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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191-200 订阅

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Reinforcement learning Based optimization Algorithm for Resource Offloading and Load Balancing in Connected Vehicles 4

Reinforcement Learning Based Optimization Algorithm for Reso...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zhao, Qian Duan, Zhanhao Guan, Shaoyun Harbin University of Commerce School of Computer and Information Technology Harbin China

ISBN: (纸本)9798350394375

Edge computing in Internet of vehicles accomplishes the objectives of low latency and energy consumption by offloading tasks to edge computing servers. However, how to reduce the task offloading latency and energy consumption of vehicle terminals while ensuring the priority of server computing and server load balancing is still a challenge. To solve this problem, this paper first constructs the channel model, communication model, delay model, load balancing model, and target optimization model of the Internet of Vehicles edge computing, and uses non-orthogonal multiple access (NOMA) communication technology. then the paper proposes an improved Q-learning algorithm based on genetic factors (GAQ-learning algorithm) to calculate the vehicle task offloading scheme. Finally, comparative experiments were conducted between the proposed solution and various schemes. Simulation results demonstrate that the proposed solution can effectively reduce the average offloading latency of users, optimize the workload between servers, and effectively reduce energy consumption, with improved performance compared to three base offloading schemes. © 2024 IEEE.

关键词： learning algorithms

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Day-ahead Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Harris Hawk optimization 4

Day-ahead Optimal Power Flow with Stochastic Wind and Solar ...

引用

4th IEEE international conference in Power Engineering applications, ICPEA 2024

作者： Jaiswal, Supriya Maheshwari, Ankur Singh, Mukesh NIT Hamirpur Electrical Engineering Department Himachal Pradesh India

ISBN: (纸本)9798350344578

the increasing integration of renewable energy sources (RESs), such as wind and solar power, contributes to the escalating unpredictability of electrical power systems. Ac- knowledging and accommodating the stochastic nature of these sources is paramount for accurate conclusions in power system analyses. Given the inadequacy of probability density functions in addressing uncertainty in Day-Ahead Optimal Power Flow (DA-OPF), this study employs a scenario-generating method via Monte Carlo simulations. Furthermore, it applies the Harris Hawks optimization (HHO) technique to optimize the day-ahead operation of a power system integrating stochastic wind and solar energy sources, focusing on minimizing operational costs. the analysis encompasses both standard and modified IEEE 30-bus configurations. the findings illustrate the efficacy and efficiency of the proposed algorithm in tackling the intricate challenge posed by RESs uncertainty. through the amalgamation of scenario generation techniques and optimization algorithms, this study presents a viable approach to manage the inherent complexity of power networks incorporating stochastic RESs. © 2024 IEEE.

关键词： Solar power plants

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A review implying an in-depth exploration of deep learning and its applications 4th

A review implying an in-depth exploration of deep learning a...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Arora, Apoorva Kaur, Amandeep Kaur, Ramanpreet Gautam, Vinay Chandigarh Engineering College Punjab Landran India Chitkara University Rajpura India

ISBN: (纸本)9781032911571

the paper gives a complete insight into deep learning, along with its diverse applications. Deep learning uses neural networks with 3 or more levels to imitate human brain behavior. As the paper proceeds, it gives in-depth knowledge along with historical developments, moving towards the importance as well as applications of neural networks. the paper further discussed various architectures of different neural networks, for instance, Convolutional neural networks, Multilayer Perceptron, and Radial basis function neural networks. Moving further it compares machine learning traditional methods with deep learning *** this comprehensive study, readers are bound to gain insight into concepts and fundamentals of usage of deep learning in the real world. © 2025 the Author(s).

关键词： Adversarial machine learning

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Cloud Resource Allocation using Deep learning Techniques-A Study 4

Cloud Resource Allocation using Deep Learning Techniques-A S...

引用

4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Mahalakshmi, V. Poornima, V. Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331515720

the current era of information processing, communication, and technological advances provides resources to clients on demand. the client and service provider determine how much need there is for the resources. the fitness function and resource management strategy determine the best way to allocate cloud resources. Cloud Computing (CC) is a popular technology these days with the use of centralised computing facilities. Clients can utilize these resources to finish calculations, and the CC center will provide the user with the program's processed results. CC is useful for both consumer and business users. Users can save computing expenditures by avoiding the need to buy numerous PCs by investing in a cloud server. In huge-scale distributed computing, allocation of resources is essential as networks of machines face challenging optimization issues. this study is to analyse and evaluate the cloud resource optimization techniques such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), (Long Short term memory (LSTM) and Reinforcement learning (RL) for the allocation of resources in cloud. © 2024 IEEE.

