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2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献

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An intelligent Command and Control decision-making support model based on big data mining 基于大数据挖掘的智能指挥控制决策支持模型
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731297
Jin Wang, Kun Qin, Xing-seng Li, Hong-liang Gao, Wenjia Lu
{"title":"An intelligent Command and Control decision-making support model based on big data mining","authors":"Jin Wang, Kun Qin, Xing-seng Li, Hong-liang Gao, Wenjia Lu","doi":"10.1109/acait53529.2021.9731297","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731297","url":null,"abstract":"The decision-making process of traditional assistant decision-making model has the disadvantages of long decision-making time and slow convergence. Big data mining technology has the characteristics of extracting or mining hidden valuable information from complex and multi-scale massive data, and its convergence speed is fast based on artificial intelligence algorithm. Based on the original “six database” decision model and the characteristics of data mining, this paper designs a big data decision support model based on deep learning algorithm, and deeply expounds its working principle and operation process. This process provides an intelligent and information-based auxiliary means for commanders’ decision-making, and improves the real-time and accuracy of command and control decision-making. In addition, the further application of data mining and deep learning algorithm in command and control information system is also discussed.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"9 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":"125294900","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
Competitive swarm optimizer for Solving Flexible Jobshop Scheduling Problem 求解柔性作业车间调度问题的竞争群优化算法
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731219
Mingliang Wu, Dongsheng Yang, Zhile Yang, Yuanjun Guo
{"title":"Competitive swarm optimizer for Solving Flexible Jobshop Scheduling Problem","authors":"Mingliang Wu, Dongsheng Yang, Zhile Yang, Yuanjun Guo","doi":"10.1109/acait53529.2021.9731219","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731219","url":null,"abstract":"F1exible job shop scheduling problem (FJSP) is an extension of job shop scheduling problem (JSP) that has received increasing attention in recent decades. FJSP is a high-dimensional combinatorial optimization problem. Using accurate algorithms to solve them is a challenge and costly. The difference is that a meta-heuristic algorithm is an algorithm based on intuition or experience that gives a feasible solution to the problem at an acceptable cost (referring to calculation time and space). Particle Swarm optimization (PSO) is a classic meta-heuristic algorithm that has achieved many successful applications. However, it is easy to converge prematurely when solving high-dimensional problems. Competitive Swarm optimizer (CSO), as a variant of particle swarm optimization, has excellent global search capabilities to deal with high-dimensional problems. Therefore, this article uses CSO to solve FJSP. We introduced five other algorithms as a comparison to verify our algorithm. Numerical comparison results show that CSO can optimize all FJSP better overall.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"18 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":"129792830","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 In-depth Intelligent Optimization of Sponsored Search Advertising Content on E-commerce Platforms 电子商务平台赞助搜索广告内容深度智能优化研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731267
Qinglong Ge
{"title":"Research on In-depth Intelligent Optimization of Sponsored Search Advertising Content on E-commerce Platforms","authors":"Qinglong Ge","doi":"10.1109/acait53529.2021.9731267","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731267","url":null,"abstract":"With the rapid development of e-commerce market, online shopping has been integrated into many people’s daily life. In order to optimize the advertising content sponsored by e-commerce platform, we build the selling point keyword prediction model of advertising products based on multi-task deep learning. The experimental results show that compared with the basic model, the click-through rate of advertising products is improved by 0.48%. After introducing additional feature information, the AUC result of the model increased by 0.79%, effectively optimizing the advertising content of sponsored search, enhancing the user’s personalized buying experience, and providing new research ideas for the in-depth intelligent optimization of sponsored search by e-commerce platform.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"27 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":"129825875","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 Micro Video Character Recognition Based on Target Detection Technology 基于目标检测技术的微视频字符识别研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731196
Yue Lei
