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

共 13504 条记录，以下是531-540 订阅

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Adversarial Detection of Android Malicious applications Based on Feature Decentralization 4

Adversarial Detection of Android Malicious Applications Base...

引用

4th international conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Qiang, Gao State Grid Jining Power Supply Company Shandong Jining272000 China

ISBN: (纸本)9798331528324

Machine learning-based methods for detecting malicious Android applications are widely researched, but their security is a concern. Adversarial attacks can easily evade the detection of these methods. this paper designs a feature-dispersion-based Android malware detection method that can effectively defend against adversarial attacks. the method takes the function call graph as features and reduces the feature dimensions according to the function types. the feature matrix is then separated to form 349 features of 1*349 dimensions, while 349 lightweight convolutional neural networks are used for malicious application detection. Experimental results on public datasets show that the method in this paper is effective in detecting adversarial attacks, with a high detection rate of 94.83%. © 2024 IEEE.

关键词： Adversarial machine learning

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An Intelligent Auction-based Framework for Electric Vehicles Charging Management Utilizing Reinforcement learning and Blockchain 6

An Intelligent Auction-based Framework for Electric Vehicles...

引用

6th international conference on Communications Signal Processing and their applications

作者： Al Sadawi, Alia ElGhanam, Eiman Hassan, Mohamed S. Osman, Ahmed Amer Univ Sharjah Dept Ind Engn Sharjah U Arab Emirates Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9798350384826;9798350384819

Different electric vehicle (EV) charging coordination techniques are proposed in the literature to match the EV energy demand with the available supply through wired and wireless charging stations, and other nearby EVs. this station-to-EV and EV-to-EV energy exchange creates an energy trading market (ETM) that needs to be accurately managed to ensure efficient operation. Auctioning provides an ideal monetary mechanism to manage the EV ETM, since it incentives EVs to provide truthful information thus reaching optimal ETM state. Accordingly, this study presents a review of state-of-the-art literature on energy coordination within EV ETMs, and uses the findings of this review to propose an intelligent auction-based framework for EV energy trading using reinforcement learning (RL) algorithms and blockchain technologies. the proposed framework aims to satisfy the EV owners' charging needs at optimal response time and energy pricing. It also preserves EV owners' privacy, considers the remaining battery charge, maximizes the charging systems' profit, and balances the energy demand and supply. Hence, this work paves the way for future studies into implementing and validating the proposed framework for different case studies of EV ETMs.

关键词： Auction blockchain electric vehicles energy trading market optimization reinforcement learning

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Blood Smear morphological analysis and optimization through machine learning model 4

Blood Smear morphological analysis and optimization through ...

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Soni, Palvi Tandan, Gajendra Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798350364729

this article examines the utility of image processing and machine learning in analyzing the shape and structure of cell pictures obtained from peripheral blood smears, particularly on automatic identification. the fundamental principles of the three primary components (segmentation, quantitative characteristics, and classification) are delineated, and contemporary works are examined. this study examines malignant lymphoids and normal cells. these technologies can assist cytologists in conducting practical, objective, and rapid morphological studies of blood cells. they can help analyze specific morphological characteristics and function as educational and research instruments. Further study is required to establish screening strategies that effectively utilize image-based automated recognition systems in testing facilities, alongside other analysis approaches, as part of their regular operations. © 2024 IEEE.

关键词： Machine learning

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optimization of weight update for convolutional operators for domestic accelerators 5

Optimization of weight update for convolutional operators fo...

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Wang, Xibing Chen, Yongqing Lie, Bin Bu, Jingde Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China Dawning Informat Ind Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Convolutional neural networks play an important role in deep learning, which includes three processes: forward propagation, backpropagation, and weight update. On domestic heterogeneous accelerators, the performance of the weight update operator is somewhat weak. In this paper, we handwritten and optimized the weight update operator on domestic heterogeneous accelerators, including both ordinary convolution and grouped convolution. Among them, ordinary convolution uses the implicit gemm algorithm, Grouped convolution uses a parallelized direct convolution algorithm, which reduces memory usage and has strong universality. Parallel direct convolution algorithms make better use of it.

关键词： Operator optimization weight updates convolutional neural networks Domestic accelerators

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Advanced Analog Design optimization: Comparison between Reinforcement learning and Heuristic algorithms 20

Advanced Analog Design Optimization: Comparison between Rein...

引用

20th international conference on Synthesis, Modeling, Analysis and Simulation Methods and applications to Circuit Design, SMACD 2024

作者： Chevalier, Michel Trochut, Severin Guizzetti, Roberto Urard, Pascal Labrak, Lioua Samuel, John Cellier, Rémy Abouchi, Nacer STMicroelectronics Crolles France Université de Lyon CPE Lyon Institut des Nanotechnologies de Lyon Lyon France Université de Lyon CPE Lyon LIRIS Lyon France

ISBN: (纸本)9798350351927

Last decades have seen a lot of research on Analog Design Automation. the most recent approaches are based on Reinforcement learning (RL). this paper describes a new learning strategy enhancing the most recent Proximal Policy optimization RL approach, applied to analog design. the method is compared to more classical heuristic approaches such as Ant Colony optimization, Particle Swarm optimization or Differential Evolution algorithm. this study is done using a gymnasium environment embedding a spice-like electrical simulator. the experiments are done under equivalent calculation conditions. the paper highlights convergence properties and demonstrates the RL ability to avoid local minimum traps. © 2024 IEEE.

关键词： Reinforcement learning

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Self-supervised representation learning for histopathological images using contrastive learning 4th

Self-supervised representation learning for histopathologica...