关键词： Cloud platforms

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D2D Resource Allocation optimization Algorithm Based on Deep Reinforcement learning 4

D2D Resource Allocation Optimization Algorithm Based on Deep...

引用

4th international conference on Electronic Materials and Information Engineering (EMIE)

作者： Zheng, Kaijin Wang, Yijun Zhuang, Dawei Zhou, Jialu Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun Peoples R China

ISBN: (纸本)9798350390230;9798350390223

In the cellular network framework, the resource allocation problem for Device-to-Device (D2D) communication technology is particularly critical. the core issue is how to efficiently allocate channel resources to D2D users while minimizing interference to cellular users (CU), ensuring the overall performance and Quality of Service (QoS) of the network. To address this challenge, this letter has proposed a resource allocation strategy based on deep reinforcement learning algorithms, aimed at dynamically adjusting channel allocation and transmission power in D2D communication. Firstly, an in-depth study and analysis of the conflicts and interference issues between D2D communication and cellular users in the cellular network environment regarding channel reuse are conducted. By quantitatively analyzing the impact of signal interference on communication quality, key factors requiring optimization are identified. Secondly, Double-Actor Critic (D-AC) algorithm is developed for dynamically allocating communication channels and adjusting transmission power. this algorithm iteratively optimizes D2D channel reuse and power control strategies, aiming to balance the efficient use of channel resources with the minimization of signal interference. thirdly, the D-AC algorithm is implemented and evaluated for its effectiveness in enhancing system throughput, reducing collision probabilities, and maintaining quality of service standards. By comparing with existing resource management schemes, the advantages of the D-AC algorithm are demonstrated.

关键词： Device to Device (D2D) Deep Reinforcement learning (DRL) power control resource allocation

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Machine learning based Dynamic Resource Allocation in 6G Network 4

Machine Learning based Dynamic Resource Allocation in 6G Net...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Pujar, Rajani S. Chyalakshmi, C.L. Kakkasageri, Mahabaleshwar S. Basaveshwar Engineering College Electronics and Communication Engineering Department Karnataka Bagalkote587102 India

ISBN: (纸本)9798350349900

the 6G network is a new generation of wireless technology that builds on the 5 G network's advancements. It aims to improve speed, connectivity, and performance, with high data rates and broad frequency bands. It offers higher capacity and lower latency. Dynamic resource allocation in 6G networks will likely heavily leverage machine learning (ML) techniques due to the complexity and scale of these networks. the resource requirements of devices in 6G network are variable in nature. Dynamic resource allocation is crucial in 6G networks, and machine learning algorithms help to manage resource allocation efficiently. By leveraging machine learning techniques in dynamic resource allocation, 6 G networks can achieve higher efficiency, flexibility, and adaptability to meet the diverse requirements of emerging applications and services. A machine learning algorithm used in this paper showed higher resource utilization and success rate than existing methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Locally Linear Embedding Based on Gannet optimization Algorithm 4

Locally Linear Embedding Based on Gannet Optimization Algori...

引用

4th IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhu, Yiqi Yang, Yang Fang, Yilin Nai, Wei Yang, Zan Li, Dan Huzhou College School of Electronic Information Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Huzhou College Public Teaching and Research Department Huzhou China

ISBN: (纸本)9798350363586

the analysis and feature extraction of high-dimensional data can help people solve various problems in current industry applications, and manifold learning is a type of data dimensionality reduction method developed based on the concept of topological manifolds. Locally linear embedding (LLE) algorithm, as a classic manifold learning algorithm, has attracted much attention due to its low computational complexity and strong scalability. However, the objective function of LLE algorithm may contain absolute value terms and cannot be guaranteed to be convex. Traditional derivative dependent solving methods are difficult to obtain its optimal solution, making it impossible to achieve effective feature extraction of data. In this paper, an LLE based on gannet optimization algorithm (GOA) has been proposed, and it is supposed to be used as a derivative-free optimization method to effectively solve the problems of non-differentiable of non-convex objective function, premature convergence, and easy to fall into local optimum in the process of solving the objective function of LLE. © 2024 IEEE.

