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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是91-100 订阅

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A Comparative Study of Various optimization Techniques used in Wireless Sensor Networks 4

A Comparative Study of Various Optimization Techniques used ...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Tiwari, Abha Jain, Nitin Kumar, Manish Babu Banarasi Das University Lucknow India Symbiosis University of Applied Sciences Indore India

ISBN: (纸本)9798331518981

the applications of wireless sensor networks (WSNs) in a variety of industries, including smart cities, health care, and environmental monitoring, have drawn a lot of attention. Nevertheless, in order to increase network performance, prolong lifespan, and boost efficiency, optimization techniques must be used due to sensor nodes' restricted resources. this work covers different optimization strategies used in WSNs and groups them into three categories: algorithms inspired by nature, heuristic approaches, and classical optimization strategies. We give a thorough taxonomy of these techniques, highlight important findings from the research, talk about the difficulties that have been faced, and suggest possible avenues for future investigation. © 2024 IEEE.

关键词： Ant colony optimization

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Blockchain-Enabled Privacy-Preserving Federated learning algorithms in Edge Computing 4

Blockchain-Enabled Privacy-Preserving Federated Learning Alg...

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4th international conference on Computer Science and Blockchain, CCSB 2024

作者： Zhao, Jinyang School of Cyber Science and Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350356502

Aiming at the problems of privacy leakage of model parameters, untrustworthy servers that may return incorrect aggregation results, and users participating in training that may upload incorrect or low-quality model parameters during federated learning conducted in edge computing scenarios, proposing a Blockchain-Enabled Privacy-Preserving Federated learning algorithms in Edge Computing. specifically speaking, Each device is trained locally using a sharpness-aware minimization optimization algorithm, and then local updates with differential privacy noise are uploaded to the associated edge node to protect device data privacy and reduce the impact of noise on local updates. Using blockchain to achieve system decentralization and designing a consensus mechanism combining edge nodes based on Byzantine algorithm, the scoring system can effectively exclude low-quality or malicious model parameter updates by verifying the scoring system. Experimental results show that the algorithm in this paper has high accuracy in and can resist 30% of poisoning attacks. © 2024 IEEE.

关键词： Federated learning

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A supervised generative optimization approach for tabular data 23

A supervised generative optimization approach for tabular da...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Hamad, Fadi Nakamura-Sakai, Shinpei Obitayo, Saheed Potluru, Vamsi K. Univ Pittsburgh Pittsburgh PA 15260 USA Yale Univ New Haven CT USA JP Morgan AI Res New York NY USA

ISBN: (纸本)9798400702402

Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specific data sets and use cases remains challenging. Moreover, the majority of existing approaches are "unsupervised" in the sense that they do not take into account the downstream task. To address these issues, this work presents a novel synthetic data generation framework. the framework integrates a supervised component tailored to the specific downstream task and employs a meta-learning approach to learn the optimal mixture distribution of existing synthetic distributions.

关键词： Synthetic data generation Bayesian Optimisation Meta-learning Fraud Detection

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A Comprehensive Analysis of Secure Attack Detection on Virtual Machines in Cloud Computing 4

A Comprehensive Analysis of Secure Attack Detection on Virtu...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Nemirajaiah, Naveen Kumar Adalagere Raju, Channa Krishna Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumakuru India Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Artificial Intelligence and Machine Learning Tumakuru India

ISBN: (纸本)9798350352931

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual Machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. the VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. this detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale optimization algorithm-based Differential Evolution (M-WODE), Gene Expression Programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. the performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches. © 2024 IEEE.

关键词： Genetic algorithms

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Arithmetic optimization Algorithm Improved by Multi-Strategy Fusion 24

Arithmetic Optimization Algorithm Improved by Multi-Strategy...

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4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Feng, Chun Cai, Maoguo Luo, Jiaxin Li, Bin College of Electronic and Information Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400710247

To address the issues of slow convergence, low accuracy, and vulnerability to local optima in the Arithmetic optimization Algorithm (AOA), this paper introduces the multi-strategy fused improved Arithmetic optimization Algorithm (CLSAOA). Initially, the Circle chaotic mapping is utilized for population initialization, thereby augmenting diversity and balance within the initial population. then, the lens imaging opposition-based learning strategy is incorporated into the optimization phase of AOA to improve the algorithm's exploration capabilities and its ability to escape local optima, thereby enhancing the convergence accuracy and speed. Finally, a sine-cosine strategy is added to perturb the particle positions, further improving the algorithm's convergence accuracy. Simulation experiments compare with other improved AOA algorithms, the original AOA algorithm, and other metaheuristic algorithms using international benchmark test functions for optimization. the findings indicate that the CLSAOA algorithm exhibits superior performance in terms of both convergence precision and speed. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Auto Tuning Smart optimization Algorithm for Mobility Management in Heterogeneous Networks 4

Auto Tuning Smart Optimization Algorithm for Mobility Manage...

