2022 5th Asia Conference on Machine Learning and Computing (ACMLC)最新文献_第2页

A Comparison of Clustering Method to Determine Depot Location for a Bike-sharing Operation 一种聚类方法在共享单车运营中确定仓库位置的比较

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00030

Kanokporn Boonjubut, H. Hasegawa

引用次数: 0

The Design of English Translation Software Based on Machine Learning Technology 基于机器学习技术的英语翻译软件设计

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00014

Xiaoshan Zeng

{"title":"The Design of English Translation Software Based on Machine Learning Technology","authors":"Xiaoshan Zeng","doi":"10.1109/ACMLC58173.2022.00014","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00014","url":null,"abstract":"With the increasing frequency of our country’s international communication and the popularization and penetration of mobile Internet into people’s work and life styles in modern society, the level of social informatization has also increased. In order not to be eliminated by this era, people must follow the pace of development of this era, always keep an eye on and receive the latest information from all over the world. Most of these materials are published on the Internet in foreign languages. Therefore, language has become the biggest obstacle for people to obtain information. As machine translation technology is restricted in terms of convenience and cost control, people’s need for machine translation or machine translation technology has become more and more urgent. This paper studies the design of English translation software (ETS) based on machine learning technology (MLT). By introducing MLT into ETS, a new neural machine translation method is proposed, and related experiments are used to test the effectiveness of the translation method. The designed translation software has been evaluated for translation quality. The experimental results show that as the arc length distribution increases, the translation quality (TTQ) decreases. The English translation software designed in this paper is of great importance to the research and development of machine translation.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131763130","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

Natural Language Processing in Advertising – A Systematic Literature Review 广告中的自然语言处理——系统文献综述

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00024

Vinh Truong

{"title":"Natural Language Processing in Advertising – A Systematic Literature Review","authors":"Vinh Truong","doi":"10.1109/ACMLC58173.2022.00024","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00024","url":null,"abstract":"Computational or programmatic advertising is the new way to advertise products and services online and in real-time. In this emerging type of advertising, Natural language processing (NLP) is a powerful tool for intelligently targeting and placing advertisements at the right time and in the right place for the right audience in a very short period. This study systematically reviewed journal articles, book chapters, and conference proceedings for the last ten years to find out what are the uses, approaches, and challenges that the researchers have been recently facing in making use of natural language processing techniques in the domain of advertising. It is found that in the majority of studies, information extraction and sentiment analysis are still the main focus areas. Only a small number of advanced artificial intelligence (AI) techniques, such as deep learning and speech synthesis, are used. In addition, most of the studies are based on traditional forms of advertising (such as search engines, websites, and job listings), excluding the newer forms of mobile and app-based advertising. The ongoing challenge in the current literature is applying natural language processing to automatically target advertisements.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678548","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

Mobile-Based Navigation Assistant for Visually Impaired Person with Real-time Obstacle Detection Using YOLO-based Deep Learning Algorithm 基于深度学习算法的视障人士实时障碍物检测移动导航助手

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00020

G. Catedrilla

{"title":"Mobile-Based Navigation Assistant for Visually Impaired Person with Real-time Obstacle Detection Using YOLO-based Deep Learning Algorithm","authors":"G. Catedrilla","doi":"10.1109/ACMLC58173.2022.00020","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00020","url":null,"abstract":"This project mainly aims to develop a mobile-based application for navigation with real-time obstacle detection to provide fair access to people with visual impairment to some activities, specifically navigating outdoors. It is a navigation mobile application equipped with speech and gesture recognition, to allow the people with visual impairment to access and use the application, and obstacle detection to provide audio prompts to the user, so they will know whenever an object or obstacle is within the frame of the phone camera. The research was structured and accomplished through different scientific and technological process and approach. With the use of Dialog flow, it was possible to create a speech recognition feature for the application, while YOLO algorithm allowed the process of object detection using mobile phone camera, possible. In this research, it was found out that the application was applicable to improving the navigation of the visually impaired, it is ideal that it serves as supplement to the white stick in order to improve their navigation experience. Also, this project would like to emphasize that researches that seeks to help person with disability be considered and conducted by other researchers.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128387315","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

Integrated Age Estimation Mechanism based on Decision-Level Fusion of Error and Deviation Orientation Model 基于决策层误差与偏差取向融合模型的综合年龄估计机制

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/acmlc58173.2022.00016

Fan Li, Y. Li, Pin Wang, Hong Chen, Wei Wang, Jie Xiao

{"title":"Integrated Age Estimation Mechanism based on Decision-Level Fusion of Error and Deviation Orientation Model","authors":"Fan Li, Y. Li, Pin Wang, Hong Chen, Wei Wang, Jie Xiao","doi":"10.1109/acmlc58173.2022.00016","DOIUrl":"https://doi.org/10.1109/acmlc58173.2022.00016","url":null,"abstract":"Age estimation based on machine learning has received lots of attention. Traditional age estimation mechanism focuses age error ignoring the deviation between the estimated age and real age due to disease. Pathological age estimation mechanism used age deviation as the training label to solve the above problem. However, it results in a larger error between the estimated age and real age in the normal control (NC) group. An integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem. Firstly, the traditional age and pathological age estimation mechanisms are weighted together. Then, their optimal weights are obtained by minimizing mean absolute error (MAE). Finally, with the optimal weight, the integrated age estimation mechanism (IAE) is built. Several representative age-related datasets are used for verification. The results show that the proposed age estimation mechanism achieves a good tradeoff effect of age estimation. It not only improves the classification ability of the estimated age, but also reduces the age estimation error of the NC group.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121917808","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