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

作者： Gupta, Ekta Gupta, Varun Chandigarh College of Engineering and Technology Punjab University Chandigarh India

ISBN: (纸本)9781032911571

Artificial Intelligence (AI) is becoming popular in the field of histopathological imaging. there isn’t a lot of annotated medical image data, and it is time consuming task to get this data annotated. the self-supervised learning (SSL) is an upcoming approach used for the unlabelled data. this paper suggests an SSL-based approach to classify breast cancer histopathological images. Self-supervised contrastive learning is used to learn the representations from unlabelled datasets and then used the same representations for the classification tasks of histopathological images of the labelled datasets. Two publically aavailable breast cancer datasets are used in the proposed approach: BreakHis and BCHD. Our method obtained an accuracy of 95.96% for the BreakHis dataset and 91.4% for the BCHD dataset. these results show that self-supervised learning can help learn representations of images, even if there isn’t labelled data and also this approach can help to make AI models work better in medical imaging applications. © 2025 the Author(s).

关键词： Self-supervised learning

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the optimization of parameters for a Circulation loss accident classification model 4

The optimization of parameters for a Circulation loss accide...

引用

4th international conference on Advanced algorithms and Signal Image Processing, AASIP 2024

作者： Wang, Qi Mu, Xiangyang Li, Hengfeng Zhang, Jiahao School of Electronic Engineering Xi’an Shiyou University Xi’an710065 China Xi'an ESWIN Material Technology Co. Ltd. Xi’an710065 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

Mud leakage poses a persistent challenge in drilling operations, necessitating innovative solutions for improved sealing and circulation efficiency. this paper leverages data from the Mullen Field to categorize mud loss severity levels. Employing the Optuna framework, hyperparameter optimization enhances model performance metrics such as precision, recall, and F1-score. Our study underscores the efficacy of hyperparameter tuning in refining predictive models for mud loss severity. © 2024 SPIE.

关键词： Mathematical optimization Performance modeling Machine learning Decision trees Random forests Data modeling Safety Engineering Lawrencium Statistical modeling

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Creating Sorted Grid Layouts with Gradient-based optimization 24

Creating Sorted Grid Layouts with Gradient-based Optimizatio...

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4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Barthel, Kai Uwe Barthel, Florian Tim Eisert, Peter Hezel, Nico Schall, Konstantin HTW Berlin Visual Comp Grp Berlin Germany Fraunhofer HHI Berlin Germany HU Berlin Berlin Germany

ISBN: (纸本)9798400706028

Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationships. this approach facilitates the effective sorting of diverse elements ranging from data points to images, and enables the simultaneous visualization of a significant number of elements. However, sorting data on two-dimensional grids is a challenge due to its high complexity. Even for a small 8-by-8 grid with 64 elements, the number of possible arrangements exceeds 1.3.10(89) - more than the number of atoms in the universe - making brute-force solutions impractical. Although various methods have been proposed to address the challenge of determining sorted grid layouts, none have investigated the potential of gradient-based optimization. In this paper, we present a novel method for grid-based sorting that exploits gradient optimization for the first time. We introduce a novel loss function that balances two opposing goals: ensuring the generation of a "valid" permutation matrix, and optimizing the arrangement on the grid to reflect the similarity between vectors, inspired by metrics that assess the quality of sorted grids. While learning-based approaches are inherently computationally complex, our method shows promising results in generating sorted grid layouts with superior sorting quality compared to existing techniques.

关键词： Visual Image Exploration Sorted Grid Layouts Permutation learning Quadratic Assignment Problem

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Cooperative UAV Maneuver Decision-Making Based on Multi-Agent Reinforcement learning 4

Cooperative UAV Maneuver Decision-Making Based on Multi-Agen...

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4th international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Huo, Weiyan Li, Jianxun School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350378542

In this paper, we design a cooperative UAV maneuver decision-making task and use multi-agent reinforcement learning to solve it. In this task, two UAVs must learn cooperating with each other to defeat a stronger enemy. Due to sparse reward signal and insufficient exploration, the agents will learn a suboptimal policy. To mitigate this effect, the weighted entropy of reward is maximized to encouraged agent to explore while the expected cumulative reward is maximized. the weighted entropy of reward as an additional objective can be also optimized with reinforcement learning where the additional reward function is negatively correlated with the frequency of the reward obtained by agent. the test results show that compared with Multi-Agent Proximal Policy optimization (MAPPO), MAPPO with maximizing the weighted reward entropy has better performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of Distributed Simulation Operation and Maintenance Management System Based on Artificial Intelligence 4

Design of Distributed Simulation Operation and Maintenance M...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Meng, Yibo Hong, Dehua Zhang, Min Li, Ming Chai, Wujun Li, Bin State Grid Anhui Electric Power Co. Ltd. Information and Communication Branch Hefei China

ISBN: (纸本)9798350359589

Operation and maintenance system is an important platform to support the deployment, operation and evaluation of distributed simulation system. the operation monitoring tool is to manage the simulation node, control the operation process, feedback the operation results, and evaluate the operation effect. then this paper mainly describes the composition of the operation management system. through the research of distributed simulation test, a distributed simulation test management platform based on virtual instrument is proposed. the corresponding design ideas are given based on its functional requirements. the architecture, module composition and overall working process of the system are described. the working mechanism, design and implementation of node management, auxiliary analysis, instruction management and condition monitoring are analyzed. Multi-dimensional multi-source heterogeneous fault data are used as training samples to achieve automatic detection of equipment operating conditions. Reinforcement learning theory is applied to equipment fault diagnosis to achieve automatic judgment of test data, so as to establish the active early warning mechanism of equipment. © 2024 IEEE.

关键词： Artificial intelligence deep learning distributed simulation system equipment operation and maintenance system multi-source heterogeneous data

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