2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献_第2页

Gleason Grading of Prostate Cancer Based on Improved AlexNet 基于改进AlexNet的前列腺癌Gleason分级

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731223

Zhenfeng Li, Yuchun Li, Yu Zhang, Mengxing Huang, Jing-Gui Chen, Siling Feng, Zhiming Bai

{"title":"Gleason Grading of Prostate Cancer Based on Improved AlexNet","authors":"Zhenfeng Li, Yuchun Li, Yu Zhang, Mengxing Huang, Jing-Gui Chen, Siling Feng, Zhiming Bai","doi":"10.1109/acait53529.2021.9731223","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731223","url":null,"abstract":"Prostate cancer is a common malignant tumor in male genitourinary system, its morbidity is increasing in recent years. Puncture pathological examination with Gleason scoring is the ultimate means of diagnosing prostate cancer. Early detection of prostate cancer is obviously very important for the treatment and prognosis of the cancer. However, the pathological image of prostate cancer has a complicated texture structure, especially the difference between Gleason Grade 3 and Gleason Grade 4. Therefore, pathological images with a Gleason score of 7 are difficult to distinguish between \"3+4\" and \"4+3\". The misjudgment of \"3+4\" and \"4+3\" impact on quality of life in patients with prostate cancer after operation can be profound. In order to improve the classification accuracy of histopathological images of prostate cancer especially for detecting \"3+4\" and \"4+3\", this paper proposed an image classification model based on improved AlexNet. On the basis of ALexNet, Res1_block and Res2_block structures are added to extract the features of pathological images. Experimental results show that our approach can automatically classify prostate cancer pathological images, and the test accuracy can reach 78.4%.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Self-adaptive Multi-task Differential Evolution Algorithm 一种自适应多任务差分进化算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731130

Kangjia Qiao, Jing J. Liang, Kunjie Yu, B. Qu, C. Yue, Gongping Li

{"title":"A Self-adaptive Multi-task Differential Evolution Algorithm","authors":"Kangjia Qiao, Jing J. Liang, Kunjie Yu, B. Qu, C. Yue, Gongping Li","doi":"10.1109/acait53529.2021.9731130","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731130","url":null,"abstract":"This paper proposes a new self-adaptive scheme and differential evolution based evolutionary multi-task optimization algorithm to address multiple different optimization problems or tasks simultaneously. The proposed algorithm assigns a specific population and a transfer rate for each task and uses the differential evolution strategies to update each population. Compared with traditional evolutionary multi-task optimization algorithms that adopt a fixed transfer rate, the proposed algorithm uses a self-adaptive scheme to dynamically adjust transfer rate, which reduces the harm of negative transfer on the evolutionary direction of the population. The population is simultaneously driven by the information from the intra-task and other tasks. Based on the performance of the two evolutionary strategies, the population adaptively adjusts the transfer rate to complete the high-quality knowledge transfer process. The experiment is conducted on the single-objective multi-task test suite. The results show that the proposed algorithm can find more accurate solutions with a faster convergence rate in comparison with several state-of-the-art evolutionary multi-task optimization algorithms.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval Optimization for Integrated Demand Response in A Battery-Equipped Household Multi-Energy System 电池户用多能系统综合需求响应的区间优化

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731271

Diankun Hu, Yongxin Su, Mao Tan

引用次数: 0

Research on the Application of Intelligent Learning Algorithms in Network Security Situation Awareness and Prediction Methods 智能学习算法在网络安全态势感知与预测方法中的应用研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731205

Zhihua Chen

引用次数: 2

Adaptive Self-Paced Transfer Learning for COVID-19 Diagnosis with CXR Images 基于CXR图像的自适应自节奏迁移学习新冠肺炎诊断

