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Abdelghani Azri, Adil Haddi and Hakim Allali
Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user?item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in ...
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Nadia Brancati and Maria Frucci
To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and ...
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Xuanyuan Xie and Jieyu Zhao
The diffusion model has made progress in the field of image synthesis, especially in the area of conditional image synthesis. However, this improvement is highly dependent on large annotated datasets. To tackle this challenge, we present the Guided Diffu...
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Zhenyu Yin, Feiqing Zhang, Guangyuan Xu, Guangjie Han and Yuanguo Bi
Confronting the challenge of identifying unknown fault types in rolling bearing fault diagnosis, this study introduces a multi-scale bearing fault diagnosis method based on transfer learning. Initially, a multi-scale feature extraction network, MBDCNet, ...
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Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias and Antonio G. Ravelo-García
This study presents a novel approach for kernel selection based on Kullback?Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of ...
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Zilin Zhao, Yuanying Chi, Zhiming Ding, Mengmeng Chang and Zhi Cai
Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow ...
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Yunfei Zhang, Hongzhen Xu and Xiaojun Yu
An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborativ...
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Sevilay Kilmen and Okan Bulut
Psychological scales play a key role in the assessment, screening, and diagnosis of latent variables, such as emotions, mental health, and well-being. In practice, researchers need shorter scales of psychological traits to save administration time and co...
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Zitong Yan, Hongmei Liu, Laifa Tao, Jian Ma and Yujie Cheng
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fully ...
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Laurent Risser, Agustin Martin Picard, Lucas Hervier and Jean-Michel Loubes
The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applic...
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