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Reputation based Adaptive Federated Learning in Medical Scenarios 9

Reputation based Adaptive Federated Learning in Medical Scen...

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9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Liu, Tianrui Wang, Yingxiaochun Wang, Xinlei Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence. © 2024 IEEE.

关键词： Federated learning

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Optimization of Aluminum Electrolytic Process Parameters Based on Parallel Tumbleweed Algorithm 11

Optimization of Aluminum Electrolytic Process Parameters Bas...

引用

11th Joint international Information technology and Artificial Intelligence conference, ITAIC 2023

作者： Minke, Huang Information Institute North China University of Technology Beijing China

ISBN: (纸本)9798350333664

In order to enhance the optimization capability of process parameters in aluminum electrolysis production, this paper proposes a Parallel Tumbleweed Algorithm (PTA) based on parallel computing. The Tumbleweed Algorithm(TA) is prone to local convergence during the iteration process. The PTA algorithm achieves parallel processing between populations through two communication strategies. First, the optimal values are exchanged between two subpopulations. Second, in a three-subpopulation scenario, a portion of individuals in one subpopulation is replaced with the best individuals from the other two subpopulations. These two types of exchanges allow for the transfer and sharing of the optimal values among the subpopulations. Applying PTA to solve the CEC2017 test functions and comparing it with various intelligent optimization algorithms. The results show that this algorithm has better optimization performance and competitiveness. Finally, applying the PTA algorithm to the aluminum electrolysis parameter optimization problem validates the practicality of the algorithm. © 2023 IEEE.

关键词： aluminum electrolysis CEC2017 Parallel Tumbleweed Algorithm Parameter Optimization

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Modernizing Finance, HR: AI, IoT, and Cloud Solutions for Enhancing Efficiency and Employee Satisfaction

Modernizing Finance, HR: AI, IoT, and Cloud Solutions for En...

引用

2024 IEEE international conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vijayakanthan, S. Nesakumari, J. Navena Uma, S. Lekshmi, R.S. Sathish, A. Avinash, S. SRM Institute of Science and Technology Department of Management Studies Vadapalani Tamil Nadu Chennai India Velammal Engineering College Department of Management Studies Tamil Nadu Chennai India BS Abdur Rahman Crescent Institute of Science and Technology Crescent School of Business Vandalur Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Management Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331539948

By fixing the flaws in conventional systems, the convergence of Internet of Things (IoT), cloud computing, and Artificial intelligence (AI), technologies are revolutionizing financial HR practices. The manual procedures and static data of legacy HR systems make it difficult for them to match the demands of modern business, which results in inefficiencies and lost opportunities. The study proposes a comprehensive system in response that makes use of AI-driven predictive analytics, scalable cloud infrastructure, and real-time data collection via IoT devices. The technique improves employee happiness, forecasts trends, and allocates resources optimally to enable proactive personnel management. Based on the pilot results, HR operational efficiency has significantly improved. Processing times have decreased (HR requests processed in 3 days compared to 6 hours), accuracy has increased (payroll data accuracy increased from 95% to 98%), and staffing efficiency has increased from 80% to 90%. The potential of modern technology to reinvent talent management procedures in finance HR is highlighted by the revolutionary approach. © 2024 IEEE.

关键词： Decentralized finance

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The convergence of IoT and 5G: Transforming Connectivity in Smart City Applications 2

The Convergence of IoT and 5G: Transforming Connectivity in ...

