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
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  • 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: Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore propose a system that could contribute to lowering both the costs and the work of an imaging diagnosis centre of breast cancer and in addition to increase the trust level in that diagnosis. We present a multi-objective evolutionary approach based on Multi-Expression Programming—a linear Genetic Programming method—that could classify a mammogram starting from a raw image of the breast. The processed images are represented through Histogram of Oriented Gradients and Kernel Descriptors since these image features have been reported as being very efficient in the image recognition scientific community and they have not been applied to mammograms before. Numerical experiments are performed on freely available datasets consisting of normal and abnormal film-based and digital mammograms and show the efficiency of the proposed decision support system.
    Applied Intelligence 10/2015; 43(3). DOI:10.1007/s10489-015-0668-8
  • Applied Intelligence 09/2015; DOI:10.1007/s10489-015-0713-7
  • Applied Intelligence 09/2015; DOI:10.1007/s10489-015-0709-3
  • [Show abstract] [Hide abstract]
    ABSTRACT: Geo-demographic analysis is an essential part of a geographical information system (GIS) for predicting people’s behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-demographic analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-demographic analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime data set. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.
    Applied Intelligence 09/2015; 43(2):1-22. DOI:10.1007/s10489-015-0705-7
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    ABSTRACT: Twin support vector machine (TWSVM) is regarded as a milestone in the development of powerful SVMs. However, there are some inconsistencies with TWSVM that can lead to many reasonable modifications with different outputs. In order to obtain better performance, we propose a novel combined outputs framework that combines rational outputs. Based on this framework, an optimal output model, called the linearly combined twin bounded support vector machine (LCTBSVM), is presented. Our LCTBSVM is based on the outputs of several TWSVMs, and produces the optimal output by solving an optimization problem. Furthermore, two heuristic algorithms are suggested in order to solve the optimization problem. Our comprehensive experiments show the superior generalization performance of our LCTBSVM compared with SVM, PSVM, GEPSVM, and some current TWSVMs, thus confirming the value of our theoretical analysis approach.
    Applied Intelligence 09/2015; 43(2). DOI:10.1007/s10489-015-0655-0
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    ABSTRACT: Credit scoring, which is also called credit risk assessment has attracted the attention of many financial institutions and much research has been carried out. In this work, a new Extreme Learning Machines’ (ELMs) Ensemble Selection algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP), referred to as ELMsGraspEnS, is proposed for credit risk assessment of enterprises. On the one hand, the ELM is used as the base learner for ELMsGraspEnS owing to its significant advantages including an extremely fast learning speed, good generalization performance, and avoidance of issues like local minima and overfitting. On the other hand, to ameliorate the local optima problem faced by classical greedy ensemble selection methods, we incorporated GRASP, a meta-heuristic multi-start algorithm for combinatorial optimization problems, into the solution of ensemble selection, and proposed an ensemble selection algorithm based on GRASP (GraspEnS) in our previous work. The GraspEnS algorithm has the following three advantages. (1) By incorporating a random factor, a solution is often able to escape local optima. (2) GraspEnS realizes a multi-start search to some degree. (3) A better performing subensemble can usually be found with GraspEnS. Moreover, not much research on applying ensemble selection approaches to credit scoring has been reported in the literature. In this paper, we integrate the ELM with GraspEnS, and propose a novel ensemble selection algorithm based on GRASP (ELMsGraspEnS). ELMsGraspEnS naturally inherits the inherent advantages of both the ELM and GraspEnS, effectively combining their advantages. The experimental results of applying ELMsGraspEnS to three benchmark real world credit datasets show that in most cases ELMsGraspEnS significantly improves the performance of credit risk assessment compared with several state-of-the-art algorithms. Thus, it can be concluded that ELMsGraspEnS simultaneously exhibits relatively high efficiency and effectiveness.
    Applied Intelligence 09/2015; 43(2). DOI:10.1007/s10489-015-0653-2
  • [Show abstract] [Hide abstract]
    ABSTRACT: Detection and analysis of activities of daily living (ADLs) are important in activity tracking, security monitoring, and life support in elderly healthcare. Recently, many research projects have employed wearable devices to detect and analyze ADLs. However, most wearable devices obstruct natural movement of the body, and the analysis of activities lacks adequate consideration of various real attributes. To tackle these issues, we proposed a two-fold solution. First, regarding unobtrusive detection of ADLs, only one small device is worn on a finger to sense and collect activity information, and identifiable features are extracted from the finger-related signals to identify various activities. Second, to reflect realistic life situations, a weighted sequence alignment approach is proposed to analyze an activity sequence detected by the device, as well as attributes of each activity in the sequence. The system is validated using 10 daily activities and 3 activity sequences. Results show 96.8 % accuracy in recognizing activities and the effectiveness of sequence analysis.
    Applied Intelligence 09/2015; 43(2). DOI:10.1007/s10489-015-0649-y
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    ABSTRACT: This work investigates a possibility degree-based micro immune optimization approach to seek the optimal solution of nonlinear interval number programming with constraints. Such approach is designed under the guideline of the theoretical results acquired in the current work, relying upon interval arithmetic rules, interval order relation and immune theory. It involves in two phases of optimization. The first phase, based on a new possibility degree approach, assumes searching efficient solutions of natural interval extension optimization. This executes five modules - constraint bound handling, population division, dynamic proliferation, mutation and selection, with the help of a varying threshold of interval bound. The second phase collects the optimal solution(s) from these efficient solutions after optimizing the bounds of their objective intervals, in terms of the theoretical results. The numerical experiments illustrated that such approach with high efficiency performs well over one recent nested genetic algorithm and is of potential use for complex interval number programming.
    Applied Intelligence 09/2015; 43(2). DOI:10.1007/s10489-014-0639-5
  • Applied Intelligence 08/2015; DOI:10.1007/s10489-015-0708-4
  • Applied Intelligence 08/2015; DOI:10.1007/s10489-015-0702-x
  • Jerry Chun-Wei Lin · Wensheng Gan · Philippe Fournier-Viger · Tzung-Pei Hong · Vincent S. Tseng
    Applied Intelligence 08/2015; DOI:10.1007/s10489-015-0703-9
  • Wenbin Hu · Huan Wang · Liping Yan · Bo Du
    Applied Intelligence 08/2015; DOI:10.1007/s10489-015-0701-y
  • Teemu Tossavainen · Shun Shiramatsu · Tadachika Ozono · Toramatsu Shintani
    Applied Intelligence 08/2015; DOI:10.1007/s10489-015-0704-8