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Now showing items 21-28 of 28
Zero-shot learning via self-organizing maps
(Springer, 25.01.2023)
Collecting-labeled images from all possible classes related to the task at hand is highly impractical and may even be impossible. At this point, Zero-Shot Learning (ZSL) can enable the classification of new test classes ...
Improving Brain Tumor Classification with Deep Learning Using Synthetic Data
(Tech Science Press, 2023)
Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset ...
A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves
(Tech Science Press, 2023)
Parametric curves such as Bézier and B-splines, originally developed for the design of automobile bodies, are now also used in image processing and computer vision. For example, reconstructing an object shape in an image, ...
Deep learning prediction of motor performance in stroke individuals using neuroimaging data
(Elsevier Science Direct, 2023)
The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have ...
Inncreawatch Distilled Water Monitoring System
(KOSGEB, 2021-2023)
Inncreawatch Su Takip Sistemi web arayüzü üzerinden tıbbi su arıtma cihazlarının uzaktan izlenmesini ve kontrol edilmesini sağlamayı öngören bir sistemdir. Sistemin temel amacı, tıbbi su arıtma cihazlarının iletkenlik ve ...
Determination of Photonuclear Reaction Cross-Sections on Stable P-shell Nuclei by Using Deep Neural Networks
(Springer, 13 Mayıs 2)
Photonuclear reactions are widely used in investigations of nuclear structure. Thus, the determination of the cross-sections are essential for the experimental studies. In the present work, (γ, n) photonuclear reaction ...
Effects of Neighborhood-based Collaborative Filtering Parameters on Their Blockbuster Bias Performances
(Ağustos, 2)
Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably ...
Blockbuster: A New Perspective on Popularity-bias in Recommender Systems
(IEEE, Ekim, 2021)
Collaborative filtering algorithms unwittingly produce ranked lists where a few popular items are recommended too frequently while the remaining vast amount of items get not deserved attention, also referred to as the ...