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Mohammad Alhumaid and Ayman G. Fayoumi
Paranasal sinus pathologies, particularly those affecting the maxillary sinuses, pose significant challenges in diagnosis and treatment due to the complex anatomical structures and diverse disease manifestations. The aim of this study is to investigate t...
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Karl Payne, Peter Chami, Ivanna Odle, David Oscar Yawson, Jaime Paul, Anuradha Maharaj-Jagdip and Adrian Cashman
Barbados is heavily reliant on groundwater resources for its potable water supply, with over 80% of the island?s water sourced from aquifers. The ability to meet demand will become even more challenging due to the continuing climate crisis. The consequen...
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Xing Yi, Hao Pan, Huaici Zhao, Pengfei Liu, Canyu Zhang, Junpeng Wang and Hao Wang
Image generation technology is currently one of the popular directions in computer vision research, especially regarding infrared imaging, bearing critical applications in the military field. Existing algorithms for generating infrared images from visibl...
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Huanhuan Zhang and Yufei Qie
Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the ...
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Manuel Domínguez-Rodrigo, Ander Fernández-Jaúregui, Gabriel Cifuentes-Alcobendas and Enrique Baquedano
Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblanc...
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Shuai Dong, Wei Wang, Wensheng Li and Kun Zou
A 2D floor plan (FP) often contains structural, decorative, and functional elements and annotations. Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D FP...
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Bilel Benjdira, Adel Ammar, Anis Koubaa and Kais Ouni
Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy ...
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Yong Liu, Jialin Zhou, Dong Zhang, Shaoyu Wei, Mingshun Yang and Xinqin Gao
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generativ...
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Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of ...
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Pedro Celard, Adrián Seara Vieira, José Manuel Sorribes-Fdez, Eva Lorenzo Iglesias and Lourdes Borrajo
In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Tem...
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Baris Yigin and Metin Celik
In recent years, advanced methods and smart solutions have been investigated for the safe, secure, and environmentally friendly operation of ships. Since data acquisition capabilities have improved, data processing has become of great importance for ship...
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Zahid Masood, Muhammad Usama, Shahroz Khan, Konstantinos Kostas and Panagiotis D. Kaklis
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be...
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Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
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Muzi Cui, Hao Jiang and Chaozhuo Li
Image inpainting aims to synthesize missing regions in images that are coherent with the existing visual content. Generative adversarial networks have made significant strides in the development of image inpainting. However, existing approaches heavily r...
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Fengxu Guan, Siqi Lu, Haitao Lai and Xue Du
Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image en...
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Liangliang Cheng, Yunfeng Dou, Jian Zhou, Huabin Wang and Liang Tao
Because of the acoustic characteristics of bone-conducted (BC) speech, BC speech can be enhanced to better communicate in a complex environment with high noise. Existing BC speech enhancement models have weak spectral recovery capability for the high-fre...
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Qing Liu, Jianjun Hao and Yijun Guo
The high cost of acquiring training data in the field of emotion recognition based on electroencephalogram (EEG) is a problem, making it difficult to establish a high-precision model from EEG signals for emotion recognition tasks. Given the outstanding p...
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Sunghae Jun
In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documen...
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Yibo He, Kah Phooi Seng and Li Minn Ang
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Amani Alqarni and Hamoud Aljamaan
Software defect prediction is an active research area. Researchers have proposed many approaches to overcome the imbalanced defect problem and build highly effective machine learning models that are not biased towards the majority class. Generative adver...
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