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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

共 2977 条记录，以下是51-60 订阅

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A Neural Network Inference Verification Framework and application Based on Generalized GKR Protocol 5

A Neural Network Inference Verification Framework and Applic...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Wang, Yujian Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the rapid advancement of edge computing technology and the widespread application of artificial intelligence, the deployment of neural network inference at the edge has garnered increasing significance. However, constrained by limitations in computational resources and security considerations, effectively verifying the correctness of neural network inference at the edge poses a formidable challenge. To address this challenge, this paper proposes a neural network inference verification framework based on the generalized GKR protocol, specifically tailored for edge deployment. Leveraging the bidirectional efficiency inherent in the generalized GKR protocol, this framework enables rapid and precise validation of neural network inference, thereby enhancing the reliability and security of edge-based neural network inference. Additionally, this paper tackles challenges encountered in designing the verification framework, such as handling model parameters and transforming non-linear functions, utilizing pertinent techniques. Finally, the effectiveness and performance of the framework are validated and analyzed through experimental verification.

关键词： Neural network Verifiable computation machine learning Edge computing

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An Online Transfer learning Model for Intrusion Detection using FT-Transformer and KSWIN-Driven Concept Drift Detection Mechanism 5

An Online Transfer Learning Model for Intrusion Detection us...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Zhang, Zhaozhe Zhang, Hongpo Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Deep learning has been successful in intrusion detection, but new attack methods cause data distribution changes, known as concept drift. To address this, we propose an online transfer learning method. It includes a concept drift detection mechanism to identify traffic pattern changes and fine-tune deep learning models accordingly. Experimental results show an average accuracy of 99.46% on the CIC-IDS dataset and 96.79% on the NetFlow V2 dataset.

关键词： Network intrusion detection Deep learning Data stream Concept drift detection Transfer learning

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Extreme learning machine based on Improved Multi-Objective Particle Swarm Optimization 5

Extreme Learning Machine based on Improved Multi-Objective P...

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5th international conference on computer Engineering and application (ICCEA)

作者： Tan, Kaimin Jiangsu Univ Sci & Technol Sch Comp Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

the Extreme learning machine (ELM) is a single-hidden layer feed forward neural network with advantages such as fast learning speed and good generalization performance. However, due to the random initialization of input weights and hidden biases, the ELM network structure is redundant and affects the generalization performance and stability of the network. this paper proposes a hybrid algorithm that uses a improve multi-objective particle swarm optimization (IMOPSO) algorithm to consider and optimize the characteristics of multiple conflicting objectives. the algorithm achieves global optimization by optimizing the root mean square error (RMSE) on the validation set, the L-2 norm of the output weights, and the L-2,L-1 norm the output weights. this constrains the input weights and hidden biases of the ELM network within reasonable ranges, improving the poor generalization performance and instability caused by the random selection of input weights and hidden layer thresholds. Additionally, it makes the ELM network structure more compact. To improve the global search capability of the multi-objective particle swarm optimization algorithm, we introduce an enhanced version called the IMOPSO. this upgraded algorithm integrates a novel global best particle selection strategy and employs multiple swarms to boost population diversity and convergence, surpassing the capabilities of the conventional MOPSO. Finally, we validate the efficiency of the hybrid algorithm by conducting experiments on various benchmark datasets.

关键词： Extreme learning machine IMOPSO Generalization performance stability of performance structural simplicity

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Enhancing autonomous driving systems with deep learning and spatial channel attention mechanisms: an experimental study 4

Enhancing autonomous driving systems with deep learning and ...

引用

4th international conference on machine learning and computer application, icmlca 2023

作者： Yao, Yao South China Normal University Guangzhou China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the advancement of technology, autonomous driving has become a popular research field. However, designing an autonomous driving system that can handle complex environments and meet real-time requirements remains an open challenge. In this study, we explore how to implement an efficient and reliable autonomous driving system using deep learning techniques. We experimented with a series of deep learning models, including VGG16, Inception series models, ResNet, MobileNetV2 and DenseNet121. By comparing the performance of these models on our dataset, we identified the best performing ones and fine-tuned them to optimize their performance for our task. In addition, to further improve the performance of the model, we introduced a spatial channel attention mechanism. We found that deploying the spatial channel attention mechanism at appropriate locations could significantly improve the performance of the model. However, deploying the spatial channel attention mechanism at all locations might cause the model to focus too much on local features and ignore global features. Overall, our results show that the performance of autonomous driving systems can be effectively improved by improving the network structure and training methods. © 2024 SPIE.

关键词： Autonomous vehicles

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Research on the evaluation and pricing of data assets under the circulation value of highway data assets 24

Research on the evaluation and pricing of data assets under ...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Wang, Sheng Guangxi Vocational Normal College College of Accounting and Audit Guangxi Nanning China

ISBN: (纸本)9798400709999

With the digital transformation of highway construction, a considerable amount of structured, semi-structured, and unstructured data resources have been accumulated, exerting an extremely high application value in the highway sector. the market demand for highway data transactions is gradually escalating, and it becomes particularly crucial to scientifically assess the value of highway data. Nevertheless, at present, the application of highway data has limitations, the value content is indistinct, and a set of methods or models applicable to the valuation of highway data assets has not yet been established. Hence, based on the characteristics of highway data assets, this paper utilizes data mining and machine learning techniques to construct a dynamic valuation model of highway data asset value, which holds reference value for the assessment of highway data value. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Privacy Efficient federal learning approach for smart security 4

Privacy Efficient federal learning approach for smart securi...

