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2022 International Conference on Statistics, data Science, and Computational Intelligence, CSDSCI 2022

作者： Deng, Jianglin School of Information and Engineering Zhejiang Ocean University Zhoushan316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510661325

With the increasing exploitation of marine resources, there is worldwide concern about the marine environment. It is a hot topic how to assert the marine pollution. Concentrating on dissolved oxygen and suspended solids in offshore water of yellow river mouth, this paper establishes a model of marine pollution assessment by a compositional interval-valued fuzzy relation with Bandler-Kohout subproduct. And a real-life example is shown for the sake of illustrating the efficiency of our proposed model. © 2023 SPIE.

关键词： Marine pollution

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Assisted diagnosis of breast cancer based on federated learning and improved DenseNet 5

Assisted diagnosis of breast cancer based on federated learn...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Xiang, Hexia Li, Jingjie Sun, Yongkai Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

Breast cancer is one of the common cancers worldwide, and how to identify breast cancer as early as possible plays an important role in its later diagnosis and treatment. However, relying only on data from a single organisation can limit the ability of trained neural networks to meet the performance demands of medical diagnostic services, and centralised learning that collects data from decentralised organisations can lead to privacy leakage. To address the above challenges, this paper proposes an assisted diagnosis model for breast cancer combining federated learning and improved DenseNet. Specifically, in contrast to mainstream centralised learning, this paper introduces federated learning to integrate multiparty data and effectively prevent privacy leakage. Each client sends the local model to the central server for aggregation, and then the global model obtained from the aggregation is sent to each client for training, thus achieving collaborative training of neural network models. In addition, in order to enhance the ability to capture feature information, this paper incorporates a Dynamic Convolution module into the densely connected network to improve the diagnostic accuracy without significantly increasing the size of the network model. The experimental results show that the proposed method has better diagnostic performance in breast cancer diagnosis compared with other methods. © 2024 SPIE.

关键词： Information leakage

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Towards Exploratory Query Optimization for Template-Based SQL Workloads 40

Towards Exploratory Query Optimization for Template-Based SQ...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Feng, Jieming Li, Zhanhuai Chen, Qun School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798350317152

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives. © 2024 IEEE.

关键词： Dynamic programming

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A novel numerical simulation of CO_(2) immiscible flooding coupled with viscosity and starting pressure gradient modeling in ultra-low permeability reservoir

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Frontiers of Earth Science 2023年第3期17卷 884-898页

作者： Jie CHI Binshan JU Jiabei WANG Xing ZHANG Wenbin CHEN Mengfei ZHANG School of Big Data and Fundamental Sciences Shandong Institute of Petroleum and Chemical TechnologyDongying 257061China School of Energy Resources China University of Geosciences(Beijing)Beijing 100083China Key Laboratory of Geological Evaluation and Development Engineering of Unconventional Natural Gas Energy Beijing 100083China School of Petroleum Engineering Chongqing University of Science&TechnologyChongqing 401331China Research Institute of Petroleum Engineering Technology Shengli Oilfield CompanySINOPECDongying 257000China

CO_(2) immiscible flooding is an environmentally-friendly and effective method to enhance oil recovery in ultra-low permeability reservoirs.A mathematical model of CO_(2) immiscible flooding was developed,considering the variation in crude oil viscosity and starting pressure gradient in ultra-low permeability reservoirs based on the non-Darcy percolation *** mathematical model and numerical simulator were developed in the C++language to simulate the effects of fluid viscosity,starting pressure gradient,and other physical parameters on the distribution of the oil pressure field,oil saturation field,gas saturation field,oil viscosity field,and oil *** results showed that the formation pressure and pressure propagation velocity in CO_(2) immiscible flooding were lower than the findings without considering the starting pressure *** formation oil content saturation and the crude oil formation viscosity were higher after the consideration of the starting pressure *** viscosity of crude oil considering the initiation pressure gradient during the formation was higher than that without this gradient,but the yield was lower than that *** novel mathematical models helped the characterization of seepage resistance,revealed the influence of fluid property changes on seepage,improved the mathematical model of oil seepage in immiscible flooding processes,and guided the improvement of crude oil recovery in immiscible flooding processes.

关键词： viscosity starting pressure gradient flow simulation CO_(2)immiscible flooding ultra-low permeability reservoir

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HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction

arXiv

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arXiv 2024年

作者： Liu, Bin Wu, Siqi Wang, Jin Deng, Xin Zhou, Ao Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications China

The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and target chemical structures. However, existing deep learning methods typically generate drug features via aggregating molecular atom representations, ignoring the chemical properties carried by motifs, i.e., substructures of the molecular graph. The atom-drug double-level molecular representation learning can not fully exploit structure information and fails to interpret the DTI mechanism from the motif perspective. In addition, sequential model-based target feature extraction either fuses limited contextual information or requires expensive computational resources. To tackle the above issues, we propose a hierarchical graph representation learning-based DTI prediction method (HiGraphDTI). Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules. Then, an attentional feature fusion module incorporates information from different receptive fields to extract expressive target features. Last, the hierarchical attention mechanism identifies crucial molecular segments, which offers complementary views for interpreting interaction mechanisms. The experiment results not only demonstrate the superiority of HiGraphDTI to the state-of-the-art methods, but also confirm the practical ability of our model in interaction interpretation and new DTI discovery. Copyright © 2024, The Authors. All rights reserved.

