterns have been researched in the association rule mining area because traditional frequent pattern mining methods have to generate a large amount of unnecessary patterns in order to find rare patterns from large databases. One such technique, the multiple minimum support threshold framework was devised to extract rare patterns by using a different minimum item support threshold for each item in a database. Nevertheless, this framework cannot sufficiently reflect environments of the real world. The reason is that it does not consider weights of items, such as market prices of products and fatality rates of diseases, in its mining process. Therefore, an algorithm has been proposed to mine rare patterns with utilities exceeding a user-specified minimum utility by considering rarity and utility information of items. However, since this algorithm employs the concept of traditional high utility pattern mining, patterns’ lengths are not considered for determining utilities of the patterns. If the length of a pattern is sufficiently long, the pattern is more likely to have an enough utility to become a high utility pattern regardless of item utilities within the pattern. Therefore, the algorithm cannot guarantee that all items in a mined pattern have high utilities. In this paper, we propose a novel algorithm that effectively reduces such dependency of patterns on their lengths by considering their lengths in the mining process in order to mine more meaningful rare patterns compared to patterns mined by previous algorithms. Experimental results demonstrate that our algorithm extracts a lesser number of more meaningful patterns and consumes less computational resources compared to state-of-the-art algorithms.
    No preview · Article · Feb 2016 · Applied Intelligence