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4th international conference on machine learning and computer application, icmlca 2023

作者： Zhan, Runjia Fang, Chen Zhu, Zhitong Information Engineering University Zhengzhou China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

In the traditional security field, user data is collected by different monitoring points, which can not be shared effectively, resulting in the phenomenon of "data island". To this end, this paper designs a privacy-efficient federated learning method for the field of intelligent security. By using a horizontal federated learning framework, under the premise of ensuring that the data does not go out of each region, the distributed monitoring points across the domain are jointly trained to ensure data privacy and security. In order to ensure the security of gradient data transmission in the process of joint modeling, a privacy protection protocol based on additive secret sharing was designed to effectively prevent gradient leakage attacks and enhance the efficiency of calculation. this paper proposes a communication compression strategy based on adaptive bidirectional TOP-K gradient sparsification, which greatly reduces the communication overhead in the joint modeling process while ensuring the model training performance. Our experiments on COCO dataset and Gunknipe-dataset dataset show that our method can not only compress communication overhead and improve training efficiency, but also effectively prevent gradient leakage attacks and ensure the security of transmitted data. © 2024 SPIE.

关键词： Efficiency

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Optimizing EV Charging Station Placement in New South Wales: A Soft Actor-Critic Reinforcement learning Approach 5

Optimizing EV Charging Station Placement in New South Wales:...

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5th international conference on computer Engineering and application (ICCEA)

作者： Huang, Jinyi Zhou, Xiaozhou Monash Univ Melbourne Vic Australia Jiangsu Univ Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In this work, the Soft Actor-Critic (SAC) reinforcement learning algorithm is employed to investigate the optimization of new electric vehicle (EV) charging station layouts in New South Wales. this work integrates traffic density data and existing charging station locations, aiming to minimize the distance between electric vehicles and charging stations. It will ease the growing pressure on demand for EV infrastructure. Tailored reward mechanisms are designed within the SAC framework to accommodate adaptive dimensionality, demonstrating strong compatibility with urban scenarios. the model's performance offers convincing evidence of its efficacy in real-world settings. Integrating more granular data and more detailed reward mechanism design could enhance local decision-making processes, leading to more coherent overall infrastructure planning. this work contributes to advancing sustainable urban mobility by showcasing how advanced machine-learning techniques can be utilized in infrastructure planning.

关键词： Electric Vehicle (EV) Infrastructure Charging Station Optimization Reinforcement learning (RL) Soft ActorCritic (SAC) Algorithm Deep Q-Networks (DQN)

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Improved Human Optimisation Algorithm for Global Path Planning in AGV 5

Improved Human Optimisation Algorithm for Global Path Planni...

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5th international conference on computer Engineering and application (ICCEA)

作者： Wang, Qin Tang, Shaoyu Anhui Polytech Univ Sch Elect Engn Wuhu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Aiming at the problems of slow convergence speed of human learning algorithm for optimum search, low accuracy of the optimization search, low efficiency of the path search, and lack of security in path planning, a diligent human learning optimal algorithm that integrates the particle swarm algorithm and the improved HLO algorithm is proposed. the improved algorithm is applied to global path planning, and simulation experiments prove the feasibility of the improved algorithm in AGV path planning, which has faster convergence speed and shorter planning path length than the traditional algorithm, and can effectively reduce the number of algorithm iterations.

关键词： path planning human learning optimization algorithm particle swarm algorithm AGV

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Navigating Complexity: A Deep learning Approach to Lane Change Decisions in Autonomous Vehicles 5

Navigating Complexity: A Deep Learning Approach to Lane Chan...

引用

5th international conference on computer Engineering and application (ICCEA)

作者： Kassem, Nada Eid, Sameh Elshaer, Yasser Arab Acad Sci Technol & Maritime Transport Sch Mech Engn Giza Egypt Knowledge Hub Univ Coventry Univ Branch Sch Mech Engn Cairo Egypt

ISBN: (纸本)9798350386783;9798350386776

Decision-making plays a critical role in guiding autonomous vehicles through complex environments characterized by dense traffic and dynamic elements. the existing research for decision-making in autonomous driving suffers from safety concerns, high computational power needs, low accuracy in comparison to human drivers, and their outputs cannot be easily interpreted. the paper discusses a deep learning-based decision-making algorithm for autonomous vehicles in dense and dynamic environments. It aims to create an easily interpreted algorithm that ensures safety, efficiency, and accuracy in lane change scenarios. the study introduces a novel approach that can be used within embedded systems and behaves similarly to human drivers. the methodology involves classifying lanes as blocked or free and making decisions based on this classification, mimicking human decision-making processes. the results show the algorithm's effectiveness in complex scenarios, reaching a safety of 96% and accuracy of 90% which surpasses the Hierarchical Finite State machine (HFSM) algorithm.

关键词： component Autonomous vehicles decision-making dense scenarios dynamic objects deep learning lane change

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Data Mining of the Underachievers' Performance of E-learning and Finals in College English with SPSS and WEKA 5

Data Mining of the Underachievers' Performance of E-learning...

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5th international conference on computer Engineering and application (ICCEA)

作者： Zhou, Xiaoxia Dalian Neusoft Univ Technol & Informat Coll English Dept Sch Foreign Languages Dalian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

this study aims to explore the academic performance of underachievers in college English through the perspectives of e-learning engagement and learning outcomes by using the statistical means of SPSS and the predictive modeling of J48 Decision Tree with WEKA. the statistics in iTEST, paper-based and computerized final exams by underachievers were analyzed with regard to the differences in genders and majors and the correlations mainly through SPSS. the J48 Decision Tree in WEKA provided a nuanced understanding of the hierarchical nature in learning factors and their effect on success in the final exam. the integration of SPASS and WEKA made it possible to generate feasible approaches for pedagogical strategies, and personalized learning instructions to enhance the underachieving students' academic achievement in the e-learning environment.

关键词： underachievers data mining SPSS WEKA

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