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  • Springer  (2)
  • American Association for the Advancement of Science (AAAS)  (1)
  • Molecular Diversity Preservation International  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Multiple-Category Decision-Making: Review and Synthesis (1999)
    Springer
    Marketing letters 10 (1999), S. 319-332 
    Details
    ISSN: 1573-059X
    Keywords: Multiple-Category Choice ; Consideration Sets ; Cross-Category Dependence ; Product Bundling ; Market Baskets
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008143526174
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Perspectives on Multiple Category Choice (1997)
    Springer
    Marketing letters 8 (1997), S. 297-305 
    Details
    ISSN: 1573-059X
    Keywords: multiple category choice ; product bundles ; market basket models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Economics
    Notes: Abstract Multiple category choice is a decision process in which an individualselects a number of goods, all of which are nonsubstitutable with respect toconsumption. Choices can be made either simultaneously or sequentially. Thekey feature of multiple category choice is the treatment of the choices asinterrelated because each item in the final collection of goods contributesto the achievement of a common behavioral goal. We discuss current andpotential applications of psychology, economics and consumer choice theoryin developing models of multiple category choice.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007960529932
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    Paper (German National Licenses)
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  • 3
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    Ultralow thermal conductivity in disordered, layered WSe2 crystals (2006)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2006-12-16
    Description: The cross-plane thermal conductivity of thin films of WSe2 grown from alternating W and Se layers is as small as 0.05 watts per meter per degree kelvin at room temperature, 30 times smaller than the c-axis thermal conductivity of single-crystal WSe2 and a factor of 6 smaller than the predicted minimum thermal conductivity for this material. We attribute the ultralow thermal conductivity of these disordered, layered crystals to the localization of lattice vibrations induced by the random stacking of two-dimensional crystalline WSe2 sheets. Disordering of the layered structure by ion bombardment increases the thermal conductivity.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Chiritescu, Catalin -- Cahill, David G -- Nguyen, Ngoc -- Johnson, David -- Bodapati, Arun -- Keblinski, Pawel -- Zschack, Paul -- New York, N.Y. -- Science. 2007 Jan 19;315(5810):351-3. Epub 2006 Dec 14.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois, Urbana, IL 61801, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17170252" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 4
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    Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-05-30
    Description: Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.
    Electronic ISSN: 2079-9292
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
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