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An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

computers, Materials & Continua 2023年第10期77卷 665-680页

作者： Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Ahmad Almadhor Tai-hoon Kim Department of Computer Science COMSATS UniversityIslamabadPakistan College of Computer Studies De La Salle UniversityManila1004Philippines Faculty of Information and Communication Studies University of the Philippines Open UniversityLos Banos4031Philippines College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia School of Electrical and Computer Engineering Yeosu CampusChonnam National UniversityYeosu-si59626Korea

Cardiac disease is a chronic condition that impairs the heart’s *** includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart *** conditions can lead to serious complications and even be life-threatening if not detected and managed in *** have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and *** approaches have been applied to predict and treat heart disease utilizing ML and *** paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease *** approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart *** models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart ***-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed *** reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

关键词： Deep neural network heart disease healthcare machine learning stacking

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Noise-robust neural networks for medical image segmentation by dual-strategy sample selection

Noise-robust neural networks for medical image segmentation ...

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作者： Shi, Jialin Yang, Youquan Zhang, Kailai School of Computer and Communication Engineering Shunde Innovation School University of Science and Technology Beijing Beijing China Shunde Innovation School ByteDance Beijing China Department of Networks China Mobile Communications Group Co Ltd Beijing China

Deep neural networks for medical image segmentation often face the problem of insufficient clean labeled data. Although non-expert annotations are more readily accessible, these low-quality annotations lead to significant performance degradation of existing neural network methods. In this paper, we focus on robust learning of medical image segmentation with noisy annotations and propose a novel noise-tolerant framework based on dual-strategy sample selection, which selects the informative samples to provide effective supervision information. First, we propose the first round of sample selection by designing a novel joint loss, which includes conventional supervised loss and regularization loss. To further select information-rich samples, we propose confidence-based pseudo-label sample selection from a novel perspective as the complement. The dual strategies are used in a collaborative manner and the network is optimized with mined informative samples. We conducted extensive experiments on datasets with both simulated noisy labels and real-world noisy labels. For instance, on a simulated dataset with 25% noise ratio, our method achieves segmentation Dice value with 90.56% (Formula presented.) 0.03%. Furthermore, increasing the noise ratio to 95%, our method still maintains a high Dice value of 73.85% (Formula presented.) 0.28% compared to other baselines. Extensive results have demonstrated that our method can weaken the effects of noisy labels on medical image segmentation. © 2024 John Wiley & Sons Ltd.

关键词： deep learning medical image segmentation noisy labels sample selection

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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A structure based on fiber-optic gyroscopes for gravity measurement 2

A structure based on fiber-optic gyroscopes for gravity meas...

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Advanced Optical Manufacturing Technologies and Applications 2022, AOMTA 2022 and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2022

作者： Chen, Yanjun Zhu, Lanxin Wang, Wenbo He, Yan Cao, Yuwen Huang, Huimin Ma, Xiangdong Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronics Engineering & Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510661264

We propose a structure based on fiber-optic gyroscopes for gravity measurement in accordance with the tilt-coupling effect, where Sagnac effect can remove the coupling between translational and rotational motion in the tilt-coupling effect. A fiber-optic gyroscope with a sensitivity of 2 × 10−11 rad/s2/√Hz in sub-millihertz frequencies is reported, which can be utilized to measure the rotational motion in gravity measurement. The application of dual-polarization configuration improves the adaptability of exploration environment. © 2023 SPIE. All rights reserved.

关键词： Fiber optics

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Traffic Prediction- and Explainable Artificial Intelligence-Based Dynamic Routing in Software-Defined Elastic Optical networks

Traffic Prediction- and Explainable Artificial Intelligence-...

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IFIP Networking Conference

作者： Róża Goścień Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783903176638

ISBN: (纸本)9798350390605

The recent research reveals a high potential and efficiency of machine learning (ML) algorithms applied to aid the optimization of telecommunication networks. Moreover, thanks to the tools of explainable artificial intelligence (XAI), it is possible to better understand and interpret the decisions of ML models, identify the features influencing them the most, and use that knowledge for further targeted optimization improvements. In this paper, we use these approaches to aid the optimization of the well-known and essential problem of dynamic routing in software-defined elastic optical networks (EONS). We use the XAI to verify which transmission and network state parameters influence the performance the most (expressed as a bandwidth blocking probability (BBP)) of a dynamic EON. The analysis reveals that two of the most relevant features are related to characteristics of the incoming traffic requests - the source node and the traffic prediction for the demand's source-destination nodes. Next, we use that data to propose six traffic prediction-and XAI-based demands ordering policies, which are used to improve a benchmark allocation algorithm. Then, we perform simulations to evaluate the policies' efficiency and compare them with reference methods. The results show that our targeted proposal is exceptionally efficient - the best policy allowed to reduce the BBP up to about 6.1%, representing 4203 Gbps of more served traffic compared to the best reference policy.