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Chowdhary, Yuvraj Singh Singh, Geetika Netaji Subhas Univ Technol Dept Comp Sci New Delhi India Indira Gandhi Delhi Tech Univ Women Dept Informat Technol New Delhi India

ISBN: (纸本)9798331540661;9798331540678

5G technology is a noteworthy achievement in developing wireless communication, offering unparalleled speed, little delay, and extensive connection. This article examines the profound influence of 5G on the (IoT), emphasizing how 5G's advanced features enable the smooth incorporation of IoT devices in several industries. 5G facilitates real-time data processing and transmission, which is essential for applications in smart cities, healthcare, industrial automation, and autonomous cars, due to its improved data transfer speeds and dependability. The study explores the technological connections between 5G and IoT, analyzing how network slicing, edge computing, and massive MIMO enhance the effectiveness and scalability of IoT systems. Moreover, it examines the difficulties and possibilities brought about by this merging, including issues of security, distribution of spectrum, and the need for novel regulatory structures. In conclusion, this article highlights the capacity of 5G to completely transform the (IoT), by stimulating progress and promoting a more interconnected and smarter global society.

关键词： 5G Internet of Things (IoT) Connectivity Low latency Real-time data processing Smart cities

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An Effective Fire Detection Algorithm Based on Improved YOLOv7 24

An Effective Fire Detection Algorithm Based on Improved YOLO...

引用

6th international conference on Control and Computer Vision, ICCCV 2024

作者： Shen, Jing Wang, Xue Alghazzawi, Daniyal Aldhaheri, Sahar Business School Hubei University Hubei Wuhan430062 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Shandong Qingdao266061 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9798400718045

In the critical domain of fire detection, essential for safeguarding public safety and industrial assets, traditional methods have struggled with issues like low accuracy and slow response times, compromising real-time detection capabilities. Our response to this challenge is the development of a novel algorithm built upon the enhanced YOLOv7 framework, which integrates the CBS module with the cutting-edge ConvNeXtBlock to form the CN-B network module—a design that allows for effective longitudinal and transverse normalization, expanding the receptive field to capture a more comprehensive set of global information without the penalty of increased memory usage. This is further bolstered by the introduction of the SIoU loss function, which dramatically improves model convergence speed, ensuring a swift and accurate learning process. Rigorous testing on the BoWFire dataset has proven the algorithm’s superior performance, boasting an average fire detection accuracy of 89.74% and improving detection speed by 2.9 milliseconds per image, outperforming the original YOLOv7 network by a substantial margin and demonstrating a 2.1% increase in precision. Comparative experiments with eight leading networks validate our model’s adherence to the stringent requirements of real-time and accurate fire detection, positioning our improved YOLOv7 network as a significant advancement in the field of fire detection technology. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Fires

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Mobile resources big data deduplication and offloading algorithm based on new computing network architecture 3

Mobile resources big data deduplication and offloading algor...

引用

3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Xiong, Qi Computer and Information Engineering Guangzhou Huali College Guangzhou511325 China

ISBN: (纸本)9781510661301

The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture. © 2023 SPIE.

关键词： Network architecture

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A Structural Reliability Analysis Method Based on Ensemble Learning and Active Learning 24

A Structural Reliability Analysis Method Based on Ensemble L...

引用

9th international conference on Big Data and computing, ICBDC 2024

作者： Li, Bingyi Long, Jiahui Jia, Xiang Chen, Dahao Wang, Miao Zhu, Lin College of Systems Engineering National University of Defense Technology China

ISBN: (纸本)9798400718205

The structural reliability of systems is important, and the security of the system in the current big data context is of even greater concern. Traditional structural reliability analysis methods mainly focus on sampling and indicator estimation to control and monitor the performance of systems. A series of adaptive Kriging methods are very popular, but there is space improvement in improving the prediction accuracy of reliability indicators and reducing the cost of calling expensive true system or finite elements. In this paper, we propose an integrated ensemble learning function (ELF) method that can realize the synthesis of multiple learning functions, and the dual stability-based termination criterion for iterative addition of points is applied to improve convergence efficiency. Then the prediction of reliability indicators is conducted by the converged Kriging model. A highly nonlinear numerical function and an engineering practical nonlinear oscillation system are analyzed based on the procedure for the proposed method. Compared with the single learning function method consisted of ELF, the results show that the proposed method can realize more accurate and low-cost prediction for failure probability with more representative learning points added. © 2024 ACM.

