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Classification of Information Objects with Fuzzy Parameters in E-Learning systems 12

Classification of Information Objects with Fuzzy Parameters ...

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12th ieee international conference on intelligent Data Acquisition and Advanced Computing systems: technology and Applications, IDAACS 2023

作者： Mukhin, Vadym Zavgorodnii, Valerii Liskin, Viacheslav Syrota, Sergiy Koval, Vasyl Honchar, Liudmyla National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of System Design Kiev Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Information Systems and Technologies Kiev Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Applied Mathematics Kiev Ukraine West Ukrainian National University Department of Information-Computing Systems and Control Ternopil Ukraine West Ukrainian National University Department of Computer Science Ternopil Ukraine

ISBN: (纸本)9798350358056

The paper describes the process of the information objects classification in e-learning systems using the mathematical background of fuzzy logic. This approach can be used to improve the process of searching and organizing information in e-learning systems. As the volume of educational materials grows, it becomes more and more difficult to find and structure information. Fuzzy logic helps to optimize the search, and the use of a neural network can correctly classify materials in e-learning systems, which increases the efficiency and accuracy of search queries. © 2023 ieee.

关键词： classification e-learning system fuzzy logic information object neural network

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification 5

A Novel Breast Cancer Detection and Classification Using Enh...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 ieee.

关键词： Lung cancer

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 ieee.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment 20

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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20th ieee international conference on Mechatronics and automation, ICMA 2023

作者： Tian, Wei Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process. © 2023 ieee.

关键词： Reinforcement learning

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SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction

SFADNet: Spatio-temporal Fused Graph based on Attention Deco...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Mei Weng, Wenchao Li, Jun Lin, Yiqian Chen, Jing Seng, Dewen Hangzhou Dianzi University Hangzhou China Zhejiang University of Technology Hangzhou China

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional prediction methods are often limited by static spatial modeling, making it difficult to accurately capture the dynamic and complex relationships between time and space, thereby affecting prediction accuracy. This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on temporal and spatial feature matrices. For each pattern, we construct an independent adaptive spatio-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules to better capture dynamic spatio-temporal relationships under different fine-grained traffic patterns. Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baselines across four large-scale datasets. © 2025 ieee.

关键词： Air traffic control

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Modelling of a Non-Linear Dynamic System Using Long Short-Term Memory 4

Modelling of a Non-Linear Dynamic System Using Long Short-Te...

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4th ieee international conference on Computing, Communication, and intelligent systems, ICCCIS 2023

作者： Sahu, Richa Srivastava, Smriti Kumar, Rajesh Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Control and Instrumentation Engineering Delhi India National Institute of Technology Department of Electrical Engineering Kurukshetra India

ISBN: (纸本)9798350306118

Long Short-Term Memory (LSTM) is a Recurrent Neural Network (RNN) that overcomes typical neural network constraints. Because of its ability to record temporal dependencies and solve nonlinear equations, long-Term data can be simply managed. LSTM is intended to alleviate the problems of RNN's vanishing gradient and exploding gradient difficulties. In this paper, we used LSTM to solve nonlinear plant equations with a gradient descent-based back propagation approach and retrieved the plant's output as well as other performance measures including Average Mean Square Error (AMSE) and Total Mean Average Error (TMAE). When the output of an LSTM is compared to that of a feed-forward Neural Network (FFNN), the LSTM outshines the FFNN. All the parameters of both models, such as iteration count and learning rate are kept constant. © 2023 ieee.

关键词： Backpropagation

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Joint Recommendation of Materialized Views and Indexes Based on Reinforcement Learning 20

Joint Recommendation of Materialized Views and Indexes Based...

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20th ieee international conference on e-Business Engineering, ICEBE 2024

作者： Wang, Jiangqing Hu, Yinglun Sun, Chong Zhang, Huili Ruan, Chongwei Cao, Shipeng Zhang, Bowen South-Central Minzu University College of Computer Science Wuhan430074 China Hubei Provincial Engineering Research Center of Agricultural Blockchain and Intelligent Management Wuhan430074 China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Wuhan430074 China Dong Feng Machine Tool Plant Co. Ltd Hubei ShiYan442022 China

ISBN: (纸本)9798350365856

With the increasing amount of data in current OLAP databases, higher requirements are being placed on query efficiency. Index recommendation and materialized view recommendation are very important practical problems in database management systems, as both aim to improve the execution efficiency of specified workloads. Tuning both views and indexes for a given workload can be a daunting task for DBAs or automation tools, as both index tuning and view recommendation alone are NP-hard problems. When placed in the same tuning system, the search space rises exponentially. To address these challenges, we propose an intelligent approach for integrated physical database design recommendation. The system is made up of an index and view selection model based on Monte Carlo Tree Search and a utility estimation model based on deep learning, which can speed up the efficiency of index and view recommendation and quickly and accurately estimate the execution cost of a query. Specifically, our approach achieves up to a 60% performance gain when running industrial benchmarks. © 2024 ieee.