{"title":"Research on Micro Video Character Recognition Based on Target Detection Technology","authors":"Yue Lei","doi":"10.1109/acait53529.2021.9731196","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731196","url":null,"abstract":"With the rapid development of Internet short video platform, micro video is loved by more and more users because of its advantages such as small traffic and time period. However, the content of micro video on the Internet is complex, including much illegal and illegal content information. This research is an improved algorithm based on target detection technology and aiming at the characteristics of difficult character recognition in micro video. The algorithm combines Gabor wavelet transform algorithm and 2DPCA algorithm. Through MATLAB simulation analysis, it can be seen that the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"62 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":"132079635","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 Semi-supervised Modeling Method for Dioxin Emission Prediction Based on Random Forest 基于随机森林的二恶英排放预测半监督建模方法
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731260
Wen Xu, Jian Tang, Heng Xia, Jian Zhang, Wen Yu, J. Qiao
{"title":"A Semi-supervised Modeling Method for Dioxin Emission Prediction Based on Random Forest","authors":"Wen Xu, Jian Tang, Heng Xia, Jian Zhang, Wen Yu, J. Qiao","doi":"10.1109/acait53529.2021.9731260","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731260","url":null,"abstract":"Dioxin (DXN) is a kind of pollutants with cumulative effects in the municipal solid waste incineration (MSWI) process. Its emission concentration is difficult to detect online and in real-time, which restricts the operational optimization of the MSWI process. At the same time, it is difficult to meet actual needs through traditional supervised modeling methods because of the high time and economic cost of directly measuring DXN. Therefore, a DXN emission prediction model based on semi-supervised random forest (SSRF) is established to make full use of the unlabeled data obtained in the actual industrial process. First, the training subsets are acquired through randomly sampling the labeled data. Second, the training subsets are utilized to build multiple random forest (RF) models and pseudo-label the unlabeled data. Finally, the mixed samples composed of pseudo-labeled data and labeled data are used to train an RF model for predicting the DXN emission concentration. The proposed method is verified by the actual DXN dataset.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"53 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":"132102951","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
SocialBully: A Social Information-Driven Cyberbullying Detector with Similarity-Based Word Embedding SocialBully:一个基于相似度词嵌入的社会信息驱动的网络欺凌检测器
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731340
Zehua Zhao, Min Gao, F. Luo, G. Ranzi
{"title":"SocialBully: A Social Information-Driven Cyberbullying Detector with Similarity-Based Word Embedding","authors":"Zehua Zhao, Min Gao, F. Luo, G. Ranzi","doi":"10.1109/acait53529.2021.9731340","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731340","url":null,"abstract":"Cyberbullying depicts the form of bullying that is spread through information and communication technologies, most common of which is through the Internet. When compared to traditional bullying, cyberbullying can spread quicker and reach a wider audience and, because of this, can have severe effects on people’s mental health. In this paper, we propose a new cyberbullying detection method – SocialBully. SocialBully systematically integrates both users’ social information and their posted textual information to achieve a high detection accuracy. It uses a new word embedding method “SimWord” to represent words based on the similarity of their co-occurrence vectors and exploits a graph embedding method to obtain the social representation of users. Based on these representations, SocialBully uses a bidirectional Long Short-Term Memory to detect bullying texts. Extensive experiments are conducted on four real-world datasets to validate the effectiveness of the proposed method.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"1 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":"131360608","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
Arbitrary Scene Text Detection with Bezier Proposal 任意场景文本检测与贝塞尔建议
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731235
Yuan-Po Chen, Yihong Li
{"title":"Arbitrary Scene Text Detection with Bezier Proposal","authors":"Yuan-Po Chen, Yihong Li","doi":"10.1109/acait53529.2021.9731235","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731235","url":null,"abstract":"Scene text detection is widely studied in natural language processing since 2016, in which arbitrary scene text detection is always the difficulty. At present, to deal with the problem of how to detect arbitrary shape text, the semantic segmentation-based methods are widely used, but the post-processing and label generation operations are complex. Sparse R-CNN is a novel object detection framework with simple process and high accuracy, which can simplify post-processing by bipartite graph matching loss. Therefore, an arbitrary shape text detect method without any post-process based on Bezier proposal with Sparse R-CNN is proposed. Firstly, the feature pyramid network with attention mechanism is used to extract features, and then the processed features go into the Sparse R-CNN detection head to get the score and coordinates, and finally the detection results are visualized according to the score. The results on ICDAR2015 and CTW1500 datasets show that our method can detect arbitrary text effectively, and our method have higher accuracy and higher speed than other methods.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"68 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":"114247108","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