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731278

Y. Yao, Qi Zhu, Haizhou Ye, Daoqiang Zhang

{"title":"Adaptive Self-Paced Transfer Learning for COVID-19 Diagnosis with CXR Images","authors":"Y. Yao, Qi Zhu, Haizhou Ye, Daoqiang Zhang","doi":"10.1109/acait53529.2021.9731278","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731278","url":null,"abstract":"Coronavirus Disease 2019 (COVID-19) has become an unprecedented public health crisis since December of 2019. Compared with real-time reverse transcription polymerase chain reaction (rRT-PCR), the computer-aided diagnosis machine learning algorithm based on medical images can vastly ease the burden on clinicians. Even so, despite existing hundreds of millions of confirmed cases worldwide, there has not been a mature, large scale, high quality, single standard shared image data set yet, which can lead to some problems. For instance, 1) Because the sources of medical images and the collection standards are not guaranteed, features extracted by the neural network may not be very ideal. 2) Due to the small number of samples, some outliers (e.g., blurry medical images, inconspicuous symptoms) may significantly descend the performance of the model. To address these problems, we propose an adaptive self-paced transfer learning (ASPTL) algorithm in this paper. Specifically, inspired by the process of human learning from easy to difficult, we also evaluated the learning difficulty of the samples. Samples with no obvious disease features or wrong labels are relatively difficult to diagnose, and the samples that are easy to diagnose are selected adaptively in the iterative process. In addition, we adopt transfer learning to select easy to learn samples on the pre-trained network by self-paced learning, and gradually fine-tune the pre-trained model in an iterative way. We designed two experiments to validate the ASPTL algorithm’s performance on COVID-19. The reult prove the effectiveness on solving mentioned problems.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130785562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Ship Track Clustering Method Based on Optimized Spectral Clustering Algorithm 基于优化谱聚类算法的航迹聚类方法研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731124

Hongdan Liu, Y. Liu, Lanyong Zhang, H. Sun

{"title":"Research on Ship Track Clustering Method Based on Optimized Spectral Clustering Algorithm","authors":"Hongdan Liu, Y. Liu, Lanyong Zhang, H. Sun","doi":"10.1109/acait53529.2021.9731124","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731124","url":null,"abstract":"Maritime traffic monitoring is of great significance to the navigation safety of ships, but the main method of supervision by maritime supervision departments is still human monitoring. In order to improve the efficiency of supervision, this paper studies and analyzes the ship trajectory clustering algorithm, which can intelligently classify the unlabeled trajectory data in AIS. Aiming at the problems of low accuracy in detecting abnormal ship trajectory behavior and sensitivity to outliers and noise points in track clustering in existing clustering algorithms, this paper proposes an improved spectral clustering algorithm for ship trajectory clustering. On the one hand, the algorithm improves the affinity distance function to make the clustering more stable and reduce the problem of sensitivity to outliers, on the other hand, it also improves the K-nearest neighbor part in the spectral clustering, the trajectory is mapped to the nodes in the weight graph, and then the distance distribution is calculated by setting a threshold. Finally, based on the data of navigable merchant ships at the Port of Dover in the United Kingdom and the Port of Calais in France, it is verified that the optimized spectral clustering algorithm can improve the computational efficiency and accuracy for ship trajectory clustering, and maintain clustering consistency, the better visual clustering results can be obtained.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134458904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Safety Inspection Technology of High-Speed Railway Catenary System Combined with Kalman Filtering 结合卡尔曼滤波的高速铁路接触网系统安全检测技术研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731154

Ling-Chao Zhang

引用次数: 0

Human Activity Recognition Based on Wavelet-CNN Architecture 基于小波- cnn结构的人体活动识别

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731338

Dongwen Zhang, Lin Zhang, Qingwu Yi, Lu Huang, Guanghua Zhang

{"title":"Human Activity Recognition Based on Wavelet-CNN Architecture","authors":"Dongwen Zhang, Lin Zhang, Qingwu Yi, Lu Huang, Guanghua Zhang","doi":"10.1109/acait53529.2021.9731338","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731338","url":null,"abstract":"Human activity recognition (HAR) has become an important research field in pervasive computing and has attracted extensive attention from researchers at home and abroad. However, traditional recognition methods rely heavily on artificial feature extraction, which greatly affects the generalization ability of the model. Therefore, this paper designs a deep learning model based on wavelet transform and convolution neural networks. Firstly, the waveform data of multi-channel sensor is decomposed into low-frequency and high-frequency components by wavelet transform (WT) after window sliding segmentation, which are combined as the input data of network model. Then, convolution neural networks with different convolution kernels are used to extract multidimensional features efficiently, and the max-pooling layers are used to filter the interference noise caused by human unconscious jitter. Finally, through the output classification of full connection layer, the accurate recognition of human activity state is realized. In order to verify the effectiveness of the designed model, we evaluate the performance of the model from the convergence speed, loss and accuracy of the model, and compare it with the more advanced recognition model on the public dataset of OPPORTUNITY. Finally, the proposed architecture Wavelet-CNN achieves 91.65% F-measure and has higher activity recognition ability.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129901445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mechanism Analysis of Antitumor Drugs based on MTDNN 基于MTDNN的抗肿瘤药物作用机制分析

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731305

Jun Yang

引用次数: 0

Deep Palmprint Image Quality Assessment Network 深度掌纹图像质量评估网络

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9730893

Xiao Sun, Lunke Fei, Jie Wen, Xiaozhao Fang, Na Han, Yong Xu

引用次数: 1