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Lazcano H.G.,Pedro de Valdivia University | Quintana M.G.B.,Knowledge Computing
International Journal of Engineering Education | Year: 2014

This research is focused on developing an approach for learning activities in a virtual learning environment employing Web 2.0. These activities are incorporated into the design of a course intended to contribute to the development of generic and specific competencies. Input from subject experts was utilized in developing the approach. A pilot project was implemented and assessed in aCivil Engineering and Informatics course at the Universidad Católica de la Santísima Concepción. The approach was assessed through a developed guide to the most effective course design based on learning theories relating instructional design to learning outcomes. © 2014 TEMPUS Publications. Source


Georgopoulos V.C.,Technological Educational Institute of Western Greece | Chouliara S.,Obstetrician and Gynecological Clinic | Stylios C.D.,Knowledge Computing
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 | Year: 2014

Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive 'what-if' scenarios in case studies and prepare for dealing with critical adverse events. © 2014 IEEE. Source


Stylios C.D.,Knowledge Computing | Georgopoulos V.C.,Alexander Technological Educational Institute of Thessaloniki
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2011

Medical Decision Support Systems (MDSS) are very important constructions that are incorporated into Intelligent Information E-Health Systems aiming to produce warnings or to consult and suggest clinical judgments either to inexperienced medical professionals or in their lighter versions to the general public through medical advisors websites. Soft Computing (SC) techniques, especially those that are based on exploiting human knowledge and experience, are extremely useful to model complex decision making procedures and thus, they have a key role in development such MDSS. Such a modeling methodology is Fuzzy Cognitive Maps which is suitable to represent human reasoning and to infer conclusions and decisions in a human-like way. In order to develop an integrated stand alone MDSS, Fuzzy Cognitive Maps could be complemented by other Soft Computing techniques such as Genetic Algorithms and/or Case Based Reasoning and so to construct more efficient advanced Medical Decision Support Systems. The synergism and complementary of these methodologies may pave the way to new sophisticated systems. © 2011 IFAC. Source


Patent
Knowledge Computing | Date: 2011-05-27

Date-warehouse systems are populated using an enhanced Extraction-Load-Transform (ETL) process and system by employing three ideas: Out-of-order-fill ETL, relative-ordering index (ROI), and dependent queries. Out-of-order-fill ETL allows a data warehouse to accept the loading of data files in any order, and does not require the loading of any previous backup data files in order to provide some functionality to end users under the view that some functionality or data access is better than none at all. Dependent queries are processes that use defined data structures for use in constructing, extracting, and validating each record to be written in said data-warehouse system in order to ensure that referential integrity is maintained and that no orphaned data is pushed into the data warehouse. Finally, ROI is a process wherein a value is determined, based on the constraints of the source data, which indicates the relative newness of the data.


Kolios S.,Knowledge Computing | Stylios C.D.,Knowledge Computing
Applied Geography | Year: 2013

This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from the Enhanced Thematic Mapper (ETM+) sensor on board at the Landsat 7 satellite platform is used for this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, there was an increasing tourist activity and a high growth in the construction sector of the study area. The land-use changes were identified, examining several vegetation indices and band combinations, along with the implementation of different well-known classification techniques. The Normalized Difference Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification techniques. The best overall accuracy for the study area was achieved with the SVM classifier and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The classification results depicted notable urbanization, small deforestation and important LULC changes in the agriculture sector, indicating a rapid coastal environment change in the region of interest. © 2013 Elsevier Ltd. Source

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