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检索条件"任意字段=2017 2nd International Conference on Intelligent Computing and Information Engineering（ICIE 2017）"

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Improved algorithm for liver tumor segmentation in CT images based on U-Net 2

Improved algorithm for liver tumor segmentation in CT images...

引用

2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Qi, Yan Zhao, Hongkun Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

Aiming at the problem of inaccurate boundary segmentation of liver tumors by existing algorithms, an improved model REEC-UNet based on U-Net was proposed. Firstly, in order to alleviate the problem of gradient disappearance and semantic information loss in deep networks, residual connection is introduced into U-Net network framework. Then, the ECANet channel attention network was added in the coding stage and decoding stage to improve the performance of the convolutional neural network. The training and testing were carried out on the LiTS2017 dataset. The experimental findings demonstrated that the proposed method achieved more precise tumor boundary segmentation and enhanced the accuracy of liver tumor segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

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Text Classification Based on Multi-Head Attention Mechanism 2

Text Classification Based on Multi-Head Attention Mechanism

引用

2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Wang, Donghui Zhou, Fan Yin, Zhenyu Shenyang Ligong University Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350368888

Aiming at the limitations of traditional classification methods in single text information extraction, this paper proposes a multi-head attention classification model that introduces a bias term. The model employs a multi-head attention mechanism in place of a single attention mechanism to obtain effective information from multiple perspectives. By introducing bias vectors in each attention head, the model is able to focus on different aspects of the input in multiple dimensions, thus capturing key information more accurately. In this paper, the researchers carried out experiments using the IMDB dataset. The findings demonstrate that the suggested model displays notable enhancements in text categorization tasks, thus confirming the efficacy of the approach. © 2024 IEEE.

关键词： Neural network models

来源：评论

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Multi-Agent Reinforcement Learning Based on Cross Task information Sharing 2

Multi-Agent Reinforcement Learning Based on Cross Task Infor...

引用

2nd international conference on Signal Processing and intelligent computing, SPIC 2024

作者： Gao, Yu Zhu, Lizhong Liu, Yunting Yang, Jiaming Harbin First Machinery Group Corporation Harbin China Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

This article selects StarCraft as the experimental environment, which has a complex and vast action and state space, making it difficult for intelligent agents to make optimal decisions. In this article, the sharing of parameter experience values among multiple agents in a single task is used to enhance the collaboration ability between agents and avoid repetitive learning of basic strategies. In addition, after distilling and refining the core strategies of individual tasks, policy interaction is carried out in multiple tasks of the same type. By establishing a joint objective function for multiple tasks and alternating optimization for mutual constraints and updating iterations, information exchange between cross task multi-agent systems is completed, solving the problem of difficulty in obtaining effective data in complex scenarios and improving the learning efficiency of micro unit joint decision-making. This paper also adds qualification tracking to solve the problem of reward delay at the initial stage of model training, and uses course transfer learning to complete incremental joint training. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on brain tumor segmentation algorithm based on attention mechanism 2

Research on brain tumor segmentation algorithm based on atte...

引用

2nd international conference on intelligent Perception and Computer Vision, CIPCV 2024

作者： Kong, Xiangmao Liu, Yuxi Fang, Hongwang Gao, Xiaoxin Xiong, Genxin Beijing China-power Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350360097

Magnetic Resonance Imaging (MRI) playing a crucial role in the task of brain tumor segmentation. Achieving high-precision segmentation in brain tumor images is highly challenging. Currently, brain tumor segmentation mainly relies on manual segmentation by skilled medical professionals, which is time-consuming. In this paper, we propose a brain tumor segmentation algorithm, AUFP (Attention-based U-Net with Feature Pyramid), aimed at improving segmentation accuracy. AUFP introduces an Attention Feature Pyramid module (AFP) before the up-sampling stage in the U-Net architecture and an Attention Up-Sample module (AU) during the up-sampling process. The AFP module incorporates attention mechanisms to use global information effectively, enhancing segmentation accuracy. Experimental evaluations on the BraTS2017 data set indicate the effectiveness of the proposed AUFP algorithm. The overall tumor segmentation, core tumor segmentation, and enhancing tumor segmentation accuracy on the BraTS2017 data set are 92.1%, 74.4%, and 71.1%. Compared to a modified U-Net model (DUNet), with respective increases of 2.1%, 3.7%, and 7.7%. In conclusion, the introduced feature pyramid and attention up-sample modules in this paper effectively enhance brain tumor segmentation accuracy by improving the utilization of local information. Our results validate the efficacy of these modules for the task of brain tumor segmentation. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Performance of Time-Based Feature Expansion Classification Method for Predicting Disease Spread 2

Performance of Time-Based Feature Expansion Classification M...

