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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

共 11563 条记录，以下是261-270 订阅

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An intelligent Controller System for SRv6 Network with Congestion Control Capability 4

An Intelligent Controller System for SRv6 Network with Conge...

引用

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

作者： Wang, Luman Wang, Sibo Huang, Zonghe Chen, Huiguang Yang, Kun Zhang, Xue China Telecom Research Institute China

ISBN: (纸本)9798350363586

As the scale of telecommunications operators' networks expands, the requirements for intelligent traffic scheduling and end-to-end Service Level Agreement (SLA) assurance for user services in the backbone network are gradually increasing. this paper introduces the design and implementation of an intelligent controller system based on the Segment Routing IPv6 (SRv6) network, focusing on the overall architecture of the intelligent controller based on the SRv6 network and its congestion control algorithm with priority assurance. the algorithm achieves rapid resolution of link congestion according to the steps of relay link matching, traffic matching, and path re-optimization, effectively addressing path optimization issues in the backbone network when facing traffic congestion alarms, and meeting the flexible scheduling needs of the backbone network. © 2024 IEEE.

关键词： Traffic congestion

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A Deep Q-learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

引用

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

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. this method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. the presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. the system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. the system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. this intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Composition Identification and Analysis of Ancient Glass Artifacts Based on Statistical Methods and Machine learning 4

Composition Identification and Analysis of Ancient Glass Art...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Liu, Yan Xie, Haifan Jin, Aitong Yang, Zicong Tan, Jianyi Chen, Liyun Zhuhai Coll Sci & Technol Sch Comp Sci Zhuhai Peoples R China Zhuhai Coll Sci & Technol Sch Big Zhuhai Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Glass was introduced to China from West Asia and Egypt through the early Silk Road. Its primary raw material is quartz sand, which has a high melting point. therefore, during refinement, a flux is added to lower the melting point. the performance of glass artifacts is influenced by different materials, making the analysis and identification of chemical composition crucial in the study of ancient Chinese glass. In this paper, non-parametric tests and machine learning techniques, including cluster analysis and KNN algorithm, are employed to analyze and identify a collection of ancient glass artifacts. this research investigated the relationship between the weathering condition of glass and its type, patterns, and colors, and predicted the chemical composition of these artifacts prior to weathering. Additionally, a detailed sub-classification and analysis of the chemical composition of glass artifacts is conducted to accurately identify different types of glass, focused on the classification patterns of two specific types of glass, namely high-potassium glass and lead-barium glass as well. Furthermore, the correlation between the chemical compositions of glass artifacts from different categories is also analyzed.

关键词： Glass composition identification Machine learning Linear regression prediction Independent sample test

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DeBERTaSSL: Enhancing Molecular Property Prediction with Self-Supervised learning 4

DeBERTaSSL: Enhancing Molecular Property Prediction with Sel...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Ghaayathri Devi, K. Bedadhala, Ravi Shankar Sachin Kumar, S. Soman, K.P. Bodapatiq, Jyotsana Devi Amrita School of Artificial Intelligence Coimbatore India Technology Research University Vignan's Foundation for Science Guntur India

ISBN: (纸本)9798350349900

this research introduces a novel approach to molecular property prediction, leveraging the transformative power of the DeBERTa model, a state-of-the-art transformer-based architecture. By integrating DeBERTa with graph convolutional neural networks (GCNs) and self-supervised learning techniques using fusion modality, our methodology achieves comprehensive representation learning from SMILES-encoded molecular structures. We combine graph-based and sequential representations to capture both local and global relationships within molecules, while incorporating self-supervised learning enables efficient learning from limited labeled data. through rigorous evaluation and comparison with existing methodologies, including Graph self-supervised learning, Graph Convolutional Network and others, our model DeBERTaSSL, demonstrates superior performance in predicting molecular properties across diverse datasets. this research contributes to advancing the field of molecular property prediction, offering a novel framework that combines the strengths of transformer-based models with graph-based representations and self-supervised learning techniques. © 2024 IEEE.

关键词： Self-supervised learning

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Comparison of Response Surface Methodology (RSM) and Machine learning Algorithms in Predicting Tensile Strength and Surface Roughness of AA8090/B4C Surface Composites Fabricated by Friction Stir Processing 4th

Comparison of Response Surface Methodology (RSM) and Machine...

引用

4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Adiga, Karthik Herbert, Mervin A. Rao, Shrikantha S. Shettigar, Arunkumar Shrivathsa, T., V Tapariya, Rahul Natl Inst Technol Karnataka Dept Mech Engn Surathkal India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Friction stir processing is an innovative solid-state process, widely utilized for surface composite fabrication, material property enhancement, and microstructural modification. Rotational speed, traverse speed, groove width, and axial force are key FSP parameters that improve the characteristics of surface composites (SCs). this work makes use of FSP to fabricate AA8090/B4C SCs by altering parameters within ranges. Response variables include ultimate tensile strength (UTS) and surface roughness (SR). Central composite design (CCD) of response surface methodology (RSM) leads trials, establishing a mathematical relationship between input parameters and UTS/SR. the models' adequacy is validated using ANOVA, which investigates the impact of input parameters on UTS and SR. this study also looks into machine learning regression methodologies for UTS and SR forecasting in AA8090/B4C SCs. the ML algorithms are evaluated by utilizing performance metrics like coefficient of determination (R-2) and root mean squared error (RMSE). Predicted UTS and SR values from RSM are compared with machine learning outcomes.

