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

共 13588 条记录，以下是851-860 订阅

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An Enhanced Intrusion Detection System Model Using Machine learning Algorithm 4

An Enhanced Intrusion Detection System Model Using Machine L...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Chandra, Leena Singh Singh, Devendra Kumar Guru Ghasidas Vishwavidyalaya Department of Computer Science and Engineering Bilaspur India

ISBN: (纸本)9798350349856

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding the integrity and performance of an organization. throughout recent years, various approaches have been devised and put into practice to fortify the security, reliability, and availability of computer networks within enterprises. Our research specifically on IDSs constructed using Machine learning (ML) techniques. In our research, we have conducted an in-depth analysis of the CICIDS2017 intrusion detection dataset, which serves as the foundation for training and evaluating our machine learning models. We have implemented several machine learning approaches, including Decision Trees (DT), Extra Trees, Random Forest, and XGBoost, to study their effectiveness in detecting intrusions and ensuring the security of computer systems. In our experiments, we explored the use of Principal Component Analysis (PCA) for feature selection and HyperOpt for optimization. Our findings revealed that the XGBoost classifier achieved the highest accuracy among all three other methods (DT,ET,RF) the methods, with an impressive accuracy rate of 99.331%. this suggests that XGBoost is particularly effective in our context. © 2024 IEEE.

关键词： Decision trees

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Game theory applications in AI 4

Game Theory Applications in AI

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

作者： Rathore, Rachna Sharma, Pranay Sharma, Umesh Chandra Jenasamanta, Abhinash Shilpa, S. Sahu, Praveen Kumar Jiwaji University Dept. of Mathematics Gwalior India Madhav University Dept. of Cse Sirohi India Mangalayatan University Dept. of Cea UP Aligarh India Usha Martin University Dept. of Computing and Information Technology Jharkhand Ranchi India Dhanalakshmi University Dept. of Cse Coimbatore India Lingayas Vidyapeeth Dept. of Agriculture Faridabad India

ISBN: (纸本)9798350364729

the convergence of artificial intelligence (AI) and game theory presents intriguing research and application opportunities. this overview explores the fundamental concepts of game theory and its influence on AI systems and techniques. Various AI applications of game theory are examined, including mechanism design, auctions, adversarial machine learning, reinforcement learning, and multi-agent systems. Additionally, we delve into game-theoretic methods utilized in AI, such as cooperative game theory, Nash equilibria computation, and the minimax algorithm. through case studies and comparative analysis, we assess the strengths, weaknesses, current challenges, and future directions in this field. the aim of this review is to illuminate the synergies between game theory and artificial intelligence. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning for Personalized Medicine: A Comprehensive Review 4

Deep Learning for Personalized Medicine: A Comprehensive Rev...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Mirzapure, Isha A. Telrandhe, Umesh Dandekar, Mayur R. Salve, Yash M. Maharashtra Wardha India

ISBN: (纸本)9798331523923

Deep learning is now considered to be almost the most potent tool in personal medicine that has changed how healthcare is perceived into data-based and most precise at the same time. It makes decisions for aficionado administration with traits in interpreting large- and complex-data-sized deep learning models that analyze multimodal information, genomics, pictures, and clinical data, into personalized care strategies meant for the personalization of each patient's characteristics. the main components of deep learning are highlighted to show various applications in personal medicine and the challenges that must be overcome to make them work optimally in clinical practice and future directions regarding real-time health monitoring, multi-omics data integration, and explainable AI systems. Most of the factors-including data privacy, model interpretability, and a much stronger regulatory framework towards challenging problems that require innovation not just across disciplinary fully coordinated research but also collaborations amongst AI researchers, clinicians, and regulatory bodies in every field coming together to put inappropriate practices using deep learning for healthcare more responsible and fairer. Deep learning is so revolutionary at the moment and there is still potential for its application and research to grow to change the future in personalized medicine according to the most advanced and latest research together with invention. © 2025 IEEE.

