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ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 203-216页

作者： Siyi XUN Yan ZHANG Sixu DUAN Mingwei WANG Jiangang CHEN Tong TONG Qinquan GAO Chantong LAM Menghan HU Tao TAN Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Jinan Branch of China Telecom Co.Ltd. Jinan 250000China School of Physics and Electronics Shandong Normal UniversityJinan 250000China Department of Dardiovascular Medicine Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310000China Clinical School of Medicine Hangzhou Normal UniversityHangzhou 310000China Hangzhou Institute of Cardiovascular Diseases Hangzhou 310000China Shanghai Key Laboratory of Multidimensional Information Processing Shanghai 200000China School of Communication&Electronic Engineering East China Normal UniversityShanghai 200000China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghai 200000China College of Physics and Information Engineering Fuzhou UniversityFuzhou 350000China

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

关键词： Brain tumor MRI U-net Segmentation attention mechanism Deep learning

Intrusion Detection Based on Bidirectional Long Short-Term Memory with attention mechanism

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Computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing capabilities.Cloud computing has traditionally played an important role in establishing IoT.However,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic distribution.However,IoT networks are vulnerable to unwanted assaults because of their open and shared nature.As a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available.In this study,we examined the time-related aspects of network traffic data.We presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset.We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

EEG Emotion Recognition Using an attention mechanism Based on an Optimized Hybrid Model

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Computers, Materials & Continua 2022年第11期73卷 2697-2712页

作者： Huiping Jiang Demeng Wu Xingqun Tang Zhongjie Li Wenbo Wu Brain Cognitive Computing Lab School of Information EngineeringMinzu University of ChinaBeijing100081China Case Western Reserve University USA

Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.

关键词： Emotion recognition EEG signal optimized hybrid model attention mechanism

CNN-LSTM based on attention mechanism for brake pad remaining life prediction

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Mechanical Engineering Science 2022年第2期4卷 30-38,I0005页

作者： Shuo WANG Zhenliang YU Guangchen XU Sisi CHEN Yingkou Institute of Technology School of Mechanical and Power Engineering YingkouChina

In order to predict the remaining service life of brake pads accurately and efficiently,and to achieve intelligent warning,this paper proposes a CNN-LSTM brake pad remaining life prediction model based on an attention mechanism.The model constructs a non-linear relationship between brake pad features such as brake temperature,brake oil pressure and brake speed and brake pad wear data through convolutional neural network(CNN)and long and short term memory network(LSTM),as well as capturing the time dependence that exists in the brake pad wear sequence.The attention mechanism is also introduced to assign different weight values to the features output from multiple historical moments,highlighting the features with high saliency and avoiding the influence of invalid features,so as to improve the prediction effect of the remaining brake pad life.The results show that the proposed CNN-LSTM-attention model can effectively predict the remaining life of brake pads,with the mean absolute error MAE value of 0.0048,root mean square error RMSE value of 0.0059 and coefficient of determination R2 value of 0.9636;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,the coefficient of determination R2 values are closest to 1,with an improvement of 8.26%,5.25%,3.99%and 1.85%respectively,enabling more effective monitoring and intelligent warning of the remaining brake pad life.

关键词： attention mechanism CNN-LSTM brake pads life prediction

Video expression recognition based on frame-level attention mechanism

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High Technology Letters 2023年第2期29卷 130-139页

作者：陈瑞 TONG Ying ZHANG Yiye XU Bo College of Information&Communication Engineering Nanjing Institute of TechnologyNanjing 211167P.R.China Jiangsu Future Network Innovation Research Institute Nanjing 211111P.R.China

Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.

关键词： facial expression recognition(FER) video sequence attention mechanism feature extraction enhanced feature VGG network image classification neural network

Using BiLSTM with attention mechanism to automatically detect self-admitted technical debt

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Frontiers of Computer Science 2021年第4期15卷 49-60页

作者： Dongjin YU Lin WANG Xin CHEN Jie CHEN School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China

Technical debt is a metaphor for seeking short-term gains at expense of long-term code quality.Previous studies have shown that self-admitted technical debt,which is introduced intentionally,has strong negative impacts on software development and incurs high maintenance overheads.To help developers identify self-admitted technical debt,researchers have proposed many state-of-the-art methods.However,there is still room for improvement about the effectiveness of the current methods,as self-admitted technical debt comments have the characteristics of length variability,low proportion and style diversity.Therefore,in this paper,we propose a novel approach based on the bidirectional long short-term memory(BiLSTM)networks with the attention mechanism to automatically detect self-admitted technical debt by leveraging source code comments.In BiLSTM,we utilize a balanced cross entropy loss function to overcome the class unbalance problem.We experimentally investigate the performance of our approach on a public dataset including 62,566 code comments from ten open source projects.Experimental results show that our approach achieves 81.75%in terms of precision,72.24%in terms of recall and 75.86%in terms of F1-score on average and outperforms the state-of-the-art text mining-based method by 8.14%,5.49%and 6.64%,respectively.

关键词： technical debt self-admitted technical debt long short-term memory attention mechanism natural language processing

A DGA domain names detection modeling method based on integrating an attention mechanism and deep neural network

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Cybersecurity 2018年第1期1卷 697-709页

作者： Fangli Ren Zhengwei Jiang Xuren Wang Jian Liu Institute of Information Engineering Chinese Academy of Sciences100093 BeijingChina University of Chinese Academy of Sciences 100093 BeijingChina College of Information Engineering Capital Normal University100048 BeijingChina

Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.

关键词： Domain generation algorithm Malware attention mechanism Deep learning

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Survey of Action Recognition Based on attention mechanism

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Survey of Action Recognition Based on Attention Mechanism

ADFNet: A two-branch robotic grasping network based on attention mechanism

2021中国智能自动化大会(CIAC2021)

作者： Zhixuan Wu Nan Ma Yongqiang Yao Guoping Zhang Beijing Key Laboratory of Information Service Engineering Beijing Union University College of Robotics Beijing Union University

attention mechanism for action recognition is to enhance data by using attention mechanism so as to recognize human action in video. This research has been intensely investigated in the field of computer vision, which has attracted extensive attention from academia and industry. It is also the premise of intelligent interaction and human-computer cooperation, which helps the machine to perceive the external environment. In the past decade, especially the emergence of deep learning technology, great progress has been made in this field. Therefore,it is necessary to make a comprehensive review of the recent work. This paper summarizes the research background and application fields, typical datasets and experimental evaluation, and compares various attention mechanism for human action feature representation and action classification algorithms. Finally, we look forward to the development prospect of human action recognition in self-driving vehicles. This paper provides ideas for the follow-up research on the understanding of the behavior of self-driving vehicles, and makes self-driving vehicles become interactive wheeled robots.

关键词： attention mechanism Human action recognition Self-driving Body language interaction

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ADFNet: A two-branch robotic grasping network based on atten...