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Multi-Omics Data Integration Patient Classification Method Based on Deep Dense Residual Shrinkage Network

Multi-Omics Data Integration Patient Classification Method B...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Wenyao Li Kai Lin Yaqing Hou Qiang Zhang School of Computer of Science Dalian University of Technology Dalian China

Represented by high-throughput sequencing technology, rapid advances in omics technologies have allowed scholars to understand human diseases in a more profound and comprehensive manner. However, omics data are difficult to analyze accurately on account of their characters: massive redundancy, full of noise and high dimension. Meanwhile, it is crucial to improve the performance of omics analysis from multiple modalities using data from different omics. To this end, we propose a novel multi-omics integration model for patient classification. Firstly, anti-noisy deep dense residual shrinkage neural networks (DDRSNN) are trained and utilized to make preliminary predictions on various omics data of patients. then confidence scores are calculated for each sample through the confidence distance to evaluate the reliability of the preliminary prediction result. Finally, referring to uncertain evidence fusion theory, the preliminary prediction results from different omics are integrated based on confidence scores and the final patient classification is obtained. the algorithm combines multiple omics, treating each omics as evidence of a separate modality, and improves the accuracy and reliability of patient classification by integrating evidence from multiple modalities.

关键词： Sequential analysis Neural networks Redundancy Data integration Reliability theory Prediction algorithms Data models

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Improving the Early Rumor Detection Performance of the Deep Learning Models By CGAN 34

Improving the Early Rumor Detection Performance of the Deep ...

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34th International conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Dong, Fangmin Zhu, Yumin Wan, Shuzhen Xu, Yichun Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments China Three Gorges University Hubei Yichang443002 China College of Computer and Information Technology China Three Gorges University Hubei Yichang443002 China

ISBN: (纸本)1891706543

Deep learning models are recently applied to detect rumors on social media based on the information in the posts. However, at the early stage of rumor propagation, due to the lack of responses, the performance of these models often degrades. In this paper, we propose a method based on the conditional generative adversarial network, which can generate the responses like data and help the deep learning models in early detection. On two large-scale Sina Weibo datasets, the proposed method is applied on the existing convolution neural network model, the recurrent neural network model, and the recursive neural network model. the results show that the proposed method can significantly improve the performance of the models in the case of zero response, and has performance superiority in a certain early period. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Generative adversarial networks

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Optimized Echo State Network based on PSO and Gradient Descent for Choatic Time Series Prediction

Optimized Echo State Network based on PSO and Gradient Desce...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Rebh Soltani Emna Benmohamed Hela Ltifi Computer Science and Mathematics Department Faculty of sciences and techniques of Sidi Bouzid University of Kairouan Tunisia Sidi bouzid Tunisia Computer Science Department Faculty of Sciences of Gafsa University of Gafsa Tunisia REsearch Groups in Intelligent Machines University of Sfax National School of Engineers (ENIS) Sfax Gafsa Tunisia Computer Science and Mathematics Department Faculty of sciences and techniques of Sidi Bouzid University of Kairouan Tunisia REsearch Groups in Intelligent Machines University of Sfax National School of Engineers (ENIS) Sfax Sidi bouzid Tunisia

Echo State Network (ESN), as a paradigm of Reservoir Computing (RC), refers to a well-known Recurrent Neural Network (RNN). Its randomly generated reservoir represents the main reason for its ability of rapid learning. Nevertheless, designing a reservoir for a specific role constitutes a difficult task. To resolve the challenge of the reservoir structure design, in this paper, a new combination of two optimization methods, Particle Swarm Optimization (PSO) and Stochastic Gradient Descent (SGD), have been proposed to reach a higher performance. the resulted model was tested using Mackey Glass and NARMA 10 benchmarks. the experimentations proved that the suggested PSO-SGD-ESN model performs well in time series prediction tasks and outperforms the original one.