关键词： optimization algorithms

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Auto-tuning of Large-Scale Parallel Computing applications Based on Bayesian optimization 4

Auto-tuning of Large-Scale Parallel Computing Applications B...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jinhu Sun, Bin Sun, Jianpeng Yu, Hao Xie, Xiguo Zhengzhou University Zhengzhou China Co. Ltd. Beijing China

ISBN: (纸本)9798331531225

the performance of large-scale parallel computing applications is highly dependent on the parameter settings within complex systems. Due to the high-dimensional and nonlinear nature of kernel environment parameter spaces, traditional manual tuning methods are both labor-intensive and inefficient when exploring optimal parameter combinations, often leading to suboptimal performance. To address this issue, this paper proposes an automated parameter tuning framework based on Bayesian optimization, aimed at efficiently exploring complex high-dimensional parameter spaces and predicting better-performing parameter combinations. this framework builds a surrogate model, iteratively selects potentially efficient parameter configurations, and integrates SHAP to analyze the influence of each parameter on performance optimization. We validated the effectiveness of this framework in several typical large-scale parallel computing application scenarios, including NAS Parallel Benchmarks (NPB), quantum chemistry simulation software Quantum ESPRESSO and VASP, and the Weather Research and Forecasting model (WRF). Experimental results show that Bayesian optimization significantly shortens the overall tuning time and performs traditional methods such as genetic algorithms in terms of performance optimization. Experimental results show that Bayesian optimization significantly shortens the overall tuning time by 50% and improves application performance by up to 11%, outperforming traditional methods such as genetic algorithms in terms of performance optimization. Furthermore, SHAP analysis reveals the importance of key kernel parameters in enhancing application performance. © 2024 IEEE.

关键词： Bayesian networks

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Deep learning Optimizers for Histological Image Classification: A Comparative Study 10

Deep Learning Optimizers for Histological Image Classificati...

引用

10th international conference on optimization and applications, ICOA 2024

作者： Haggouni, Jamal Azzouzi, Salma El Hassan Charaf, Moulay Ibn Tofail University LaRI Laboratory Faculty of Sciences Kenitra Morocco

ISBN: (纸本)9798350387353

Over the last few years, there has been a great deal of progress in the field of image recognition, using classical or modern methods based on machine learning algorithms. In this context, numerous studies on object detection have been carried out in various fields, with promising results. the aim of this paper is the design of an automatic classification system for tumor tissue cells (breast cancer) in medical images (histological images). the proposed approach relies on the use of convolutional neural networks (CNNs), with numerous experiments using different optimization algorithms such as RMSprop, SGD or Adam to evaluate the performance of our approach. the findings show the effectiveness of the RMSprop optimizer, with a fast convergence rate and high accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A Reinforcement learning-based Adaptive Digital Twin Model for Forests 4

A Reinforcement Learning-based Adaptive Digital Twin Model f...

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4th international conference on Applied Artificial Intelligence (ICAPAI)

作者： Damasevicius, Robertas Maskeliunas, Rytis Vytautas Magnus Univ Akademija Lithuania

ISBN: (纸本)9798350349764;9798350349771

We present a Reinforcement learning-based Adaptive Digital Twin (RL-ADT) model designed for forest ecosystems, utilizing advanced IoT data collection and spatiotemporal graph modeling. It focuses on dynamically representing forests for optimized health resource management and sustainability, simulating environmental interactions, and adapting to changing conditions for real-time monitoring and efficient resource usage. the implementation of the model substantially improved the energy and resource efficiency of the digital twin. the construction of spatiotemporal graphs within the model has led to a more accurate and precise representation of the complex interactions within forest ecosystems. this improvement in model fidelity is crucial to understanding and managing the dynamic nature of forests effectively. the adaptability of the RL algorithms is instrumental in managing the dynamic aspects of forests. the RL algorithm has optimized the trade-off between model accuracy and computational overhead, which is vital for the real-time application of the model in forest management. the insights gained from this study have substantial implications for the sustainable management of forest resources. By improving efficiency in resource use, technology aligns closely with sustainability goals and responsible stewardship of natural resources.

关键词： Digital Twin Forest Ecosystem Management Reinforcement learning Spatiotemporal Data Analysis Real-time Ecosystem Modeling Resource optimization in Forestry

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