关键词： Artificial Intelligence Deep learning Medical Imaging Personalized Medicine Predictive Modelling

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HyFed: A Hybrid Blockchain Empowered Federated learning Privacy Fair Framework 4th

HyFed: A Hybrid Blockchain Empowered Federated Learning Priv...

引用

4th international conference on Data and Information in Online Environments

作者： Chao, Kailin Jiang, Fan Xiao, Jianmao Zhong, Yaozhang Wu, Junyi Gu, Keyang Feng, Zhiyong Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9783031807121;9783031807138

this study presents the meticulous construction of a robust experimental system framework based on a hybrid blockchain network, designed to meet the experimental needs of federated learning research. the framework leverages TensorFlow Federated (TFF) to facilitate the optimization and substitution of federated learning aggregation algorithms and the development of reputation and contribution systems. the hybrid blockchain network architecture within this framework combines the advantages of public and private blockchains, capable of processing public transactions and managing private data. An innovative data encryption and access control mechanism has been implemented, ensuring data privacy and security. Performance optimizations, including the acceleration of block production speed and database query optimization, have been carried out to enhance system efficiency. this article provides a comprehensive deployment of the framework and an analysis of its components, offering a foundation for further research. With the evolution of federated learning and blockchain technology, the proposed experimental system framework is expected to have broader application prospects and research value.

关键词： Federated learning Blockchain Smart Contract Data Security Privacy Protection

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Synthesis of sparse conformal arrays through multi-task learning method 4

Synthesis of sparse conformal arrays through multi-task lear...

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4th international conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Li, Longjun Gu, Zhigang Xia, Lei Han, Bing Air Force Communications NCO Academy Dalian China

ISBN: (纸本)9781510682467

In this letter, we propose a multi-task learning (MTL) method for efficient design of sparse conformal arrays with weighting optimization. the problem is to find a set of optimal weights with minimum dipole antennas that will generate a beam pattern radiated by the uniform conformal array. Simulation results are also shown that a sparse conformal antenna array design with favorable pattern-matching is obtained by MTL method. © 2024 SPIE.

关键词： Multi-task learning

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Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems 4

Research on traffic sign recognition algorithms for autonomo...

引用

4th international conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wang, Chuanfei School of Government Beijing Normal University Beijing100875 China

ISBN: (纸本)9781510682528

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. this study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. the images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. the research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.

关键词： Object detection Detection and tracking algorithms Unmanned vehicles Performance modeling Feature extraction Image processing Data modeling Image segmentation Mathematical optimization

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Predictive Tracking Control of Autonomous Mobile Platform with Stage Visual Effects Lighting 4

Predictive Tracking Control of Autonomous Mobile Platform wi...

引用

4th international conference on Control theory and applications, ICoCTA 2024

作者： Zhu, Huazhong Sun, Yuhao Liu, Andong Ni, Hongjie Zhejiang University of Technology College of Information Engineering Hangzhou China

ISBN: (纸本)9798331506155

In this paper, a predictive control (MPC) approach is proposed to deal with the asynchronized problem of autonomous mobile platform in stage visual effects lighting. First, a tracking error model is established with intermittent measurement by defining the geometric center of the visual effects lighting as a virtual robot to generate a reference path for the autonomous mobile platform. the intermittent measurement is represented by a binary random variable. An unconstrained MPC controller is designed and a terminal penalty function is introduced, while the mean-square asymptotic stability of the system is analyzed. Moreover, the MPC optimization problem is reformulated into a constrained quadratic programming problem within the input and state constraints of the system. To solve the constrained problem, a primal-dual neural network (PDNN) is employed to solve the quadratic programming problem. the PDNN method is adopted for its fast computational ability, powerful real-Time optimization capability, ease of hardware implementation and robust global convergence. Finally, the effectiveness of the proposed method is verified by a simulation example, which demonstrates the ability of the method to achieve accurate tracking in stage environments. © 2024 IEEE.

