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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Jiang, Xiaoyan Zhou, Zhi Wang, Hailing Wang, Guozhong Fang, Zhijun School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798331520526

Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. How-ever, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To address these limitations, we developed datasets with lesion-specific text annotations for liver tumors and introduced the TexLiverNet model. TexLiverNet employs an agent-based cross-attention module that integrates text features efficiently with visual features, significantly reducing computational costs. Additionally, enhanced spatial and adaptive frequency domain perception is proposed to precisely delineate lesion boundaries, reduce background interference, and recover fine details in small lesions. Comprehensive evaluations on public and private datasets demonstrate that TexLiverNet achieves superior performance compared to current state-of-the-art methods. Code is available at https://***/sky-visionX/TexLiverNet. © 2025 IEEE.

关键词： Image segmentation

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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Research on MapReduce Heuristic Multi Table Join Algorithm Based on Binary Optimization and Pancake Parallel Strategy

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Recent Patents on engineering 2023年第6期17卷 49-62页

作者： Jia, Songhao Yang, Jizheng Yang, Cai Zhang, Haiyu School of Computer Science and Technology Nanyang Normal University Nanyang China Faculty of Electronic Information Engineering Henan Polytechnic Institute Nanyang China

Background: With the development of technology, the data amount has increased signif-icantly. In data processing, the multi table query is the most frequent operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper pro-poses a heuristic multi table join optimization method. By sharing information, the unconnected tu-ples are eliminated so as to reduce the amount of data transmitting. This shortens response time and improves execution performance. Methods: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying. © 2023 Bentham science Publishers.

关键词： Optimization

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Machine Learning Approach to Detect Fake News 17

Machine Learning Approach to Detect Fake News

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Khare, Sakshi Singh, Pushpa Kumar, Pankaj Shrivastava, Rajesh Kumar Gl Bajaj Institute of Technology Department of Computer Science and Engineering Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350307672

Social media is a powerful platform that allows users to share their thoughts, news, and a variety of information freely. Most people trust social media news and get affected by it. No one is sure about the content available on the social media. In this paper, we will target to detect whether the given content over social media is real or fake. Here, we applied various machine learning methods to label data and compare their accuracy. we achieved 99% accuracy, 99% precision, 99% recall, and 99f1score value as our experiment result © 2023 IEEE.

关键词： Support vector machines

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An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit

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Journal of Bionic engineering 2025年第2期22卷 866-883页

作者： Xin Wang Jian Feng Yuxin Qin College of Electrical and Information Engineering Hunan University of TechnologyZhuzhou412007HunanChina Hunan Engineering Research Centre of Electrical Drive and Regenerative Energy Storage and Utilization Zhuzhou412007HunanChina School of Computer Science University of GlasgowGlasgowG128QQScotlandUK

To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.

关键词： Multi-objective artificial hummingbird algorithm Tent mapping based on random variables Urban rail transit Supercapacitor energy storage systems Capacity allocation

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Quorum sensing-based interactions among drugs,microbes,and diseases

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science China(Life sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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Improve Model-Contrastive Federated Learning by Momentum Contrast 3

Improve Model-Contrastive Federated Learning by Momentum Con...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Chen, Xiaoyang Li, Bokang Li, Weiheng School of Computer Science and Technology Tiangong University Tianjin China College of Information Science and Engineering Henan University of Technology Zhengzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9781510664814

FedAvg is a classic algorithm for Federated Learning. The MOON algorithm is based on the idea of comparative learning. The model in the previous round is a negative sample, and the global model is a positive sample, which improves the local training of FedAvg. However, in the study of MOON, it was found that since the MOON algorithm compares models rather than pictures, the number of negative samples is difficult to expand, and in traditional comparative learning, the total number of negative samples will significantly affect the effect of comparative learning, when the model in this round is far from the model in the previous round, it may approach the earlier model, thus reducing the efficiency of contrastive learning. So, this paper studied more algorithms related to contrastive learning and federated learning. Finally, a dictionary was built based on the MoCo algorithm to save the previously generated local models. The improved MOON algorithm was named FedCOMO. With the extension of negative samples, the training accuracy on the CIFAR-10 dataset can exceed MOON. © 2023 SPIE.

关键词： Moon

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Breast Cancer Diagnosis with XAI-Integrated Deep Learning Approach

Breast Cancer Diagnosis with XAI-Integrated Deep Learning Ap...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Tanim, Sharia Arfin Alam, Gazi Mohammad Imdadul Shrestha, Tahmid Enam Islam, Maruful Jahan, Fariha Nur, Kamruddin Dhaka Bangladesh School of Science Engineering & Technology East Delta University Chattogram Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (纸本)9798331533137

Breast cancer is one of the world’s significant health challenges. There is a need to have better techniques in the early diagnosis of this disease to increase patients’ survival rates. This paper introduces a robust approach for breast cancer detection by integrating deep-learning and classical machine-learning techniques into a custom lightweight neural network model. This approach is based on integrating conventional machine-learning and deep-learning paradigms using a range of data pre-processing steps, including data cleaning, label converting, scaling, and feature selecting to improve the given dataset’s readiness for training. The proposed model demonstrated impressive accuracy in breast cancer detection compared to individual classifiers by achieving an overall accuracy of 97.54%. Additionally, integrating eXplainable Artificial Intelligence (XAI) techniques gives the application interpretability and transparency for clinicians to make sense of the feature’s importance and individual prognosis. It is a more accurate and easy-to-understand tool for clinicians, making it a better use of faulty or confusing reference values. This study presents the need to learn more about making deep learning and eXplainable Artificial Intelligence (XAI) complementary approaches to breast cancer diagnosis and treatment research. ©2024 IEEE.

关键词： Contrastive Learning

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Enhancing Productivity and User Experience with Advanced Notepad: A Comprehensive Study 10

Enhancing Productivity and User Experience with Advanced Not...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Solanki, Chetna Bana, Garima Singh, Royal Goyal, Mayank Kumar Sharan, Bhagwati Gupta, Raj School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Engineering and Sciences Srm University-AP Department of Computer Science and Engineering Amaravati India

ISBN: (纸本)9789380544472

The current work outlines the creation of a system to facilitate collaborative software design. Based on the observation of system use during real-world design sessions, an iterative development strategy has been employed. As a result, we can fix interface flaws and gain additional knowledge about collaborative design. The proposed work is based on developing an advanced Notepad using Python and Tkinter Library to enhance productivity and user experience. It will provide the user with multiple themes and Fonts. The work is experimented using Visual Studio Code. The outcomes provide better quality of user experience. Additionally, we observe how conceptions change over time, frequently resulting in quick revisions that change the structure. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sentiment analysis

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