关键词： Machine learning algorithms Explainable AI Optical design Optical fiber networks Routing Prediction algorithms Resource management

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Secure Resource Allocation Protocol (SecRAP) for Time-Sensitive Networking

Secure Resource Allocation Protocol (SecRAP) for Time-Sensit...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Lukas Osswald Steffen Lindner Lukas Bechtel Tobias Heer Michael Menth University of Tuebingen Chair of Communication Networks Tuebingen Germany Esslingen University Computer Science and Engineering Esslingen Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The convergence of operational technology (OT) and information technology (IT) networks through Time-Sensitive Networking (TSN) promises enhanced efficiency and new use cases in industrial settings. However, ensuring security in this shared infrastructure is crucial to prevent potential attacks that could compromise Quality of Service (QoS) of real-time streams and pose risks to operations and safety. This paper focuses on auditing the security of the Resource Allocation Protocol (RAP), a distributed QoS signaling protocol for TSN. We analyze the vulnerability of RAP to attacks during admission control, where end stations request network resources for data transmission. We leverage the Dolev-Yao attacker model to assess the security properties of RAP in both distributed hop-by-hop admission control and hybrid admission control with a central controller. We introduce novel security extensions to RAP, called Secure Resource Allocation Protocol (SecRAP), to mitigate the identified attack vectors. Finally, we present a prototype and discuss the security properties of SecRAP.

关键词： Protocols Admission control Prototypes Vectors Real-time systems Security Resource management Protection Streams Manufacturing automation

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Verifier-local revocation group signatures with backward unlinkability from lattices

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Frontiers of Information Technology & Electronic Engineering 2022年第6期23卷 876-892页

作者： Yanhua ZHANG Ximeng LIU Yupu HU Yong GAN Huiwen JIA College of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou 450001China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Stale Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China College of Information Engineering Zhengzhou University of TechnologyZhengzhou 450044China School of Mathematics and Information Science Guangzhou UniversityGuangzhou 510006China

For group signature(GS)supporting membership revocation,verifier-local revocation(VLR)mechanism seems to be a more flexible choice,because it requires only that verifiers download up-to-date revocation information for signature verification,and the signers are not *** a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions,the first lattice-based VLR group signature(VLR-GS)was introduced by Langlois et al.(2014).However,none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability(BU),which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer(i.e.,member)is *** this study,we introduce the first lattice-based VLR-GS scheme with BU security(VLR-GS-BU),and thus resolve a prominent open problem posed by previous *** new scheme enjoys an O(log N)factor saving for bit-sizes of the group public-key(GPK)and the member’s signing secret-key,and it is free of any public-key *** the random oracle model,our scheme is proven secure under two well-known hardness assumptions of the short integer solution(SIS)problem and learning with errors(LWE)problem.

关键词： Group signature Lattice-based cryptography Verifier-local revocation Backward unlikability Short integer solution

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Electromagnetic metamaterial agent

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Light(science & Applications) 2025年第1期14卷 118-130页

作者： Shengguo Hu Mingyi Li Jiawen Xu Hongrui Zhang Shanghang Zhang Tie Jun Cui Philipp del Hougne Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing100871China National Key Laboratory for Multimedia Information Processing School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Pazhou Laboratory(Huangpu) GuangzhouGuangdong510555China Univ Rennes CNRSIETR-UMR 6164F-35000RennesFrance

Metamaterials have revolutionized wave control;in the last two decades,they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified *** deep-learning techniques play an increasingly important role in metamaterial inverse design,measurement post-processing and end-to-end optimization,their role is ultimately still limited to approximating specific mathematical relations;the metamaterial is still limited to serving as proxy of a human operator,realizing a predefined ***,we propose and experimentally prototype a paradigm shift toward a metamaterial agent(coined metaAgent)endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks,including electromagnetic(EM)field manipulations and interactions with robots and *** recently released foundation models,metaAgent reasons in high-level natural language,acting upon diverse prompts from an evolving complex ***,metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism,where agents are domain experts in sensing,planning,grounding,and *** response to live environmental feedback within a real-world setting emulating an ambient-assisted living context(including human requests in natural language),our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a *** masters foundational EM manipulation skills related to wireless communications and sensing,and it memorizes and learns from past experience based on human feedback.

关键词： agent prompt reasoning

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