关键词： Recurrent neural networks Time series analysis Stochastic processes Optimization methods Predictive models Benchmark testing Reservoirs

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Weakly Supervised Anomaly localization and Segmentation of Biomarkers in OCT Images

Weakly Supervised Anomaly Localization and Segmentation of B...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaoming Liu Qi Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

Identifying biomarkers from optical coherence tomography images is critical in diagnosing and treating ophthalmic diseases. Most existing biomarker segmentation methods require pixel-level annotations for training, which is time-consuming and labor-intensive. this paper proposed a novel weakly supervised biomarker localization and segmentation method. the framework includes a classification network and a teacher-student network to exploit category annotated data through contrastive learning and anomaly localization strategies based on knowledge distillation. the classification network combines cross-entropy loss and self- supervised contrastive loss to ensure that the model focuses on the characteristics of the biomarker of interest. We introduce a knowledge distillation-based anomaly localization method to localize biomarker-related pathological regions accurately. the trained classification network acts as a teacher model to guide the training of the student network to learn the features of normal OCT images. the biomarker regions can be accurately localized by the differences between the feature maps generated by the two networks. Experiment results on the public dataset demonstrate the effectiveness of the proposed method.

关键词： Location awareness Training Knowledge engineering Image segmentation Pathology Annotations Biological system modeling

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A Dilated Transformer Network for Time Series Anomaly Detection

A Dilated Transformer Network for Time Series Anomaly Detect...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Bo Wu Zhenjie Yao Yanhui Tu Yixin Chen Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Purple Mountain Laboratories Nanjing China Institute of Microelectronics Chinese Academy of Sciences Purple Mountain Laboratories Beijing China Shandong Future Network Research Institute Purple Mountain Laboratories Beijing China Washington University in St.Louis Purple Mountain Laboratories Washington USA

Unsupervised anomaly detection for time series has been an active research area due to its enormous potential for wireless network management. Existing works have made extraordinary progress in time series representation, reconstruction and forecasting. However, long-term temporal patterns prohibit the model from learning reliable dependencies. To this end, we propose a novel approach based on Transformer with dilated convolution for time anomaly detection. Specifically, we provide a dilated convolution module to extract long-term dependence features. Extensive experiments on various public benchmarks demonstrate that our method has achieved the state-of-the-art performance.

关键词： Convolution Wireless networks Time series analysis Benchmark testing Transformers Feature extraction Market research

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Correlation Enhancement with Graph Convolutional Network for Pedestrian Attribute Recognition

Correlation Enhancement with Graph Convolutional Network for...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Chen Chen Zhihan Zuo Yuchun Fang Yilu Cao Yaofang Zhang School of Computer Engineering and Science. Shanghai University Shanghai China

Pedestrian attribute recognition aims to predict human attributes in surveillance video. Since the correlations are different among attribute groups, mining the correlation variation between attributes is an effective way to improve the performance of pedestrian attribute recognition. In this paper, we propose a Feature Enhanced Multi-scale and Graph Convolutional Compound Network named FEM-GCNet to learn the enhanced correlations between attributes. the FEM-GCNet contains an enhanced feature extractor and a graph convolutional network. the enhanced feature extractor learns the enhanced features of attributes, improving the feature representation. the graph convolutional network exploits attribute correlation rules to mine the correlation features between attributes, which are embedded into the enhanced features to obtain the enhanced attribute correlations. Finally, a multi-label loss is used to train our model to identify pedestrian attributes. Experiments on RAPv1 and RAPv2 datasets demonstrate the robustness of the enhanced attribute correlations and the better performance of the FEM-GCNet.

关键词： Correlation Surveillance Feature extraction Robustness Convolutional neural networks Compounds Artificial intelligence

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Multiscale Approach in Deep Convolutional networks for Minutia Extraction from Contactless Fingerprint Images

Multiscale Approach in Deep Convolutional Networks for Minut...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Anderson Nogueira Cotrim Helio Pedrini Institute of Computing University of Campinas Campinas SP Brazil

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. the results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software.

