Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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0302-9743 and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024...The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics:?..Part I - theory of neural networks

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Adaptive Fusion Boundary-Enhanced Multilayer Perceptual Network (FBAIM-Net) for Enhanced Polyp Segmeonstrate FBAIM-Net’s superior performance over state-of-the-art methods, supported by quantitative metrics and qualitative analyses. FBAIM-Net presents a promising approach to advancing polyp segmentation in medical image analysis.

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Phillip J. Belfiore,Jeffrey M. Hutchinsond a substantial class imbalance, having the positive class represent 1/20 of the whole dataset, the proposed approaches include dimensionality reduction and clustering techniques. According to the obtained results, the best-performing model is the Support Vector Machine, having an accuracy of 63%, a precision of 70%, and a recall of 63%.

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Isomorphic Fluorescent Nucleoside Analogs,r disease classification. However, due to multi-omics data’s complex and high-dimensional nature, classical statistical methods struggle to capture the shared information between microbiome and metabolome. Deep learning represents a power framework to address this issue. We design a deep learning mo

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Brandon F. Greene,Stella Kililierred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In this paper, we propose a novel E