关键词： Adversarial machine learning

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End-to-end Lane-changing Decision-making Control Method for Unmanned Driving based on Proximal Optimization Algorithm 3

End-to-end Lane-changing Decision-making Control Method for ...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Wang, Shuqing Huang, Zhiqing Zhang, Chenyang Beijing University of Technology Beijing China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

End-to-end driving behavior decision-making is a research hotspot in the field of driverless driving. This paper studies the end-to-end lane change decision control based on the PPO (Proximal Policy Optimization) deep reinforcement learning algorithm. First, an end-to-end decision control model based on PPO algorithm is established. The model uses the information perceived from the environment as the input state and outputs the control quantity (acceleration, braking, steering). Training and verification in the driving environment under the TORCS (The Open Racing Car Simulator) platform show that the model can achieve end-to-end lane change driving behavior decision-making. Finally, compared with the DDPG (Deep Deterministic Policy Gradient) model, which is also a deep reinforcement learning method, the experimental results show that the PPO lane change decision control model has a faster convergence rate. © 2023 SPIE.

关键词： Reinforcement learning

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Interpretable Machine Learning Model for Breast Cancer Prediction Using LIME and SHAP 9

Interpretable Machine Learning Model for Breast Cancer Predi...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Maheswari, B Uma Aaditi, A. Avvaru, Ananya Tandon, Aryan De Prado, R. Pérez Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India University of Jaén Dept. Telecommunication Engineering Jaén23700 Spain

ISBN: (纸本)9798350394474

Breast cancer, which occurs in both men and women, causes approximately 10 lakh deaths globally and has no specific risk factors. The time frame of the treatment is a long-drawn process based on the person, the type of cancer, and its level of spread. It is imperative to detect this cancer early on in order to prevent mortality. Given the prediction's significance, an accurate breast cancer prediction model must be developed. This study explores the Breast Cancer Prediction dataset, applies SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset, and proposes an effective Machine Learning (ML) model fused with Explainable AI to provide health professionals with explanations. ML algorithms are analyzed before and after applying Principal Component Analysis (PCA), and visualization is performed using t-SNE. ML algorithms, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF), Stochastic gradient Descent, XGBoost, Gradient Boosting, Decision Tree (DT), and Naïve Bayes are trained on the Breast Cancer dataset. It is seen that the RF model outperforms other models considered with 95.9% accuracy. To understand the weightage of features considered by the best breast cancer prediction models and to provide trust to the doctors, the Explainable AI (XAI) packages LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) are used. The effective ML model with XAI techniques empowers clinicians with actionable insights for more informed breast cancer diagnosis and treatment decision-making. © 2024 IEEE.

关键词： Principal component analysis

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Concurrent Multipath Transmission for ultra-Reliable and Low Latency with Deep Reinforcement Learning 23

Concurrent Multipath Transmission for ultra-Reliable and Low...

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4th international conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Chen, Tan Ai, Jiahao Xiong, Xin Tan, Fuxing Faculty of Information Technology Beijing University of Technology China School of Information Technology Beijing City University China

ISBN: (纸本)9798400700705

Concurrent multipath transmission combining FEC is an effective approach to achieve ultra-reliable low latency communication in modern network. Rate allocation algorithm which distributing packets over multiple parallel paths plays a critical role in this architecture. However, traditional heuristic algorithm becomes less applicable due to great computational burdens and increasing demands for real-Time. In this paper, we propose a deep reinforcement learning based algorithm NeuroCMT to solve this problem. Moreover, in order to deal with large discrete action space problem in NeuroCMT, we introduce a method with low complexity and high generalization ability. Furthermore, we implement prioritized experience replay technique in DDPG to attain better sample efficiency and enhanced convergence speed utilizing prioritized sampling. Extensive experiments are performed and the evaluation results demonstrate clearly that our approach achieves high reliability as well as low latency effectively. © 2023 ACM.

关键词： Reinforcement learning

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