Computational Science and Techniques
http://journals.ku.lt/index.php/CST
Journal “Computational Science and Techniques” is a semiannual academic refereed journal published in english with the focus on interdisciplinary field connecting computer science, mathematical modeling, and life sciences. The journal is interested in papers about various computational applications that are connected with modeling, algorithms, simulations or software developed to solve science or engineering problem or computer and information science that develops and optimizes the advanced system components, which are used for example in decision support systems or are applied in ecosystems management and etc. We encourage young scientists to submit for Computational Science and Techniques. Papers of high quality in interdisciplinary field are welcome.Klaipeda Universityen-USComputational Science and Techniques2029-9966<span>Authors who publish with this journal agree to the following terms:</span><br /><ol type="a"><br /><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li><br /><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><br /><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li><br /></ol><br />Control point selection for dimensionality reduction by radial basis function
http://journals.ku.lt/index.php/CST/article/view/1095
<p>This research deals with dimensionality reduction technique which is based on radial basis function (RBF) theory. The technique uses RBF for mapping multidimensional data points into a low-dimensional space by interpolating the previously calculated position of so-called control points. This paper analyses various ways of selection of control points (<em>regularized</em> <em>orthogonal least squares</em> method, <em>random</em> and <em>stratified</em> selections). The experiments have been carried out with 8 real and artificial data sets. Positions of the control points in a low-dimensional space are found by principal component analysis. We demonstrate that <em>random</em> and <em>stratified</em> selections of control points are efficient and acceptable in terms of balance between projection error (<em>stress</em>) and time-consumption.</p><p>DOI: 10.15181/csat.v4i1.1095</p>Kotryna PaulauskienėOlga Kurasova2015-09-242015-09-244An Iterative Algorithm for Efficient Estimation of the Mean of a Normal Population Using Computational-Statistical Intelligence & Sample Counterpart of Rather-Very-Large Though Unknown Coefficient of Variation with a Small- Sample
http://journals.ku.lt/index.php/CST/article/view/1091
<p>This paper addresses the issue of finding the most efficient estimator of the normal population mean when the population “Coefficient of Variation (C. V.)” is ‘Rather-Very-Large’ though unknown, using a small sample (sample-size ≤ 30). The paper proposes an “Efficient Iterative Estimation Algorithm exploiting sample “C. V.” for an efficient Normal Mean estimation”. The MSEs of the estimators per this strategy have very intricate algebraic expression depending on the unknown values of population parameters, and hence are not amenable to an analytical study determining the extent of gain in their relative efficiencies with respect to the Usual Unbiased Estimator (sample mean ~ Say ‘UUE’). Nevertheless, we examine these relative efficiencies of our estimators with respect to the Usual Unbiased Estimator, by means of an illustrative simulation empirical study. <em>MATLAB 7.7.0.471 (R2008b)</em> is used in programming this illustrative ‘Simulated Empirical Numerical Study’.</p><p>DOI: 10.15181/csat.v4i1.1091</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong> </strong></p>Ashok SahaiRaghunadh Acharya2016-10-112016-10-114The multi-criteria assessment of Klaipeda region historical cemeteries destruction risk
http://journals.ku.lt/index.php/CST/article/view/915
<p>The article discusses the possibilities of Klaipeda region historic cemeteries destruction risk assessment using multiple criteria analytic hierarchy process (AHP). The proposing original assessment methodology developed by combining information from scientific literature on historical artefacts preservation topic with the data collected by scientists of Institute of Baltic Region History and Archaeology during their field expeditions to Klaipeda region Evangelical Lutheran Cemeteries.The results show that the process of historical cemeteries destruction risk assessment can be formalized and fully automated using AHP and modern software.</p><p>DOI:10.15181/csat.v4i1.915</p>Natalija JuščenkoVaida Skarulskytė-Tiurkina2016-11-292016-11-294