The Relationships among Green Perceived Value, Green Brand Image, Green Trust, and Green Purchase Intention: An Application Concerning Gogoro Electric Scooters in Taiwan 绿色感知价值、绿色品牌形象、绿色信任与绿色购买意愿之关系——以台湾Gogoro电动滑板车为例

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00025

C. Tsai, Po-Jiun Chiang

引用次数: 0

Copyright Page 版权页

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/acmlc58173.2022.00003

引用次数: 0

Forecasting for Wind Farm Energy Output in South Australia: A Comparative Analysis of Physical Methods and Deep Learning Methods 南澳大利亚风电场能源输出预测:物理方法和深度学习方法的比较分析

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00023

Yijia Zhang

引用次数: 0

Deep Learning for Detecting Malaria Parasites of Infected Red Blood Cells in Thin Blood Smear Images 深度学习检测薄血涂片图像中感染红细胞中的疟疾寄生虫

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/ACMLC58173.2022.00022

Wongsathon Naksuwan, Picha Suwannahitatorn, Chakrit Watcharopas, Pakaket Wattuya

{"title":"Deep Learning for Detecting Malaria Parasites of Infected Red Blood Cells in Thin Blood Smear Images","authors":"Wongsathon Naksuwan, Picha Suwannahitatorn, Chakrit Watcharopas, Pakaket Wattuya","doi":"10.1109/ACMLC58173.2022.00022","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00022","url":null,"abstract":"Malaria is a significant global health issue, with 241 million people infected and resulting in 627,000 deaths in 2020, officially reported by the World Health organization (WHO). In addition, during the Covid-19 pandemic, 47,000 people died because of a reluctance to receive treatment. In Thailand, Malaria still spreads in distant communities where restrictions are in place for military deployments due to the high risk of infection. Therefore, the 8,000 or so military personnel who deploy on missions close to the country’s borders are actively monitored by the Armed Forces Research Institute of Medical Sciences (AFRIMS). The lack of medical personnel in these remote settlements, however, slows detection and adversely impacts the health and lives of military soldiers working in these locations. Because of their comparative effectiveness to traditional learning algorithms, deep learning technologies are used as a tool for medical screenings. In this study, the YOLOv3 and the DenseNetl21 are used to diagnose malaria infection using thin film blood smears. The results show that testing on normal slide datasets can distinguish between normal red blood cells and malaria-infected red blood cells in four species, including Falciparum, Vivax, Malariae, and Ovale, with accuracy for infection classification at 98.08%, sensitivity at 98.05%, and specificity at 99.73%. Furthermore, when the hard slide dataset is examined, the infection classification’s accuracy, sensitivity, and specificity are 98.48%, 90%, and 99.24%, respectively. In normal slide datasets, this detection method yields a positive hit rate for malaria-infected red blood cells and normal red blood cells of 98.05% for the former and 92.65% for the latter.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130466757","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 Method of Predicting Occupancy in Commercial Building Based on Machine Learning 基于机器学习的商业建筑入住率预测方法

2022 5th Asia Conference on Machine Learning and Computing (ACMLC) Pub Date : 2022-12-01 DOI: 10.1109/acmlc58173.2022.00010

Qin Zou, Nan Li, Baowei Xu, Xintong Li

{"title":"A Method of Predicting Occupancy in Commercial Building Based on Machine Learning","authors":"Qin Zou, Nan Li, Baowei Xu, Xintong Li","doi":"10.1109/acmlc58173.2022.00010","DOIUrl":"https://doi.org/10.1109/acmlc58173.2022.00010","url":null,"abstract":"As a reference value, the occupancy guides the building Automation System (BAS) operation, which can significantly reduce energy consumption. However, the occupancy counts of commercials fluctuate dynamically with time, and how to gain the occupancy clusters and accurately predict the occupancy counts has yet to be well solved. To solve the above problems, this research proposes a prediction method for the occupancy counts of commercial buildings based on the integration of Wi-Fi connection counts data, categories of weekdays and holidays, outdoor climate data sets, and the combination of K-Means and decision tree algorithm. First, the K-means algorithm was used to cluster to obtain the representative occupancy daily clusters. Subsequently, the decision tree algorithm recognizes the clusters’ generation rules and constructs the prediction model. The validation experiments were conducted in a commercial building in Chongqing, China. The results showed that the prediction model had an accuracy of 95.24%, with better robustness than independent data sources. The prediction results can provide a practical reference for formulating BAS’s operation control and commercial operation scheme in the low carbon emission reduction environment.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075651","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