关键词： Atoms

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A Social recommendation model based on hypergraph deep representation learning 5

A Social recommendation model based on hypergraph deep repre...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Liu, Yanan Wu, Yufan Wang, Jinlan Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

In social recommendation, user's interest preference is influenced by multiple factors. It is a challenging problem to mine data information effectively in multiple types of interaction behaviors between users and items to obtain more accurate embedding of user items. Existing social recommendation models tend to take a homogeneous approach to the interaction effects of neighbourhood nodes on target nodes. We propose a social recommendation model based on deep representation learning of hypergraphs. The model aims to learn embedded representations of users and items from multi-type user-item interaction data efficiently. An attention mechanism that integrating time interval information is introduced for computing the preference weights of each item to the user. Messages are generated with time-interval attention weights the inter-user and inter-item hypergraph information is combined to update the messages and aggregate the information from multiple layers. Extensive experiments on two real datasets show that the proposed models are superior to other advanced recommendation methods in improving the recommendation performance. © 2024 SPIE.

关键词： Federated learning

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International Conference on Computer engineering and Intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

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An improved teaching-learning optimization algorithm based on fully informed search 4

An improved teaching-learning optimization algorithm based o...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Wu, Xiao Jiang, Xinghe Li, Shaobo Zhou, Yanqiu Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University Jiangnan Institute of Mechanical and Electrical Design Guiyang China

ISBN: (纸本)9798350354560

As an advanced version of the Rao algorithm, the fully informed search algorithm (FISA) exhibits exceptional global search capabilities. However, its slow convergence rate somewhat limits its applicability in solving real-world problems. In contrast, the teaching-learning-based optimization (TLBO) algorithm is renowned for its rapid convergence but is highly susceptible to local optima entrapment. To capitalize on the robust global search prowess of FISA and the swift convergence characteristic of TLBO, while addressing optimization challenges, this study introduces an innovative proactive hybrid algorithm termed FISTLBO. Comparative experiments are conducted on the CEC2022 benchmark suite comprising 20-dimensional test functions, pitting the FISTLBO algorithm against six classical algorithms. The results demonstrate that the FISTLBO algorithm not only achieves superior consistency and stability in benchmark function outcomes but also outperforms other comparative algorithms, securing the top rank in the Friedman rank sum test. Consequently, the proposed FISTLBO algorithm proves to be an effective solution for intricate optimization problems. © 2024 IEEE.

关键词： Optimization algorithms

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A Driving Area Detection Algorithm Based on Swin Transformer 5

A Driving Area Detection Algorithm Based on Swin Transformer

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Shuang Li, Ying Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China

ISBN: (纸本)9798350309034

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority. © 2023 IEEE.

关键词： CNNS Driving Area Detection Semantic segmentation Swin Transformer

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Multifactor prediction of carbon price based on principal component analysis and long short-term memory 3

Multifactor prediction of carbon price based on principal co...

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3rd International Conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wang, Yuanling Wen, Hao Deng, Shuyun Xia, Xin Chongqing Key Laboratory of Public Big Data Security Technology Chongqing College of Mobile Communication Chongqing401527 China School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China

ISBN: (纸本)9781510680449

In recent years, the development of the carbon trading market has become an important policy for China to achieve its "dual carbon"goals at an early date and to take on the role of a major international player in reducing emissions. Since the opening of the carbon market in 2013, there are now eight mature carbon trading pilot cities in China. Considering that carbon prices are susceptible to influences from factors such as the natural environment and financial policies, this paper introduces eight influencing factors from the energy, economic, financial, and natural environment levels for analysis. This paper selects the carbon trading market in Hubei, with the highest carbon trading volume, and the carbon trading market in Fujian, with the lowest carbon trading volume, as research objects. Firstly, the PCA model is applied to extract the main factors that influence carbon prices from the eight influential factors. Then, the LSTM model is used to establish a multifactor prediction model, and the GRU, RNN, and CNN models are used as comparative models. Finally, the predicted fitting graph is drawn, and the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are calculated. The research results show that the multi-factor prediction model based on LSTM performs the best in terms of price prediction accuracy. Through an in-depth exploration of various models, this study has made a significant contribution to the progress of carbon market research. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Carbon

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