引用

4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Meenakshi Chandigarh University ECE Department Mohali India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528171

As modern telecommunications systems face escalating demands for seamless connectivity and efficient resource allocation, the need for adaptive mechanisms to optimize network performance becomes increasingly crucial. Traditional mobility management techniques often struggle to cope with the dynamic nature of user mobility, necessitating novel approaches to address these challenges. Auto-tuning self-optimization algorithms leverage machine learning and optimization strategies to autonomously adjust network parameters based on real-time data and performance metrics. By continuously optimizing network configurations, these algorithms can adapt to fluctuating network conditions, thereby enhancing system efficiency and user experience. this paper aims to provide a comprehensive review of the principles, implementations, and performance evaluations of auto-tuning algorithms in the context of mobility management in LTE-A and 5G HetNets. through an in-depth analysis of existing literature and case studies, this review paper explores the effectiveness of auto-tuning self-optimization algorithms in addressing the complexities of dynamic user mobility in modern wireless networks. By shedding light on the operational mechanisms and benefits of these algorithms, this study contributes valuable insights to the ongoing discourse on network optimization and management. Ultimately, the findings presented in this review paper aim to inform future research directions and advancements in the field of mobile communication systems, paving the way for enhanced network performance and user satisfaction in LTE-A and 5G HetNets. © 2024 IEEE.

关键词： 5G mobile communication systems

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Cost-sensitive feature selection based on Adaptive Hunting optimization 4

Cost-sensitive feature selection based on Adaptive Hunting O...

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4th international conference on Computer Communication and Artificial Intelligence (CCAI)

作者： Liang, Yixuan Xian Univ Technol Sch Sci Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9798350362770;9798350362763

For machine learning tasks, the explosive growth of data makes feature selection be an essential preprocessing step, as it improves generalization capability, reduces run-time and model's complexity. Traditional feature selection methods select the informative subset to facilitate the classification accuracy. However, in real applications, the cost of collecting the features shall be taken into account. this paper proposes a new costsensitive feature selection method based on an adaptive optimization framework. Unlike most of the existing methods simply adding or deleting features one by one, the proposed method uses an adaptive swarm intelligence algorithm to search the optimal subset. this algorithm achieves a more reasonable balance between the exploration and exploitation utilizing a cosine congestion factor, and is employed in cost-sensitive feature selection problem. Both of test cost and misclassification cost are considered, then a new and more reasonable comprehensive evaluation criteria is proposed. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed methods.

关键词： feature selection cost-sensitive learning evolutionary computation

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Research on Music Soundtrack Recognition Based on Deep Computer learning 24

Research on Music Soundtrack Recognition Based on Deep Compu...

引用

4th international conference on Computer, Internet of things and Control Engineering

作者： Yu, Yu Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9798400711848

In an era where information technology is increasingly prevalent, music score recognition, as a vital branch of intelligent audio processing, has garnered significant attention. It not only enhances the listening experience for users but also holds substantial importance in realms such as music copyright protection, automatic scoring, and intelligent editing. the advent of deep learning technologies in computing resembles a revolution, transforming traditional audio analysis methods and rendering the processing of complex audio data more efficient and precise. By leveraging advanced deep learning models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN), researchers can extract valuable insights from vast troves of musical data, gradually uncovering the underlying patterns hidden within notes and melodies. Particularly when confronted with an array of diverse forms and styles in musical compositions, deep learning techniques significantly enhance the accuracy and speed of score recognition, revealing extraordinary potential in applications such as automatic tagging, recommendation systems, and real-time audio processing.

关键词： Computer technology deep learning algorithms music soundtrack recognition

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Adaptive optimization of Shield Tunnel Segment Assembly Points by Integrating DBSCAN and Genetic Algorithm 4

Adaptive Optimization of Shield Tunnel Segment Assembly Poin...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Xu, Xiao Liu, Hu Shanghai Institute of Technology College of Rail Transit Shanghai China

ISBN: (纸本)9798331534622

In shield tunnel construction, segment assembly is a critical process, and its quality directly affects the overall performance and safety of the tunnel. Although the technology for shield tunnel segment assembly has developed, it still has limitations. Traditional methods for selecting assembly points often rely on experience or simple rules, making it difficult to adapt to the complex and dynamic construction *** algorithms are widely used in optimization problems, and some studies have applied them to weight optimization for segment assembly point selection. However, existing methods often use fixed interval classification, which lacks flexibility and cannot dynamically adjust based on the actual data distribution. this leads to classification errors, particularly when dealing with unevenly distributed *** address these issues, this paper proposes an innovative method that integrates DBSCAN (Density-Based Spatial Clustering of applications with Noise) and genetic algorithms, aiming to achieve adaptive optimization of shield tunnel segment assembly points. the goal is to improve the accuracy and adaptability of assembly point selection, thereby enhancing tunnel construction quality. © 2024 IEEE.

关键词： Genetic algorithms

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Design and optimization of Central Air Conditioning Systems Based on Digital Twin Technology 24

Design and Optimization of Central Air Conditioning Systems ...

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4th international conference on Internet of things and Machine learning

作者： Peng, Fan Hebei Univ Technol Internet Things Engn Tianjin Peoples R China

ISBN: (纸本)9798400710353

With the rapid development of Internet technologies, the Internet of things (IoT) has become a crucial bridge connecting the physical and digital worlds. As a significant branch of IoT applications, smart homes focus on automating and intelligently controlling the home environment through the interconnection and data exchange of smart devices. However, as the complexity of smart home systems increases, ensuring their efficient operation and meeting users' personalized needs has become a focal point of research. Digital twin technology, as an innovative solution, involves creating virtual replicas of physical entities to simulate, analyze, and optimize systems without disrupting their operations. this paper proposes a smart home control system design based on digital twins, aiming to achieve precise monitoring and intelligent control of the smart home environment through a highly integrated system architecture and modular design. this system not only improves energy efficiency but also autonomously learns from user behavior and preferences, thus offering more personalized services.

关键词： Digital twin smart home Central Air Conditioning System Design Energy Efficiency optimization User Behavior Analysis

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