Zobrazeno 1 - 10
of 226
pro vyhledávání: '"IAKOVIDIS, Dimitris"'
Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. Yet there are still limitations in d
Externí odkaz:
http://arxiv.org/abs/2412.04121
Autor:
Gatoula, Panagiota, Diamantis, Dimitrios E., Koulaouzidis, Anastasios, Carretero, Cristina, Chetcuti-Zammit, Stefania, Valdivia, Pablo Cortegoso, González-Suárez, Begoña, Mussetto, Alessandro, Plevris, John, Robertson, Alexander, Rosa, Bruno, Toth, Ervin, Iakovidis, Dimitris K.
Sharing retrospectively acquired data is essential for both clinical research and training. Synthetic Data Generation (SDG), using Artificial Intelligence (AI) models, can overcome privacy barriers in sharing clinical data, enabling advancements in m
Externí odkaz:
http://arxiv.org/abs/2411.00178
Anomaly detection (AD) plays a pivotal role in multimedia applications for detecting defective products and automating quality inspection. Deep learning (DL) models typically require large-scale annotated data, which are often highly imbalanced since
Externí odkaz:
http://arxiv.org/abs/2409.13602
The interpretability of machine learning models is critical, as users may be reluctant to rely on their inferences. Intuitionistic FCMs (iFCMs) have been proposed as an extension of FCMs offering a natural mechanism to assess the quality of their out
Externí odkaz:
http://arxiv.org/abs/2408.03745
Autor:
Cholopoulou, Eirini, Diamantis, Dimitrios E., Koutsiou, Dimitra-Christina C., Iakovidis, Dimitris K.
Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography. During the last decades physics and machine learning -based methodologies have been proposed;
Externí odkaz:
http://arxiv.org/abs/2408.03734
Autor:
Diamantis, Dimitrios E., Gatoula, Panagiota, Koulaouzidis, Anastasios, Iakovidis, Dimitris K.
Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the context of image-based Clinical Decision Support (CDS) systems. To this e
Externí odkaz:
http://arxiv.org/abs/2302.02150
The adoption of Convolutional Neural Network (CNN) models in high-stake domains is hindered by their inability to meet society's demand for transparency in decision-making. So far, a growing number of methodologies have emerged for developing CNN mod
Externí odkaz:
http://arxiv.org/abs/2208.05369
Publikováno v:
IEEE Transactions on Fuzzy Systems, vol. 29, no. 11, pp. 3481 - 3488, Nov. 2020
Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling
Externí odkaz:
http://arxiv.org/abs/2202.08372
Publikováno v:
In Neurocomputing 28 September 2024 599
The huge amount of video data produced daily by camera-based systems, such as surveilance, medical and telecommunication systems, emerges the need for effective video summarization (VS) methods. These methods should be capable of creating an overview
Externí odkaz:
http://arxiv.org/abs/2011.10432