2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)最新文献_第3页

Variational Graph Network with Negative Sampling for Drug Interaction Prediction 药物相互作用预测的负抽样变分图网络

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212734

Jiawei Xu

{"title":"Variational Graph Network with Negative Sampling for Drug Interaction Prediction","authors":"Jiawei Xu","doi":"10.1109/AINIT59027.2023.10212734","DOIUrl":"https://doi.org/10.1109/AINIT59027.2023.10212734","url":null,"abstract":"Drug-drug interactions (DDIs) are crucial for pharmaceutical research. Drug combinations could have negative medication consequences that endanger patient safety and public health. Deep learning techniques, especially graph neural networks, have demonstrated their effectiveness in graph learning, and thus have been widely applied in predicting DDIs. However, existing methods are limited in two aspects: 1) They heavily rely on the validity of graph structure, therefore fail to perform robust learning from noisy data 2) They make assumptions that all unobserved graph edges are irrelevant. However, it is more rational to view them as unlabeled links instead of negative ones. To address these challenges, we formulated the DDIs prediction problem as a graph link prediction task and proposed to train graph neural networks with variational learning and structure-aware negative sampling. Extensive experiments showed that our approach achieved improved performance than multiple baselines. Importantly, we performed a case study to evaluate the quality of our model's novel predictions by performing a literature-based evaluation of new hits. Evaluation results offer concrete examples that reaffirmed the medical usefulness of our approach.","PeriodicalId":276778,"journal":{"name":"2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"323 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369268","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

A Multi-machine Cooperation Full Coverage Path Planning Method in Agriculture 农业多机协同全覆盖路径规划方法

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212883

Jinliang Li, Weibo Ren

引用次数: 0

Performance Comparison of Bird Classification Models in Dongtan, China Based on YOLOv7, SVC and XGBoost 基于YOLOv7、SVC和XGBoost的东滩鸟类分类模型性能比较

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212724

Junjie Yang

{"title":"Performance Comparison of Bird Classification Models in Dongtan, China Based on YOLOv7, SVC and XGBoost","authors":"Junjie Yang","doi":"10.1109/AINIT59027.2023.10212724","DOIUrl":"https://doi.org/10.1109/AINIT59027.2023.10212724","url":null,"abstract":"This paper mainly explores the performance differences of different models in the classification of birds in Chongming Dongtan, Shanghai. The research object of this paper is a data set composed of 2,900 images from three categories (Anas formosa, Anas platyrhynchos and swan), which are among the most abundant and representative bird species in Dongtan. I constructed VGG model, VGG-SVC fusion model and VGG-XGBoost fusion model where I input the results of the last hidden layer of VGG into the SVC and XGBoos. The experimental results show that the bird classification model constructed has achieved high accuracy in the test set. Among them, VGG model has the best performance, with its accuracy of 79.92%, followed by VGG-SVC model with the accuracy of 74.62% and VGG- XGBoost fusion model with the accuracy of 70.45%. Then, I integrated these three models into a ensemble classifier by soft voting method, and its accuracy rate was 81.82%. Finally, I compared the ensemble classifier with YOLOv7, and found that the accuracy of YOLOv7 was 91.26% which was quite good. The results show that both the deep learning model and the traditional machine learning model can be used for bird classification, and the combination of feature extractor and classifier can further improve the accuracy of the model. The research results of this paper provide useful reference for the practical application of bird classification in Dongtan and even around the world.","PeriodicalId":276778,"journal":{"name":"2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102584","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

Construction and Verification of Image Datasets for Fire Hazards in Cultural Relics Buildings 文物建筑火灾隐患图像数据集的构建与验证

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212612

Chen Zhong, Hui Liu, Qingdian Chen, Tingting Li

{"title":"Construction and Verification of Image Datasets for Fire Hazards in Cultural Relics Buildings","authors":"Chen Zhong, Hui Liu, Qingdian Chen, Tingting Li","doi":"10.1109/AINIT59027.2023.10212612","DOIUrl":"https://doi.org/10.1109/AINIT59027.2023.10212612","url":null,"abstract":"The fire safety of cultural relic building(CRB) is an important topic in the field of cultural relic protection. In recent years, more and more researchers have applied technologies such as image processing and machine learning to the early detection and alarm of CRB fires. However, the image data of fire and interference sources in CRB scenes is scarce. This article proposes a scheme for constructing a fire hazard image dataset based on the characteristics of CRB scenes. On this basis, in order to meet the requirements of timeliness, accuracy, and reliability for fire detection in CRBs, a lightweight FireNet fire detection network was used to train the FireNet dataset. The obtained training parameters were applied to the CRB Fire Hazard Dataset for testing, and the recognition accuracy reached 70.78% without training. The above results indicate that the network ensures both lightweight and high level of accuracy in fire detection of CRBs. At the same time, it also proves that there is a significant difference in the image fire detection effect between CRB scenes and other building scenes, and the construction of a CRB fire hazard image dataset is of great significance.","PeriodicalId":276778,"journal":{"name":"2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132694777","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