Intelligent Path Modeling for Large-Scale Multi-energy Microgrid Considering Demand-side Management 考虑需求侧管理的大型多能微电网智能路径建模
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731245
Hongyu Zhu, H. Goh, Tianhao Liu, Hang Dai, Dongdong Zhang, Thomas Wu
{"title":"Intelligent Path Modeling for Large-Scale Multi-energy Microgrid Considering Demand-side Management","authors":"Hongyu Zhu, H. Goh, Tianhao Liu, Hang Dai, Dongdong Zhang, Thomas Wu","doi":"10.1109/acait53529.2021.9731245","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731245","url":null,"abstract":"The energy microgrid couples multiple energy sources to effectively improve energy utilization efficiency and reduce carbon emissions. This paper takes the large-scale multi-energy microgrid as the research object. In order to solve the complexity of its physical model construction, an intelligent path modeling method is proposed. This method can automatically detect the energy flow path in the microgrid system, obtain the coupling matrix in the complex network, effectively reduce the error rate of manual modeling, and eliminate the structural nonlinearity caused by the coupling factors of the system. Then, this article comprehensively considers demand response and multi-energy storage technology, and conducts optimal dispatching research on large-scale energy microgrids. Numerical simulation shows the effectiveness and practicability of the model.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"34 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":"114591023","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
Artificial Intelligence Technology Supports the Following Research on the Generation of Subtitles for College English Teaching 人工智能技术支持大学英语字幕生成的后续研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731269
Guangmin Zuo, Yingying Qiu
{"title":"Artificial Intelligence Technology Supports the Following Research on the Generation of Subtitles for College English Teaching","authors":"Guangmin Zuo, Yingying Qiu","doi":"10.1109/acait53529.2021.9731269","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731269","url":null,"abstract":"In the process of college English teaching, the generation of subtitles in English class has gradually received more attention. The accuracy of subtitle generation determines the quality of English teaching. In order to optimize the subtitle generation technology for English teaching in university classrooms, this study uses the self attention mechanism of artificial intelligence technology to construct the subtitle generation model, and applies it to the sequence segmentation and paragraph segmentation of news data set. The results show that the subtitle generation model based on self attention mechanism can effectively complete the corresponding tasks with high accuracy, recall and FI-Score, and provide strong support for English teaching.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"222 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":"134243569","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
Prediction Model of Diabetes Based on Machine Learning 基于机器学习的糖尿病预测模型
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731180
Binhe Chen, Maosong Yan, Hongchuan Zhong, Bingwei He
{"title":"Prediction Model of Diabetes Based on Machine Learning","authors":"Binhe Chen, Maosong Yan, Hongchuan Zhong, Bingwei He","doi":"10.1109/acait53529.2021.9731180","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731180","url":null,"abstract":"Diabetes mellitus is a metabolic disorder caused by the absolute insufficient secretion of insulin and the disorder of insulin utilization. Diabetes mellitus will bring great harm to the organs, and the complications of diabetes will pose a great threat to the health and life of patients, and even lead to disability and death. The prediction of diabetes has always been a hot topic, but it is very difficult to predict. From a medical point of view, in this study, we aim to establish a diabetes prediction model based on machine learning and data mining. We first proposed a dual characteristic variable selection method based on single-factor regression and LightGBM, which can screen out the medical indicators affecting diabetes. On this basis, we built a single diabetes prediction model based on machine learning, and further studied XGBoost and ResNet. Finally, we used $text{GA}^{2}$Ms, XGBoost and ResNet to study the diabetes prediction model based on ensemble learning. The results show that the accuracy, F1 and AUC of the prediction model are 0.853, 0.888 and 0.875 respectively after five-fold cross-validation and comparative analysis, which are significantly better than other machine learning models. Therefore, the proposed method can accurately predict diabetes, so as to provide effective clinical auxiliary diagnosis for doctors, help doctors take preventive measures in advance, improve the survival rate of patients, and reduce the impact of diabetes on patients.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"76 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":"134019850","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
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