ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Decision Analysis Applied to the Field of Environmental Health (2022)
    Cham :Springer International Publishing :
    Details
    Keywords: Environmental health. ; Biomedical engineering. ; Environmental engineering. ; Biotechnology. ; Bioremediation. ; Operations research. ; Environmental Health. ; Biomedical Engineering and Bioengineering. ; Environmental Engineering/Biotechnology. ; Operations Research and Decision Theory.
    Description / Table of Contents: Introduction -- Theoritical Aspect of the Decision Analysis -- Investigating The Effect Of Quarry Dust Enhancement On Engineering Behavior Of Expansive Soil Using MCDM -- Superior Types of Bamboo in Healtcare Using with Fuzzy PROMETHEE -- Evaluation of the Green Campus and Sustainable Campus: Green Building Rating System and Sustainability Approach in Higher Education.
    Abstract: This book provides students and researchers with a resource that includes the current application of the multi-criteria decision theory in a variety of fields, including the environment, health care, engineering, and architecture. There are many critical parameters (criteria) that can directly or indirectly affect the consequences of various decisions. The application of the multi-criteria decision theory focusses mainly on the use of computational methods which include multiple criteria and orders of preference for the evaluation and the selection of the best option among many alternatives based on the desired outcome. The theory of multi-criteria decision making (MCDM) is an approach that can be extremely useful for students, managers, engineers of manufacturing companies, etc. .
    Type of Medium: Online Resource
    Pages: VIII, 134 p. 23 illus., 21 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9783030966829
    Series Statement: Professional Practice in Earth Sciences,
    URL: https://doi.org/10.1007/978-3-030-96682-9
    DOI: 10.1007/978-3-030-96682-9
    DDC: 613.1
    Language: English
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    E-BOOKS (AWI ONLY)
  • 2
    Online Resource
    Online Resource
    Application of Multi-Criteria Decision Analysis in Environmental and Civil Engineering (2021)
    Cham :Springer International Publishing :
    Details
    Keywords: Environmental management. ; Civil engineering. ; Environmental monitoring. ; Environmental Management. ; Civil Engineering. ; Environmental Monitoring.
    Description / Table of Contents: Introduction -- Theoretical Aspects of Multi-Criteria Decision-Making (MCDM) Techniques -- Analytic Hierarchy Process (AHP) -- The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) -- ELimination Et Choix Traduisant la REalité (ELECTRE) -- Preference Ranking Organization Method for Enrichment evaluation (PROMETHEE) -- VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian (VIKOR) -- Fuzzy Logic and Fuzzy Based MCDM -- Evaluation of Water Sterilization Devices using MCDM -- Evaluation and Optimization of the Treatment Scheme for the Paint Industry Effluents.
    Abstract: The purpose of this book is to provide a resource for students and researchers that includes current application of a multi-criteria, decision-making theory in various fields such as: environment, healthcare and engineering. In addition, practical application are shown for students manually. In real life problems there are many critical parameters (criteria) that can directly or indirectly affect the consequences of different decisions. Application of a multi-criteria, decision-making theory is basically the use of computational methods that incorporate several criteria and order of preference in evaluating and selecting the best option among many alternatives based on the desired outcome.
    Type of Medium: Online Resource
    Pages: VI, 209 p. 13 illus., 10 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9783030647650
    Series Statement: Professional Practice in Earth Sciences,
    URL: https://doi.org/10.1007/978-3-030-64765-0
    DOI: 10.1007/978-3-030-64765-0
    DDC: 333.7
    Language: English
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    E-BOOKS (AWI ONLY)
  • 3
    facet.materialart.
    Unknown
    Deep networks in identifying CT brain hemorrhage (2018)
    IOS Press
    Details
    Publication Date: 2018-08-26
    Print ISSN: 1064-1246
    Electronic ISSN: 1875-8967
    Topics: Mathematics
    Published by IOS Press
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.
    Print ISSN: 1687-9724
    Electronic ISSN: 1687-9732
    Topics: Computer Science
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...