自主智能(英文)最新文献_第9页

Application of Artificial Intelligence in Monitoring the Use of Protective Masks 人工智能在防护口罩使用监测中的应用

自主智能(英文) Pub Date : 2021-10-17 DOI: 10.32629/jai.v4i2.500

Alexandre Pereira Junior, Thiago Pedro Donadon Homem

引用次数: 0

Artificial Intelligence and Human Condition: Opposing Entities or Complementary Forces? 人工智能与人类状况:对立的实体还是互补的力量?

自主智能(英文) Pub Date : 2021-10-09 DOI: 10.32629/jai.v4i2.497

Diego Felipe Arbeláez-Campillo, Jorge Jesús Villasmil Espinoza, M. J. Rojas-Bahamón

引用次数: 0

Spatiotemporal Information Fusion Method of User and Social Media Activity 用户与社交媒体活动的时空信息融合方法

自主智能(英文) Pub Date : 2021-09-26 DOI: 10.32629/jai.v4i2.485

Chao Yang, Liu Yang, Kunlun Qi

{"title":"Spatiotemporal Information Fusion Method of User and Social Media Activity","authors":"Chao Yang, Liu Yang, Kunlun Qi","doi":"10.32629/jai.v4i2.485","DOIUrl":"https://doi.org/10.32629/jai.v4i2.485","url":null,"abstract":"Social media check-in data contains a lot of user activity information. Understanding the types of activities and behavior of social media users has important research significance for exploring human mobility and behavior patterns. This paper studies the user activity classification method for Sina Weibo (a very popular Chinese social network service, referred to as “Weibo”), which combines image expression and spatiotemporal data classification technology to realize the identification of the activity behavior represented by the microblog check-in data. Firstly, the user activities represented by the Sina Weibo check-in data are divided into six categories according to POI attribute information: “catering”, “life services”, “campus”, “outdoors”, “entertainment” and “travel”; Then, through the Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN) classification methods, the image scene information and spatiotemporal information in the check-in data are fused to classify the activity behavior of microblog users. The experimental results show that the proposed method can significantly improve the accuracy of microblog user activity type recognition and provide more effective data support for accurately exploring human behavior activities.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41541392","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 Chinese-Urdu Machine Translation Based on Deep Learning 基于深度学习的中文-乌尔都语机器翻译研究

自主智能(英文) Pub Date : 2021-09-18 DOI: 10.32629/jai.v3i2.279

Zeshan Ali

{"title":"Research Chinese-Urdu Machine Translation Based on Deep Learning","authors":"Zeshan Ali","doi":"10.32629/jai.v3i2.279","DOIUrl":"https://doi.org/10.32629/jai.v3i2.279","url":null,"abstract":"Urdu is Pakistan 's national language. However, Chinese expertise is very negligible in Pakistan and the Asian nations. Yet fewer research has been undertaken in the area of computer translation on Chinese to Urdu. In order to solve the above problems, we designed of an electronic dictionary for Chinese-Urdu, and studied the sentence-level machine translation technology which is based on deep learning. The Design of an electronic dictionary Chinese-Urdu machine translation system we collected and constructed an electronic dictionary containing 24000 entries from Chinese to Urdu. For Sentence we used English as an intermediate language, and based on the existing parallel corpus of Chinese to English and English to Urdu, we constructed a bilingual parallel corpus containing 66000 sentences from Chinese to Urdu. The Corpus has trained by using two NMT Models (LSTM,Transformer Model) and the above two translation model were compared to the desired translation, with the help of bilingual valuation understudy (BLEU) score. On NMT, The LSTM Model is gain of 0.067 to 0.41 in BLEU score while on Transformer model, there is gain of 0.077 to 0.52 in BLEU which is better than from LSTM Model score. Furthermore, we compared the proposed model with Google and Microsoft translation.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974815","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: 3

The QR code intelligent positioning system of the LBS cloud platform in the Internet of things environment 物联网环境下LBS云平台的二维码智能定位系统

自主智能(英文) Pub Date : 2021-09-13 DOI: 10.32629/jai.v3i2.338

Xinyue Wang, Haibao Wang

引用次数: 0

The Status Quo of José Ortega y Gasset’s Supernatural Concepts: From the Perspective of Artificial Intelligence 约瑟夫·奥尔特加·加塞特超自然概念的现状:基于人工智能的视角

自主智能(英文) Pub Date : 2021-06-07 DOI: 10.32629/jai.v4i1.494

Antonio Luis Terrones Rodríguez

引用次数: 0

The Implementation of Hexagonal Robot Mapping and Positioning System Focuses on Environmental Scanning and Temperature Monitoring 六角机器人测绘定位系统的实现——聚焦环境扫描和温度监测

自主智能(英文) Pub Date : 2021-06-02 DOI: 10.32629/jai.v4i1.493

Cristina Alvarado-Torres, Esteban Velarde-Garcés, Orlando Barcia-Ayala

引用次数: 0

Fractional Order Modeling of 1,2,3 DOF Robot Dynamic 1,2,3自由度机器人动力学的分数阶建模

自主智能(英文) Pub Date : 2021-05-22 DOI: 10.32629/jai.v4i1.490

Israel Cerón-Morales

引用次数: 0

Data Analytics to Increase Performance in the Human Resources Area 提高人力资源领域绩效的数据分析

自主智能(英文) Pub Date : 2021-03-31 DOI: 10.32629/jai.v4i1.80

Sergio Henrique Monte Santo Andrade

引用次数: 0

Machine Learning, Deep Learning and Implementation Language in Geological Field 机器学习、深度学习与地质领域的实现语言

自主智能(英文) Pub Date : 2021-03-24 DOI: 10.32629/jai.v4i1.479

Yongzhang Zhou, Jun Wang, R. Zuo, Fan Xiao, W. Shen, Shugong Wang

{"title":"Machine Learning, Deep Learning and Implementation Language in Geological Field","authors":"Yongzhang Zhou, Jun Wang, R. Zuo, Fan Xiao, W. Shen, Shugong Wang","doi":"10.32629/jai.v4i1.479","DOIUrl":"https://doi.org/10.32629/jai.v4i1.479","url":null,"abstract":"Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot of geological big data research. It will make geological big data winged and change geology. Machine learning is a training process of model derived from data, and it eventually gives a decision oriented to a certain performance measurement. Deep learning is an important subclass of machine learning research. It learns more useful features by building machine learning models with many hidden layers and massive training data, so as to improve the accuracy of classification or prediction at last. Convolutional neural network algorithm is one of the most commonly used deep learning algorithms. It is widely used in image recognition and speech analysis. Python language plays an increasingly important role in the field of science. Scikit-Learn is a bank related to machine learning, which provides algorithms such as data preprocessing, classification, regression, clustering, prediction and model analysis. Keras is a deep learning bank based on Theano/Tensorflow, which can be applied to build a simple artificial neural network.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49589469","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