Lecture Notes in Computer Science (Lect Notes Comput Sci)

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

2016 Impact Factor Available summer 2017
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

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

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a first prototype of a teleoperated telepresence system that uses a range of devices aimed at operating a mobile platform situated in a remote location while offering the user the sensation of being at that remote location. The objective of this first stage of the project is to achieve a sensation of telepresence by simply giving the user the freedom of movement of the platform and the freedom of movement of the camera system. Keywords: Teleoperation � Telepresence � Robotics � Pioneer 3-AT
    No preview · Article · Jan 2016 · Lecture Notes in Computer Science

  • No preview · Conference Paper · Jan 2016
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    ABSTRACT: Feature subset selection is a key problem in the data-mining classification task that helps to obtain more compact and understandable models without degrading their performance. This paper deals with the problem of supervised wrapper based feature subset selection in data sets with a very large number of attributes and a low sample size. In this case, standard wrapper algorithms cannot be applied because of their complexity. In this work we propose a new hybrid -filter wrapper- approach based on instance learning with the main goal of accelerating the feature subset selection process by reducing the number of wrapper evaluations. In our hybrid feature selection method, named Hybrid Instance Based Sequential Backward Search (HIB-SBS), instance learning is used to weight features and generate candidate feature subsets, then SBS and K-nearest neighbours (KNN) compose an evaluation system of wrappers. Our method is experimentally tested and compared with state-of-the-art algorithms over four high-dimensional low sample size datasets. The results show an impressive reduction in the execution time compared to the wrapper approach and that our proposal outperforms other methods in terms of accuracy and cardinality of the selected subset.
    No preview · Article · Jan 2016 · Lecture Notes in Computer Science

  • No preview · Article · Jan 2016 · Lecture Notes in Computer Science
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    ABSTRACT: Extended patient waiting times for medical care in outpatient internal medicine has a direct impact on patient dissatisfaction. This issue has become increasingly relevant in Colombia where patient waiting times tend to be longer. In this context, a methodology based on value stream mapping (VSM) and collaborative sceneries has been created to examine different improving alternatives focused on the design of integrated service networks in outpatient internal medicine with the participation of two hospitals with mixed-patient type environment. First, an individual diagnosis for each hospital is made through VSM to identify non-value activities in the value chain and design improvement strategies for each process. Second, a strategic platform of the network is set. Third, communication and service protocols of the network are defined. Then, a simulation model is designed and validated to conduct experiments on the structure of the network. Finally, payment and risk tables are determined and key performance indexes of the network are established. The results prove the validity of the proposed approach upon reducing 75 % of the lead time in this process creating a positive impact on population’s health under satisfactory and equitable financial benefits for the participant hospitals.
    No preview · Article · Dec 2015 · Lecture Notes in Computer Science
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    ABSTRACT: In Artificial Intelligence, a large number of problems (i.e. distributed resource management, distributed air traffic management, Distributed Sensor Network [1]) can be modeled and solved as Distributed Constraint Satisfaction Problems (DisCSPs). As many real world problems change continuously and incessantly over time, some methods have been developed (e.g. DynABT), for solving problems which exhibit this dynamic behavior. Meanwhile, there was no available framework that helped users to develope intelligent multi-agent systems based on Dynamic and Distributed Constraints Reasoning (DCR) techniques. In this paper, we propose a new platform, called JChoc, supporting the dynamic aspect for DisCSPs. JChoc is an easy to use platform, based on an elegant Multi-agent communication sub-platform (i.e. JADE). It deals with agents with local complex problems and allows a realistic use of agents on a real distributed and dynamic framework. A real distributed problem is addressed to illustrate how the platform can be used to solve dynamically changing problems. However, the experimental results show the defectiveness of our platform.
    No preview · Article · Dec 2015 · Lecture Notes in Computer Science
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    ABSTRACT: Hyperspectral sensing, due to its intrinsic ability to capture the spectral responses of depicted materials, provides unique capabilities towards object detection and identification. In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. By the effective exploitation of a Convolutional Neural Network we encode pixels’ spectral and spatial information and employ a Multi-Layer Perceptron to conduct the classification task. Experimental results and the performed quantitative validation on widely used hyperspectral datasets demonstrating the great potentials of the developed approach towards accurate and automated manmade object detection.
    No preview · Article · Dec 2015 · Lecture Notes in Computer Science