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Antonello Pasini and Stefano Amendola
Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of cer...
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Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted....
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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Changhong Liu, Jiawen Wen, Jinshan Huang, Weiren Lin, Bochun Wu, Ning Xie and Tao Zou
Underwater object detection is crucial in marine exploration, presenting a challenging problem in computer vision due to factors like light attenuation, scattering, and background interference. Existing underwater object detection models face challenges ...
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Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song and Shuo Yan
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model signi...
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Zhao Xiong and Jiang Wu
Malaria is one of the major global health threats. Microscopic examination has been designated as the ?gold standard? for malaria detection by the World Health Organization. However, it heavily relies on the experience of doctors, resulting in long diagn...
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Leila Malihi and Gunther Heidemann
Efficient model deployment is a key focus in deep learning. This has led to the exploration of methods such as knowledge distillation and network pruning to compress models and increase their performance. In this study, we investigate the potential syner...
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Cesar G. Pachon, Diego Renza and Dora Ballesteros
One of the strategies adopted to compress CNN models for image classification tasks is pruning, where some elements, channels or filters of the network are discarded. Typically, pruning methods present results in terms of model performance before and aft...
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Leila Ben Letaifa and Jean-Luc Rouas
Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feed...
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Young-Jae La, Dasom Seo, Junhyeok Kang, Minwoo Kim, Tae-Woong Yoo and Il-Seok Oh
Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, pheno...
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