关键词： Constrained optimization

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Nature Inspired optimization algorithms in Fractional Order Controller Design 28

Nature Inspired Optimization Algorithms in Fractional Order ...

引用

28th IEEE international conference on Intelligent Engineering Systems (INES)

作者： Dulf, Eva-H Berciu, Alexandru George Denes-Fazakas, Lehel Kovacs, Levente Tech Univ Cluj Napoca Fac Automat & Comp Sci Dept Automat & Appl Informat Cluj Napoca Romania Obuda Univ Univ Res & Innovat Ctr Budapest Hungary Obuda Univ Physiol Controls Res Ctr Budapest Hungary Obuda Univ John von Neumann Fac Informat Biomat & Appl Artificial Intelligence Inst Budapest Hungary Obuda Univ Appl Informat & Appl Math Doctoral Sch Budapest Hungary

ISBN: (纸本)9798350367607;9798350367591

Fractional-order PID (FOPID) controllers have gained increasing interest in control theory in recent years, mainly to improve the performance and stability of complex systems. FOPID controllers are a generalization of classical PID controllers that have both integral and derivative orders of fractional order. this means that instead of having three tuning parameters as in classical PID, there are two additional degrees of freedom to achieve the control objectives. At the same time, the structure of the PID controller, so valued in industrial applications, is retained. A preferred method for designing FOPID controllers is the use of optimization algorithms. Different objective functions are used, such as the integral of the square error (ISE), the integral of the absolute error (IAE), the integral of the time-weighted absolute error (ITAE) or the integral of the time-weighted square error (ITSE), which must be minimized in order to adjust the five unknown parameters of the FOPID controller. there are several papers discussing different optimization methods and objective functions for different applications, but there are no general recommendations to help engineers choose the right method for their design. the present research compares three of the most well-known nature-inspired optimization techniques from the point of view of a FOPID controller design tool, using all the above enumerated objective functions. the analysis is realized both for processes with large time constants (such as thermal processes) and for dynamic systems (such as mechatronic processes), presenting the advantages and disadvantages of each method.

关键词： fractional order controller nature inspired optimization methods Genetic algorithms Particle Swarm optimization Been colony optimization

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Bird Species Classification Using MobileNetV2 4

Bird Species Classification Using MobileNetV2

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4th international conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Valasa, Roshan Aerukonda, Sai Anil Yaseen, Haseeba Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350375466

this paper proposes to develop and evaluate a machine-learning model for bird species classification using the MobileNetV2 architecture. the model is trained on a dataset of images of various bird species and evaluated on a separate test arranged to assess its precision in categorizing unseen pictures of birds. Transfer learning is utilized to further refine the pre-established model to the specific task of bird species classification. the model is estimated using criteria similar to delicacy, recall, perfection, and F1-score. the MobileNetV2 model that was recommended achieved an overall test set accuracy of 90.5%. Further experimentation with transfer learning techniques led to an accuracy improvement of 94.3%. the results demonstrate the feasibility of using MobileNetV2 for efficient and accurate bird species classification, with potential applications in fields such as ornithology and conservation. the model's contribution towards bird identification can aid in conservation efforts through improved population monitoring and habitat management. © 2024 IEEE.

关键词： Adversarial machine learning

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2025 4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

2025 4th International Conference on Robotics, Electrical an...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

ISBN: (纸本)9798331530938

the proceedings contain 96 papers. the topics discussed include: optimizing day-ahead charging schedules for electric vehicles: a multi-factor priority-based approach;enhancing road navigation for the visually impaired: a laser-based night vision approach;hybrid energy-based battery storage swapping station for electrical vehicles and net metering;a correlative abstraction between the scenario of no-retransmission and retransmission process by utilizing evolutionary game theory for self-organized data aggregation mechanism;designing and manufacturing of Robo nurse with fully articulating hand;impact of battery operated three wheeler on local grid system;improved breast cancer detection in ultrasound images using masked image integration and transfer learning;and production cost and emissions minimization based on renewable energy using superiority of feasible solutions- moth flame optimization.

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