关键词： Reinforcement learning

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Research on Path Planning control of Maze Robot 6

Research on Path Planning Control of Maze Robot

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6th international conference on intelligent Autonomous systems, ICoIAS 2023

作者： Chi, Minglu Zhao, Longchao Yang, Fuchao Li, Zhixiang Zhang, Yahao Ma, Leichang Qian, Xiaoyan Ren, Qinchao School of Intelligent Engineering Henan Institute of Technology Xinxiang China Co. Ltd Xinxiang China School of Economics Henan Institute of Technology Xinxiang China Jiangsu Huibo Robot Technology Co. Ltd Suzhou China

ISBN: (纸本)9798350371253

To solve the problem of slow exploration speed of maze robot, a new kind of maze robot is designed in this paper. STM32FI03C8T6 with strong performance is used as the control chip to effectively improve the working efficiency of maze robot. The existing left-right exploration algorithms are compared and optimized, and the left-right cross algorithm is proposed, which effectively improves the maze exploration speed and accuracy of the maze robot. The experimental platform of the maze robot is built, and the path exploration and control program of the robot is verified. The results show that the designed maze robot can effectively explore the path, the hardware system can reduce the influence of external environmental factors such as light and temperature on the maze robot, and improve the motion control stability and exploration algorithm accuracy of the maze robot. © 2023 ieee.

关键词： Robot programming

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Iron Loss Online Identification and Minimum Loss control of Surface-Mounted Permanent Magnet Synchronous Motor 5

Iron Loss Online Identification and Minimum Loss Control of ...

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5th ieee international conference on Power, intelligent Computing and systems, ICPICS 2023

作者： Sun, Linfeng China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350333442

In 2020, China's motor ownership is about 2.95 billion kilowatts, and the annual power consumption account for more than 60% of the total social power consumption. Improving the motor operation efficiency will play an important role in achieving carbon peak and carbon neutrality. Based on the equivalent model of permanent magnet synchronous motor with iron loss, this paper proposed online identification of iron loss resistance based on model reference adaptive system (MRAS), then given current of d and q axes with the minimum sum of copper loss and iron loss is derived. Based on Simulink, the equivalent model of motor with iron loss is established, and the feasibility of the proposed method is verified by simulation. The simulation results show that compared with the traditional id=0 control method, the minimum loss control method proposed in this paper effectively reduces the loss in the motor operation, improves the efficiency, and has faster response, smaller overshoot, and better dynamic performance. © 2023 ieee.

关键词： Permanent magnets

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Dynamic Decoupling control for Virtually Coupled Train Set: A Reinforcement Learning-Based Model Predictive control Approach 27

Dynamic Decoupling Control for Virtually Coupled Train Set: ...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Luo, Chenyu Liu, Yafei Li, Yuanhang Sun, Zhanbo Hu, Xiaoxi Liu, Jin School of Transportation and Logistics Southwest Jiaotong University Chengdu China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing China Institute for Transport Studies University of Leeds Leeds United Kingdom

ISBN: (纸本)9798331505929

This paper presents a novel dynamic decoupling control method designed to manage the decoupling operations of virtually coupled train sets (VCTS) within the throat area of a railway line. The primary objective is to dynamically adjust the spacing between trains within the constraints of safety, thereby enhancing the efficiency and smoothness of decoupling maneuvers. Traditional controllers with fixed parameters often fail to effectively manage the complex constraint challenges inherent in such scenarios. To address the multi-objective and nonlinear nature of this constrained control problem, we develop a reinforcement learning (RL) based model predictive control (MPC) approach. The MPC framework is proficient at handling stringent constraints imposed by speed limits, relative braking distances, and signaling protection requirements. The RL-based strategy is capable of learning and fine-tuning MPC parameters in response to varying line conditions and train dynamics parameters by calculating the gradient from the MPC cost. Numerical experiments demonstrate that this approach significantly improves safety while reducing the decoupling time of VCTS from 59.6 seconds to 47.4 seconds after convergence. © 2024 ieee.

关键词： Predictive control systems

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