引用

2nd international conference on Software engineering and information Technology, ICoSEIT 2024

作者： Nurriadi, Riza Nawal Prasetiyowati, Sri Suryani Sibaroni, Yuliant Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350317503

Cases of the COVID-19 pandemic and endemic dengue fever are still occurring in Indonesia. Therefore, research is needed to develop a predictive classification model for the transmission of COVID-19 and dengue fever for the next few years. The scenario used is an expansion of time-based features implemented in two algorithms, namely Support Vector Machine and Deep Neural Network. The dataset used relates to the number of COVID-19 cases and their features from November 2020 to April 2022, from 2017 to 2021 for the dengue case dataset. The research results show that the optimal prediction model for the DHF dataset is the model from the previous 3 years with an accuracy of 90%. The model was produced using the SVM time-based feature expansion method with an optimal number of features of 7. Meanwhile, the most optimal COVID-19 classification prediction model was the previous 7-month model with an accuracy of 93% and was produced by SVM time-based feature expansion. Meanwhile, for the DNN algorithm, it has not been able to outperform SVM performance for both the COVID-19 dataset and the DHF dataset. © 2024 IEEE.

关键词： COVID-19

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Design of Intrusion Detection System for Wireless ADHOC Network in the Detection of DOS Attack using Oneclass SVM with Wrapper Approach Feature Selection Comparing with information Gain Algorithm 2

Design of Intrusion Detection System for Wireless ADHOC Netw...

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Kumar, M. Dinesh Nagalakshmi, T.J. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350315905

The goal of this work is to design two intrusion detection systems to detect DOS attack in a wireless adhoc network by using Innovative oneclass SVM (Support Vector Machine) with wrapper approach Feature Selection IDS (Group 1) and information Gain Algorithm (Group 2). Materials and Methods: To design an IDS model CICIDS 2017 dataset was taken. By using above mentioned algorithms the IDSs are modelled and are analysed with the SPSS tool with 19 samples in each group. Results: The IDS using Innovative oneclass SVM (Support Vector Machine) with wrapper approach Feature Selection IDS having 92% accuracy and 82% detection rate and the IDS with information Gain Algorithm having 82% accuracy and 80% detection rate. The significance p © 2023 IEEE.

关键词： Denial-of-service attack

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Machine Learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

引用

2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Trajectory Tracking of AUV under Current Disturbance Based on Adaptive LADRC 2

Trajectory Tracking of AUV under Current Disturbance Based o...

引用

2nd international conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Ji, Daxiong Wang, Xinwei Xu, Minghui Zhang, Qiang Sharma, Sanjay Sutton, Robert Institute of Marine Electronics and Intelligent Systems Ocean Collage Zhejiang University Donghai Lab Zhoushan China School of Engineering Computing and Mathematics University of Plymouth Intelligent Autonomous Control Systems Plymouth United Kingdom

ISBN: (纸本)9798350308341

In consideration of the trajectory tracking problem of autonomous underwater vehicle (AUV) under external current disturbance, an adaptive linear active disturbance rejection control (LADRC) method is proposed. Unlike the conditional LADRC using fixed parameters for extended state observer, the proposed controller makes use of the error of estimation and automatically adjusts the parameter online. As shown in the simulation, the adaptive LADRC estimates the external current disturbance effectively, and tracks the 3D trajectory better compared with traditional LADRC. © 2023 IEEE.

关键词： Disturbance rejection

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Construction of Smart Pension System for Urban and Rural Residents Based on Big Data Mining Algorithm 2

Construction of Smart Pension System for Urban and Rural Res...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhao, Jing Xi'an Fanyi University International Business School Shaanxi China

ISBN: (纸本)9798350347456

Population aging is a social problem that every country cannot avoid in the process of modernization. China has rapidly stepped into the threshold of an aging society. With the deepening of the aging of the population and with the increasing demand for diversified services for the elderly, there is an urgent need to provide multi-level, high-quality elderly care(ec) services for the elderly. There is a large gap in human resources and other resources in the traditional pension industry. The pension industry needs to develop. Zhiyi pension has opened a new realm of the pension model. It cannot only provide a variety of life assistance, Rehabilitation care, psychological comfort and social support services for the elderly through intelligent decision-making and control, but also always protect the safety of the elderly and meet the multi-level and diversified service The needs of urban and rural elderly. We should play the leading comprehensive role of innovation driving role of information technology, accelerate the development of smart health care industry, improve people's livelihood and cultivate new economic drivers, On the 23rd of 2017, the Ministry of Industry, information Technology and information Technology, the Ministry of Civil Affairs and the Health and Family Planning Commission jointly issued the Action Plan for the Development of Smart ec Industry (2017-2022), which explicitly mentioned the year 2022, It will "basically form a smart health care industry system covering the whole life cycle. So far, the Smart Pension (SM) industry has completed its overall layout and entered the practical level. © 2023 IEEE.

关键词： Big data

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Fast Image matting based on matting pyramids and surrogate models 2

Fast Image matting based on matting pyramids and surrogate m...

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2nd international conference on Robotics, Artificial Intelligence and intelligent Control, RAIIC 2023

作者： Liang, Yihui Gou, Hongshan Liu, Guisong Huang, Sibo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Zhongshan China Guiyang Institute of Humanities and Technology College of Big Data and Information Engineering Guiyang China Southwestern University of Finance and Economics School of Computing and Artificial Intelligence Chengdu China Huizhou University Network and Information Centre Huizhou China

ISBN: (纸本)9798350328004

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time. © 2023 IEEE.

关键词： Pixels

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