关键词： Machine learning Friction stir processing (FSP) Ultimate tensile strength (UTS) Surface roughness ANOVA

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Quantum-Inspired optimization Algorithms for Scalable Machine learning in Edge Computing 4

Quantum-Inspired Optimization Algorithms for Scalable Machin...

引用

4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Goyal, Rohit Kumar, Krishan Sharma, Vivek Bhutia, Rudramani Jain, Arpit Kumar, Munish School of Engineering and Computing Dev Bhoomi Uttarakhand University Uttarakhand Dehradun India Bhagat Phool Singh Mahila Vishwavidyalaya Department of Ece Haryana Sonipat India School of Computer Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering A.P. Vaddeswaram India

ISBN: (纸本)9798350387490

the convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edge devices poses challenges due to limited resources such as power, memory, and computational capability. Quantum-inspired optimization algorithms (QIOAs) offer a promising alternative to traditional optimization techniques, providing superior performance in constrained environments. this paper explores the application of QIOAs for scalable machine learning models in edge computing. We propose a novel hybrid quantum-inspired approach, benchmark it against classical algorithms, and demonstrate its efficacy in various edge computing environments. Experimental results show improved accuracy, reduced latency, and enhanced resource utilization. © 2024 IEEE.

关键词： optimization algorithms

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Fuzzy logic task scheduling optimization machine learning method to employ healthcare services in the cloud 4th

Fuzzy logic task scheduling optimization machine learning me...

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

作者： Geetha, K. Vijayalakshmi, Ponnan Department of Mathematics Periyar Maniammai Institute of Science and Technology Tamil Nadu Thanjavur India

ISBN: (纸本)9781032911571

this research study integrates machine learning and fuzzy logic methodologies to improve resource allocation and task scheduling in an AWS-based medical database. the ANN model has the highest performance with 0.9676 accuracy, followed by SVM, DT, and RF. the performance evaluation confirms the robustness of the proposed models. the study highlights the promising impact of advanced technologies in cloud-based medical databases, enhancing service quality and patient outcomes. Future studies can refine and develop new algorithms for real-time and predicted resource allocation and task scheduling in cloud-based healthcare systems. © 2025 the Author(s).

关键词： Adversarial machine learning

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Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement learning 24

Research on Autonomous Driving Decision-making Strategies ba...

引用

4th International conference on Internet of things and Machine learning

作者： Wang, Zixiang Yan, Hao Wei, Changsong Wang, Junyu Bo, Shi Xiao, Minheng Syracuse Univ Coll Engn & Comp Sci Syracuse NY 13244 USA Univ Calif Los Angeles UCLA Comp Sci Dept Los Angeles CA USA Boston Univ Grad Sch Arts & Sci Boston MA 02215 USA Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA

ISBN: (纸本)9798400710353

the behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks.

关键词： Autonomous Driving Decision-making Strategy Deep Reinforcement learning Proximal Policy optimization

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RiscV-based Edge Intelligence Application for Securing Power Automation Device 4

RiscV-based Edge Intelligence Application for Securing Power...

引用

4th Power System and Green Energy conference (PSGEC)

作者： Yang, Wei Zhang, Shuhua Song, Rui Zhao, Chuanqi Wang, Lanruo China Elect Power Res Inst Artificial Intelligence Applicat Res Ctr Beijing Peoples R China

ISBN: (纸本)9798350365573;9798350365580

For the power automation system, which is literally a cloud-edge collaboration system, the cybersecurity topic is becoming the top priority. the power automation device as the edge intelligent device of automation network does not only play the role of data acquisition, analysis and interaction with the cloud but also undertakes responsibilities for security leveraging trusted core chip, secure operation system, and secure application mechanism. Meanwhile, the trusted chip core is fundamental to implement the secure power automation. this paper presents an Edge intelligent device prototype based on the RiscV core chip because the RiscV architecture significantly mitigates the security risks due to the open source core. And the operating system and application mechanism for the edge intelligent device is also addressed to secure all the power automation system. For the operating system, it should be able defend against all kinds of cyberattacks leveraging AI machine learning algorithm. On the other hand, for application mechanism, the communication between devices and control center should be protected according to the existing standard IEC 62351 series. In addition, verification results at lab compared with others are shown in the last section.

关键词： RiscV edge intelligence machine learning power automation

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Study on Railway Informatization optimization Based on intelligent Fusion Algorithm 4

Study on Railway Informatization Optimization Based on Intel...

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

作者： Bai, Ping China Academy of Railway Sciences Group Co. Ltd Institute of Electronic Computing Technology Beijing100081 China

ISBN: (纸本)9798350394375

this research introduces a QoS model for fundamental grid railway information services and a QoS model for composite grid railway information services to assess the efficacy of the railway information service composition within a grid environment. the study utilizes the weighted sum method to convert multi-objective optimization problems into single-objective optimization problems and devises a genetic annealing fusion algorithm for the global optimization model. Subsequently, using ticketing services as an illustration, the study validates the rationality of the QoS-based grid railway information service combination optimization model and confirms the accuracy of the genetic annealing hybrid algorithm. © 2024 IEEE.

关键词： Information services

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