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Binbin Shi, Lijuan Zhang, Jie Huang, Huilin Zheng, Jian Wan and Lei Zhang
Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets...
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Rokaya Eltehewy, Ahmed Abouelfarag and Sherine Nagy Saleh
Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social medi...
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Fahim Sufi
GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitatin...
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Emilija Strelcenia and Simant Prakoonwit
In many industrialized and developing nations, credit cards are one of the most widely used methods of payment for online transactions. Credit card invention has streamlined, facilitated, and enhanced internet transactions. It has, however, also given cr...
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Kun Xiang and Akihiro Fujii
Climate change (CC) has become a central global topic within the multiple branches of social disciplines. Natural Language Processing (NLP) plays a superior role since it has achieved marvelous accomplishments in various application scenarios. However, C...
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Yingmei Qi, Heming Huang and Huiyun Zhang
Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. To addres...
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Jannis N. Kahlen, Michael Andres and Albert Moser
Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the ...
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Mehwish Leghari, Shahzad Memon, Lachman Das Dhomeja, Akhter Hussain Jalbani
Pág. 647 - 656
Now-a-days, in the field of machine learning the data augmentation techniques are common in use, especially with deep neural networks, where a large amount of data is required to train the network. The effectiveness of the data augmentation technique has...
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Nino Cauli and Diego Reforgiato Recupero
In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive t...
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Lijun Zu, Wenyu Qi, Hongyi Li, Xiaohua Men, Zhihui Lu, Jiawei Ye and Liang Zhang
The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriche...
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Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processin...
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Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th...
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Shree Krishna Acharya, Hwanuk Yu, Young-Min Wi and Jaehee Lee
Deep learning (DL) networks are a popular choice for short-term load forecasting (STLF) in the residential sector. Hybrid DL methodologies based on convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have a higher forecasting...
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Lei Li, Xiaobao Zeng, Xinpeng Pan, Ling Peng, Yuyang Tan and Jianxin Liu
Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent process...
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Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner...
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Oluwatoyin Joy Omole, Renata Lopes Rosa, Muhammad Saadi and Demóstenes Zegarra Rodriguez
Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestation...
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Yingcong Huang, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee and Mukesh Prasad
Parkinson?s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, a...
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François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie and Thomas Decourselle
Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the evaluation of cardiac function, facilitating faster clinical assessments that prove advantageous for both practitioners and patients alike. Recent studies have predominant...
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Fabi Prezja, Leevi Annala, Sampsa Kiiskinen and Timo Ojala
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive dataset...
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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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