Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference最新文献_第4页

Technology-Enhanced Systemic Quality Assurance with the Aid of Text-Based Emotion Recognition of Facebook Comments for Higher Education Institutions 基于文本的高校Facebook评论情感识别技术增强的系统质量保证

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582137

Jhon Bryan J. Cantil, Kristine Mae M. Adlaon

{"title":"Technology-Enhanced Systemic Quality Assurance with the Aid of Text-Based Emotion Recognition of Facebook Comments for Higher Education Institutions","authors":"Jhon Bryan J. Cantil, Kristine Mae M. Adlaon","doi":"10.1145/3582099.3582137","DOIUrl":"https://doi.org/10.1145/3582099.3582137","url":null,"abstract":"One of the difficult and recently-emerging problems in the realm of natural language processing is the recognition and analysis of emotions (NLP). A current area of research involves identifying a person's emotional state through textual data in addition to recognizing emotions from face and auditory records. Numerous disciplines, including higher education institutions, can use the study of emotions to their advantage. This is especially true given the widespread usage of social media in today's world, when everything is done online. This information could be very helpful in guiding an organization's decisions. The abundant text found in social media, blogs, and other places can be used to explore different text mining findings, such as emotions. This study tackles on making use of the vast amount of information available online especially in social media platforms through the development of iMosyon, an emotion recognition system to help aid higher institutions in their decision-making process. Experimental results were executed and researchers then decided to use the Support Vector Machine (SVM) model due to the fact that it received the highest accuracy score of 78% among the classifiers. The beta testing which made use of Evaluation of Performance Functionality and Software Product Quality shows an overall rating of 4.5 out of 5.0 that indicates that the respondents accepted its functionality and the feedback was good.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134409243","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

Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 基于CRF、HMM和LSTM的词性自动标注研究

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582103

Farhad Aydinov, Igbal Huseynov, Sofiya Sayadzada, S. Rustamov

{"title":"Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts","authors":"Farhad Aydinov, Igbal Huseynov, Sofiya Sayadzada, S. Rustamov","doi":"10.1145/3582099.3582103","DOIUrl":"https://doi.org/10.1145/3582099.3582103","url":null,"abstract":"Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130958209","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

Developing a Hybrid Neural Network for Part-Of-Speech Tagging and Named Entity Recognition 词性标注与命名实体识别的混合神经网络研究

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582101

Joshua Andre Huertas Gonzales, J-Adrielle Enriquez Gustilo, Glenn Michael Vequilla Nituda, Kristine Mae M. Adlaon

引用次数: 1

Effects of transfer learning for handwritten digit classification in a small training sample size situation 小样本情况下迁移学习对手写体数字分类的影响

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582119

Y. Mitani, Naoki Yamaguchi, Y. Fujita, Y. Hamamoto

{"title":"Effects of transfer learning for handwritten digit classification in a small training sample size situation","authors":"Y. Mitani, Naoki Yamaguchi, Y. Fujita, Y. Hamamoto","doi":"10.1145/3582099.3582119","DOIUrl":"https://doi.org/10.1145/3582099.3582119","url":null,"abstract":"A deep learning approach is believed to be one of the most useful for image pattern recognition. Generally, deep learning requires a large number of samples. In particular, it is prone to overlearning when the number of training samples is small. However, it is common for practical pattern recognition problems to use a limited number of training samples. One way to design a deep neural network with such a small number of training samples is to use transfer learning. Transfer learning, known to be pre-trained with a large number of samples, and its pre-trained neural networks are expected to be applicable to other pattern recognition problems, especially when the number of training samples is small. It is difficult to develop a handwritten character classification system because handwritten characters are not always readily available and the number of samples is generally small. In this paper, we examine effects of transfer learning for handwritten digit classification under a small number of training samples. Experimental results show that transfer learning is more effective than convolutional neural networks (CNNs) in classifying handwritten digits.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018908","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