  • No preview · Article · Feb 2016 · Applied Intelligence

  • No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: Reinforcement learning (RL) is an area of machine learning that is concerned with how an agent learns to make decisions sequentially in order to optimize a particular performance measure. For achieving such a goal, the agent has to choose either 1) exploiting previously known knowledge that might end up at local optimality or 2) exploring to gather new knowledge that expects to improve the current performance. Among other RL algorithms, Bayesian model-based RL (BRL) is well-known to be able to trade-off between exploitation and exploration optimally via belief planning, i.e. partially observable Markov decision process (POMDP). However, solving that POMDP often suffers from curse of dimensionality and curse of history. In this paper, we make two major contributions which are: 1) an integration framework of temporal abstraction into BRL that eventually results in a hierarchical POMDP formulation, which can be solved online using a hierarchical sample-based planning solver; 2) a subgoal discovery method for hierarchical BRL that automatically discovers useful macro actions to accelerate learning. In the experiment section, we demonstrate that the proposed approach can scale up to much larger problems. On the other hand, the agent is able to discover useful subgoals for speeding up Bayesian reinforcement learning.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: Cybersecurity is a growing concern in today’s society. Security policies have been developed to ensure that data and assets remain protected for legitimate users, but there must be a mechanism to verify that these policies can be enforced. This paper addresses the verification problem of security policies in role-based access control of enterprise software. Most existing approaches employ traditional logic or procedural programming that tends to involve complex expressions or search with backtrack. These can be time-consuming, and hard to understand, and update, especially for large-scale security verification problems. Declarative programming paradigms such as “Answer Set” programming have been widely used to alleviate these issues by ways of elegant and flexible modeling for complex search problems. However, solving problems using these paradigms can be challenging due to the nature and limitation of the declarative problem solver. This paper presents an approach to automated security policy verification using Answer Set programming. In particular, we investigate how the separation of duty security policy in role-based access control can be verified. Our contribution is a modeling approach that maps this verification problem into a graph-coloring problem to facilitate the use of generate-and-test in a declarative problem-solving paradigm. The paper describes a representation model and rules that drive the Answer Set Solver and illustrates the proposed approach to securing web application software to assist the hiring process in a company.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: Traditional activation functions such as hyperbolic tangent and logistic sigmoid have seen frequent use historically in artificial neural networks. However, nowadays, in practice, they have fallen out of favor, undoubtedly due to the gap in performance observed in recognition and classification tasks when compared to their well-known counterparts such as rectified linear or maxout. In this paper, we introduce a simple, new type of activation function for multilayer feed-forward architectures. Unlike other approaches where new activation functions have been designed by discarding many of the mainstays of traditional activation function design, our proposed function relies on them and therefore shares most of the properties found in traditional activation functions. Nevertheless, our activation function differs from traditional activation functions on two major points: its asymptote and global extremum. Defining a function which enjoys the property of having a global maximum and minimum, turned out to be critical during our design-process since we believe it is one of the main reasons behind the gap observed in performance between traditional activation functions and their recently introduced counterparts. We evaluate the effectiveness of the proposed activation function on four commonly used datasets, namely, MNIST, CIFAR-10, CIFAR-100, and the Pang and Lee’s movie review. Experimental results demonstrate that the proposed function can effectively be applied across various datasets where our accuracy, given the same network topology, is competitive with the state-of-the-art. In particular, the proposed activation function outperforms the state-of-the-art methods on the MNIST dataset.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes one of the most emerging research topic in the field of data mining due to its wide range of applications in online e-commerce data analysis, identifying interesting patterns in biomedical data and for cross marketing solutions in retail business. It aims to discover the itemsets with high utilities efficiently by considering item quantities in a transaction and profit values of each item. However, it produces a tremendous number of HUIs, which imposes further burden in analysis of the extracted patterns and also degrades the performance of mining methods. Mining the set of closed + high utility itemsets (CHUIs) solves this issue as it is a loss-less and condensed representation of all HUIs. In this paper, we aim to present a new algorithm for finding CHUIs from a transactional database, called the CHUM (Closed + High Utility itemset Miner), which is scalable and efficient. The proposed mining algorithm adopts a tricky aimed vertical representation of the database in order to speed up the execution time in generating itemset closures and compute their utility information without accessing the database. The proposed method makes use of the item co-occurrences strategy in order to further reduce the number of intersections needed to be performed. Several experiments are conducted on various sparse and dense datasets and the simulation results clearly show the scalability and superior performance of our algorithm as compared to those for the existing state-of-the-art CHUD (Closed + High Utility itemset Discovery) algorithm.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: The bag-of-words representation of text data is very popular for document classification. In the recent literature, it has been shown that properly weighting the term feature vector can improve the classification performance significantly beyond the original term-frequency based features. In this paper we demystify the success of the recent term-weighting strategies as well as provide possibly more reasonable modifications. We then propose novel term-weighting schemes that can be induced from the well-known document probabilistic models such as the Naive Bayes and the multinomial term model. Interestingly, some of the intuition-based term-weighting schemes coincide exactly with the proposed derivations. Our term-weighting schemes are tested on large-scale text classification problems/datasets where we demonstrate improved prediction performance over existing approaches.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: When multiple mobile robots cooperatively explore an unknown environment, the advantages of robustness and redundancy are guaranteed. However, available traditional economy approaches for coordination of multi-robot systems (MRS) exploration lack efficient target selection strategy under a few of situations and rely on a perfect communication. In order to overcome the shortages and endow each robot autonomy, a novel coordinated algorithm based on supervisory control of discrete event systems and a variation of the market approach is proposed in this paper. Two kinds of utility and the corresponding calculation schemes which take into account of cooperation between robots and covering the environment in a minimal time, are defined. Different moving target of each robot is determined by maximizing the corresponding utility at the lower level of the proposed hierarchical coordinated architecture. Selection of a moving target assignment strategy, dealing with communication failure, and collision avoidance are modeled as behaviors of each robot at the upper level. The proposed approach distinctly speeds up exploration process and reduces the communication requirement. The validity of our algorithm is verified by computer simulations.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: Salient object detection aims to automatically localize a foreground object with respect to its background in an image. It plays a crucial role in a wide range of computer vision and multimedia applications. In this work, we propose an improved salient object detection method based on biogeography-based optimization, a relatively new bio-inspired metaheuristic algorithm that searches for the global optimum using a migration model. Our approach consists of two steps. In the first step, a set of local (multi-scale contrast), regional (center-surround histogram), and global (color spatial distribution) salient feature maps are extracted and normalized. In the second step, an optimal weight vector for combining these feature maps into one saliency map is determined using biogeography-based optimization and improved variants of this algorithm. As a result, a salient objects were identified and labeled as distinct from the image background. We implemented our method using three biogeography-based optimization variants, and compared our results for three popular databases against two other state-of-the-art approaches. The experimental results demonstrate that our method exhibits refined and consistent detection of salient objects.
    No preview · Article · Jan 2016 · Applied Intelligence
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    ABSTRACT: Potential security threats pose a significant challenge to evaluating the trustworthiness of complex links among users in a social network. Traditional trust computation methods typically consider user comments or interaction, thereby reflecting the trustworthiness between users according to their past experiences. However, the tie strength, which reflects the closeness of user relationships, is also a potential factor for estimating the trustworthiness of links among users. To incorporate this indicator, we propose a trust evaluation scheme for complex links comprising two aspects: the reliability and strength of links among uses. Our main contributions are (1) a trust calculation method, including direct trust for directly linked users and indirect trust for indirectly linked users, which is established based on the comment factor, forwarding factor, and approving factor; (2) a link strength evaluation method to determine the trustworthiness of direct and indirect links between users considering comment stability, mutual trust, interaction frequency, and common neighbours and community similarity, and (3) a link trust evaluation algorithm based on the link trust matrix synthesizing the reliability and strength of links. The experimental results and analysis show that our proposed scheme is feasible and effective in improving the performance of trust evaluation in a social network.
    No preview · Article · Jan 2016 · Applied Intelligence

  • No preview · Article · Dec 2015 · Applied Intelligence

  • No preview · Article · Dec 2015 · Applied Intelligence
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    ABSTRACT: This paper analyzes various earlier approaches for selection of hidden neuron numbers in artificial neural networks and proposes a novel criterion to select the hidden neuron numbers in improved back propagation networks for wind speed forecasting application. Either over fitting or under fitting problem is caused because of the random selection of hidden neuron numbers in artificial neural networks. This paper presents the solution of either over fitting or under fitting problems. In order to select the hidden neuron numbers, 151 different criteria are tested by means of the statistical errors. The simulation is performed on collected real-time wind data and simulation results prove that proposed approach reduces the error to a minimal value and enhances forecasting accuracy The perfect building of improved back propagation networks employing the fixation criterion is substantiated based on the convergence theorem. Comparative analyses performed prove the selection of hidden neuron numbers in improved back propagation networks is highly effective in nature.
    No preview · Article · Dec 2015 · Applied Intelligence