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Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
(Elsevier, 2022)
Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses ...
Binary multicriteria collaborative filtering
(Scientific and technological research council of turkey, 30.11.2020)
Collaborative filtering is specialized in suggesting appropriate products and services to the users concerning personal characteristics and past preferences without requiring any effort of users. It might be more efficient ...
New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems
(MDPI, 20.01.2021)
Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from ...
IESR: Instant Energy Scheduling Recommendations for Cost Saving in Smart Homes
(IEEE, 10.05.2022)
The exponential increase in energy demands continuously causes high price energy tariffs for domestic and commercial consumers. To overcome this problem, researchers strive to discover effective ways to reduce peak-hour ...
End to End Invoice Processing Application Based on Key Fields Extraction
(IEEE, 2022)
In this paper, an automatic invoice processing system, which is in great demand among private and public companies, was proposed. The proposed system supports all invoice file types that can be submitted by companies. ...
Robustness of privacy-preserving collaborative recommenders against popularity bias problem
(PeerJ, Temmuz, 20)
Recommender systems have become increasingly important in today’s digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences ...
LVQ Treatment for Zero-Shot Learning
(Tubitak Academic Journals, 23.01.2023)
In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically ...
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 ...