Audio Source Verification Method Based on Structural Re-parameterization Network 基于结构重参数化网络的音频源验证方法

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212478

Yingqiu Zhang, Da Luo

引用次数: 0

An Image Fusion-Based Multi-Target Discrimination Method in Minimum Resolution Cell for Ground-Based Synthetic Aperture Radar 陆基合成孔径雷达最小分辨率单元图像融合多目标识别方法

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212484

Yanbo Cheng, Haifeng Huang, Tao Lai, Pengfei Ou

引用次数: 0

Flexible Charging Control Algorithm of Electric Vehicle Group in Substation Based on Semi-Supervised Integrated Pruning Algorithm 基于半监督综合剪枝算法的变电站电动汽车群柔性充电控制算法

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212810

Rongzhi Sun, Bingyin Lei, Ouyang Qiang, Yumeng Zhao

{"title":"Flexible Charging Control Algorithm of Electric Vehicle Group in Substation Based on Semi-Supervised Integrated Pruning Algorithm","authors":"Rongzhi Sun, Bingyin Lei, Ouyang Qiang, Yumeng Zhao","doi":"10.1109/AINIT59027.2023.10212810","DOIUrl":"https://doi.org/10.1109/AINIT59027.2023.10212810","url":null,"abstract":"Poor control performance of flexible charging output power fluctuation of electric vehicles will reduce the charging transmission efficiency of electric vehicles. In order to improve the stability of output power, a flexible charging control algorithm for electric vehicles in the platform area based on semi-supervised integrated pruning algorithm is proposed. Fitting the relationship between temperature and battery capacity, analyzing the active power of flexible charging of electric vehicles in the platform area, and constructing the flexible charging load model of electric vehicles in the platform area; According to this model, the stable state of flexible charging is adjusted, and the flexible charging control of electric vehicle group is realized. The experimental results show that the control performance of the proposed algorithm is strong, and the active power load value of the flexible charging process of the electric vehicle group in the station area is close to the expected power load value.","PeriodicalId":276778,"journal":{"name":"2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968999","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 Control Algorithm of Unmanned Surface Vehicle Line Tracking Based on FPGA 基于FPGA的无人水面车辆线路跟踪控制算法研究

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212843

Chuanyu Fu, Mingyong Yuan, Daoyou Lin, Fu Jian

{"title":"Research on Control Algorithm of Unmanned Surface Vehicle Line Tracking Based on FPGA","authors":"Chuanyu Fu, Mingyong Yuan, Daoyou Lin, Fu Jian","doi":"10.1109/AINIT59027.2023.10212843","DOIUrl":"https://doi.org/10.1109/AINIT59027.2023.10212843","url":null,"abstract":"The former domestic unmanned surface vehicle carrying multibeam bathymetry system to carry out measurement tasks at sea needs to follow the route issued by the upper computer, which is affected by the wind and waves at sea and other external factors, and will deviate from the target route and cannot meet the requirements of full coverage of multibeam measurement area, thus affecting the multibeam measurement results. In response to the above problems, the navigation path of the unmanned surface vehicle needs to be tracked and controlled. According to the requirements, a suitable mathematical model of unmanned surface boat motion is constructed, and the power system is controlled by incremental PID algorithm and codic algorithm based on FPGA to make real-time tracking and adjustment of the whole route. The unmanned surface vehicle “Sailing” of Hainan University is used as the research object, and the MATLAB simulation test is carried out for the designed control system, and the unmanned surface vehicle “Sailing” is used in the sea near Qinglan Port of Wenchang to conduct the real ship verification test. The feasibility and effectiveness of the designed method were verified by analyzing and comparing the simulation results and test results.","PeriodicalId":276778,"journal":{"name":"2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116336950","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

Classification and Diagnosis of Alzheimer's Disease Based on Functional and Structural MRI 基于功能和结构MRI的阿尔茨海默病的分类和诊断

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212973

B. Zhu, Qi Li, Chunjie Guo, Yu Yang

引用次数: 0

UWB and IMU Fusion Localization System Based on Unmanned Vehicles in Complex Factory Areas 基于复杂厂区无人驾驶车辆的UWB与IMU融合定位系统

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI: 10.1109/AINIT59027.2023.10212909

Chengxian Zhou, Qingyuan Xia

引用次数: 0