Practical Path of Application of Artificial Intelligence Technology in Vocational Education 人工智能技术在职业教育中应用的实践路径

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582132

Ke-sheng Liu, Lifang Su

{"title":"Practical Path of Application of Artificial Intelligence Technology in Vocational Education","authors":"Ke-sheng Liu, Lifang Su","doi":"10.1145/3582099.3582132","DOIUrl":"https://doi.org/10.1145/3582099.3582132","url":null,"abstract":"In the era of artificial intelligence, vocational education must accelerate its own reform to dynamically adapt to the needs of changing skills. In the face of the era of artificial intelligence, vocational education should first adjust the training objectives, then change the teaching mode, deepen the training mode and supplement the teaching content. This research, with the social influence of artificial intelligence technology as the background, is based on the author's real record of his own experience in vocational college, and on the basis of his own experience, carries out a research on the application of artificial intelligence technology in vocational college teaching. The research suggests that the reform path of vocational education based on artificial intelligence technology is: macro policy support for the future planning; Oriented to the employment of vocational college students; Readjustment of teaching content centered on students. Implementation measures of artificial intelligence teaching system construction in vocational schools: (1) From work to human-computer interaction: content system of artificial intelligence teaching in vocational schools; (2) From informatization to intelligentization: the technical system of artificial intelligence teaching in vocational schools; (3) From hard technology to soft power: The method system of artificial intelligence teaching in vocational schools.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749665","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

Analyzing Design Typicality by Image Classification with Deep Learning 基于深度学习的图像分类设计典型性分析

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582125

Hung-Hsiang Wang, Yun-Yun Hung, Yunpeng Shen

引用次数: 0

Reinforcement Learning at Design of Electronic Circuits: Review and Analysis 电子电路设计中的强化学习:回顾与分析

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582140

M. Ivanova, A. Rozeva, Angel Ninov, M. A. Stosovic

引用次数: 0

Forecasting Credit Card Defaults Using Light Gradient Boosting Machine with Dart Algorithm 基于Dart算法的光梯度增强机预测信用卡违约

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582130

Haoming Wang

{"title":"Forecasting Credit Card Defaults Using Light Gradient Boosting Machine with Dart Algorithm","authors":"Haoming Wang","doi":"10.1145/3582099.3582130","DOIUrl":"https://doi.org/10.1145/3582099.3582130","url":null,"abstract":"With the rapid development of financial services and technologies, credit cards have been increasingly used for personal daily consumption and small loans. However, bad debts caused by credit card defaults remarkably affect the healthy development of financial markets. Therefore, forecasting potential credit card defaults is of great significance with respect to financial stability and economic order. For this purpose, we propose a machine learning method based on Light Gradient Boosting Machine to detect credit card defaults in this paper. DART algorithm is utilized in our model instead of the traditional gradient boosting tree. The model is trained and evaluated using the dataset provided by American Express in the Kaggle competition American Express - Default Prediction. Based on feature analysis and engineering, raw data with 190 descriptors are transformed into data with 2358 descriptors, and are used to train 3 LightGBM models with different hyper-parameters. By applying the model ensemble and pseudo-label technique, the competition metric of our method reaches 0.80029/0.80767 on the public/private test set. This score ranks 106/4874 (top 2.2%), and can get a silver medal in the Kaggle competition.","PeriodicalId":222372,"journal":{"name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","volume":"777 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124965160","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

PCB Micro-Soldering Status Inspection System Research based on Deep Learning 基于深度学习的PCB微焊接状态检测系统研究

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582104

J. Shim, Y. Ha, JiHee Park, Yeung-hak Lee

引用次数: 0

Crafting tasteful experiences: Designing artificial intelligence and voice user interfaces for home delivery contexts 打造有品位的体验:为家庭交付环境设计人工智能和语音用户界面

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI: 10.1145/3582099.3582127

Caroline Lövqvist, Maja Pinter, Montathar Faraon, Victor Villavicencio

引用次数: 0