关键词： Deep learning COVID-19 Image matching Computational modeling Software algorithms computer architecture Fingerprint recognition

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Distributed Support Vector Machine Based on Distributed Loss

Distributed Support Vector Machine Based on Distributed Loss

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuefeng Ma Mengwei Wang School of Computer Science Qufu Normal University Rizhao China

Support vector machine (SVM) is a fundamental machine learning method with solid mathematical theory and high effectiveness in many applications. Because distributed datasets are difficult to centralize, SVM is hard to be computed by using traditional algorithms in distributed environment. Meanwhile most of existing distributed SVM methods are suffering in very time-consuming. the dilemma of existing distributed SVM methods has hindered their application in a great deal of domain. In this paper, we focus on the improvement of training efficiency for distributed SVM by proposing a distributed SVM method with distributed loss (namely DL-DSVM). We firstly construct an optimization problem of distributed SVM based on distributed loss. then, considering constrains in distributed environment, we propose a fast training method to solve the optimization problem based on the local optimal solution. Comprehensive experimental results show that DL-DSVM has an excellent performance in time complexity and robustness, and no significant decline in other aspects.

关键词： Support vector machines Training Machine learning algorithms Machine learning Solids Robustness Time complexity

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Image Inpainting with Context Flow Network

Image Inpainting with Context Flow Network

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Jianwen Liu Jiarui Xue Juan Zhang Ying Yang School of Journalism and Communication Shaanxi Normal University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

Image inpainting using deep and complicated convolutional neural networks(CNN) has recently produced outstanding results. Several researchers have considered employing large receptive fields and deep networks for long-distance information transfer to obtain semantically coherent inpainting results. As a side effect, these strategies would lead to the loss of detail and other artifacts. Motivated by the attention mechanism and sequence-to-sequence model, a novel convolution structure called context flow module is introduced into a coarse-to-fine two stages network, extracting information from distant regions without extra network layers or details loss. the context flow module in the refinement network can effectively gather both spatial and contextual data in the distance, and flow information to the next layer patch by patch. the coarse and refinement networks' backbones are encoder-decoder architecture stacked with gated and dilated convolutions. the refinement network encloses two extra elements: the context flow module and a feature-sharing space. the coarse network generates semantically consistent images with no gaps. the refinement network enhances the sharpness and enriches the details of the initial results. Moreover, a patch-based GAN is applied to stabilize training and generate semantically reasonable results. Experimental results show that our method excels at the performance of the state-of-the-art methods on faces(CelebA), buildings(Paris Street View), and natural images(Places2) datasets. the proposed context flow module can be easily integrated with any existing networks to improve their inpainting performance.

关键词： Training Convolution Shape Semantics Memory management Generative adversarial networks Feature extraction

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Empowering Federated Graph Rationale Learning with Latent Environments 25

Empowering Federated Graph Rationale Learning with Latent En...

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34th ACM Web conference, WWW 2025

作者： Yue, Linan Liu, Qi Li, Yawen Yao, Fangzhou Gao, Weibo Du, Junping School of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China

ISBN: (纸本)9798400712746

the success of Graph Neural networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. this method aims to enhance GNNs explainability by identifying subgraph structures (i.e., rationales) that support model predictions. However, existing methods often rely on centralized datasets, posing challenges in scenarios where data privacy is crucial, such as in molecular property prediction. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. In this context, Federated Graph Rationalization emerges as a promising research direction. However, in each client, the rationalization methods often rely on client-specific shortcuts to compose rationales and make task predictions. Data heterogeneity, characterized by non-IID data across clients, exacerbates this problem, leading to poor prediction performance. To address these challenges, we propose the Environment-aware Data Augmentation (EaDA) method for Federated Graph Rationalization. EaDA comprises two main components: the Environment-aware Rationale Extraction (ERE) module and the local-Global Alignment (LGA) module. the ERE module employs prototype learning to infer and share abstract environment information across clients, which are then aggregated to form a global environment. this information is used to generate counterfactual samples for local clients, enhancing the robustness of task predictions. the LGA module uses contrastive learning methods to align local and global rationale representations, mitigating performance degradation due to data heterogeneity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of our approaches. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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