Lecture Notes in Computer Science Journal Impact Factor & Information

Publisher: Springer Verlag

Journal description

The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research and teaching - quickly, informally, and at a high level. The cornerstone of LNCS's editorial policy is its unwavering commitment to report the latest results from all areas of computer science and information technology research, development, and education. LNCS has always enjoyed close cooperation with the computer science R & D community, with numerous renowned academics, and with prestigious institutes and learned societies. Our mission is to serve this community by providing a most valuable publication service.

Current impact factor: 0.51

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2005 Impact Factor 0.302
2004 Impact Factor 0.251
2002 Impact Factor 0.515
2001 Impact Factor 0.415
2000 Impact Factor 0.253
1999 Impact Factor 0.53

Impact factor over time

Impact factor
Year

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Lecture Notes in Computer Science website
Other titles Lecture notes in computer science, Lecture notes in artificial intelligence, Lecture notes in computer science
ISSN 0302-9743
OCLC 3719235
Material type Series, 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 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

  • Lecture Notes in Computer Science 08/2015;
  • Salomao David Cumbula, Lorenzo Cantoni
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes a community co-design approach performed in rural Mozambique. It discusses the experiences and experiments performed in a community multimedia center towards creating services with inherent values for daily community activities. The design approach pursues a holistic interpretation of community needs, and discusses emerging, new and creative applications for future community binding.
    Lecture Notes in Computer Science 07/2015; 9186(Design, User Experience, and Usability: Design Discourse):149-156. DOI:10.1007/978-3-319-20886-2_15
  • [Show abstract] [Hide abstract]
    ABSTRACT: The growing need to support financially the processes of urban regeneration of city centers clashes with the limited availability of public resources. Administrations are therefore forced to evaluate the priority areas of intervention, on the one hand trying to pursue goals of social equity, other actions to promote efficient financial plan. Consequently the reference institutional policy of intervention is based on regulatory frameworks that require a closer integration of programming needs of the allocation of resources, and social needs. The chapter shows an example of conciliation among the seek for efficiency and for social equality in choosing priority of intervention in the urban make up of historic centers.
    Lecture Notes in Computer Science 07/2015; 9157(III):317-329. DOI:10.1007/978-3-319-21470-2_22
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    ABSTRACT: In the current economic situation, characterized by a high uncertainty in the appraisal of property values, the need of “slender” models able to operate even on limited data, to automatically capture the causal relations between explanatory variables and selling prices and to predict property values in the short term, is increasingly widespread. In addition to Artificial Neural Networks (ANN), that satisfy these prerogatives, recently, in some fields of Civil Engineering an hybrid data-driven technique has been implemented, called Evolutionary Polynomial Regression (EPR), that combines the effectiveness of Genetic Programming with the advantage of classical numerical regression. In the present paper, ANN methods and the EPR procedure are compared for the construction of estimation models of real estate market values. With reference to a sample of residential apartments recently sold in a district of the city of Bari (Italy), two estimation models of market value are implemented, one based on ANN and another using EPR, in order to test the respective performance. The analysis has highlighted the preferability of the EPR model in terms of statistical accuracy, empirical verification of results obtained and reduction of the complexity of the mathematical expression.
    Lecture Notes in Computer Science 07/2015; 9157(III):194-215. DOI:10.1007/978-3-319-21470-2_14
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    ABSTRACT: Correlation filters have been extensively used in face recognition but surprisingly underused in head pose classification. In this paper, we present a correlation filter that ensures the tradeoff between three criteria: peak distinctiveness, discrimination power and noise robustness. Such a filter is derived through a variational formulation of these three criteria. The closed form obtained intrinsically considers multiclass information and preserves the bidimensional structure of the image. The filter proposed is combined with a face image descriptor in order to deal with pose classification problem. It is shown that our approach improves pose classification accuracy, especially for non-frontal poses, when compared with other methods.
    Lecture Notes in Computer Science 07/2015; 9164:203-209. DOI:10.1007/978-3-319-20801-5_22
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    ABSTRACT: This paper studies the issue of scalability of data decomposed based algorithms that are intended for attribute reduction. Two approaches that decompose a decision table and use the relative discernibility matrix method to compute all reducts are investigated. The experiments results reported in this paper show that application of the approaches makes it possible to gain a better scalability compared with the standard algorithm based on the relative discernibility matrix method.
    Lecture Notes in Computer Science 06/2015; 9124:387-396. DOI:10.1007/978-3-319-19941-2_37
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    ABSTRACT: This paper presents results of assimilation of TRMM precipitation into a hydrological model of Los Almendros River watershed, assessed by the comparison of simulated stream flow values to observed stream flow data. Los Almendros River is a tributary to the Clarillo River, located in Reserva Nacional Rio Clarillo (National Reserve Clarillo River), Chile, South America. Los Almendros basin, covering approximately 4.9 km2, is a typical Andean watershed with an average slope of 46.3. It drains an average of 18.5 L/s from a catchment area covered predominantly by grasslands (91%), while forests and savanna land cover types are less predominant (3% and 6% respectively). The hydrological model of Los Almendros watershed was developed using the Hydrological Simulation Program Fortran (HSPF). Results showed that raw TRMM precipitation time-series overestimated disaggregated precipitation values for the whole period of analysis. TRMM data required time-averaging for the monthly and annual values to be in the same range as those of disaggregated data. Time-averaging produced daily precipitation time-series consistent to disaggregated data (R2=0.85). The use of TRMM-enriched precipitation time-series for hydrological modeling of stream flow at Los Almendros watershed outlet, slightly improved a previous simulation in which only disaggregated precipitation dataset was used. When comparing simulated and observed data, the statistical fit coefficient improved from R2=0.64 (corresponding to only disaggregated precipitation data introduced into the hydrological model) to R2=0.68 (corresponding to TRMM-enriched precipitation data).
    Lecture Notes in Computer Science 06/2015; 9157:468-476. DOI:10.1007/978-3-319-21470-2_34
  • Lecture Notes in Computer Science 06/2015; DOI:10.1007/978-3-319-21407-8_34
  • Lecture Notes in Computer Science 06/2015; DOI:10.1007/978-3-319-21407-8_30