Simple proportional and integral controller is employed to control benchmark hydrodynamic process, a coupled tanks system. The proportional part of the controller is placed in the feedback path to avoid an overshoot i...
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Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. Howe...
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ISBN:
(纸本)9798350386226
Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom
The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorol...
ISBN:
(纸本)9781510687615
The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and image processing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.
In the dynamic landscape of entrepreneurship, the convergence of artificial intelligence (AI) and computerscience has heralded a paradigm shift in startup matchmaking. This paper examines the transformative impact of...
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In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, i...
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Software Defined Networking (SDN) has emerged as a revolutionary network architecture aimed at surpassing the constraints inherent in traditional network infrastructures. As SDN adoption increases, it brings additiona...
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The paper explores the design, implementation, and evaluation of an innovative approach to streamline attendance tracking in various domains. Leveraging advancements in computer vision and facial recognition technolog...
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Residential prosumers enable the peer-to-peer energy transfer to prompt the management of local energy resources for the benefit of their neighbourhood. This encourages the researchers to develop energy management pro...
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Convolutional neural networks (CNNs) perform excellently in many image processing and computer vision tasks. However, their complex structure and the vast number of parameters require substantial computational and sto...
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This paper proposes a new score function (SF) of interval-valued intuitionistic fuzzy values (IVIFVs) in order to overcome the shortcomings of the existing SFs of IVIFVs, which are not be able to distinguish the ranki...
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