The 12th International Conference on Advances in Information Technology最新文献_第4页

Interactive Online Configurator via Boolean Satisfiability Modeling 基于布尔可满足性建模的交互式在线配置器

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3468785

Tao Tao, D. Plaisted

引用次数: 1

Augmented Reality with Mask R-CNN (ARR-CNN) inspection for Intelligent Manufacturing 面向智能制造的ar - cnn (ARR-CNN)增强现实检测技术

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3468788

Tawatchai Perdpunya, Siranee Nuchitprasitchai, P. Boonrawd

{"title":"Augmented Reality with Mask R-CNN (ARR-CNN) inspection for Intelligent Manufacturing","authors":"Tawatchai Perdpunya, Siranee Nuchitprasitchai, P. Boonrawd","doi":"10.1145/3468784.3468788","DOIUrl":"https://doi.org/10.1145/3468784.3468788","url":null,"abstract":"A machine is an essential factor for industrial production. Industry 4.0 is the revolution that causes improvement of machines to have higher efficiency. Accordingly, inspection and maintenance are becoming more important. However, most of factories are not changed the operating process, there is no data logging for evaluation and analysis for preventive maintenance. This research aims to develop a model for machine inspection using augmented reality with object detection and marker techniques on real world machines and mask R-CNN algorithm allowing inspector to perform inspections. This study, we demonstrate the process of development of the proposed model by showing steps of data acquisition from a machine in a factory. The dataset is images of machines in different perspectives, and they were used for training and testing the model. The testing is done on a mobile device of an inspector. With computer vision technique and the proposed model, the instant precision tracking and detection are provided. Then the trained model is transferred to the mobile devices for testing without any modification by an expert. Some images of machines are randomly selected to verify the accuracy of the model. The result shows that the efficiency of the model is acceptable in real usage.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132741333","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: 4

A Development of Personality Recognition Model from Conversation Voice in Call Center Context 呼叫中心会话语音个性识别模型的发展

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3469180

Nakorn Srinarong, J. Mongkolnavin

{"title":"A Development of Personality Recognition Model from Conversation Voice in Call Center Context","authors":"Nakorn Srinarong, J. Mongkolnavin","doi":"10.1145/3468784.3469180","DOIUrl":"https://doi.org/10.1145/3468784.3469180","url":null,"abstract":"A call center is an important communication channel between a business and its customers. The call center staffs are responsible for resolving customer problems and fulfilling their needs. It is undeniable that customer satisfaction can be increased if personalized services relating to their characteristics such as personality are provided. Researches are suggesting that a person's personality can be recognized from his/her conversational voice. Thus, a machine learning model that recognizes each customer's personality from one's conversational voice in a call center would enable the cell center to give that one appropriate response. This study focuses on developing a personality recognition model to predict each MPI (Maudsley Personality Inventory) personality dimension from each conversational voice. The MPI personality dimension includes E-scale (representing extraversion and introversion) and N-scale (representing neuroticism and stability). Audio files of conversational voice were collected from 92 volunteers instructed to make conversation in the simulated call center context. Logistic regression, LinearSVC, Random forest, and Artificial neural networks were used in the modeling process. The result shows that the model generated by using Artificial neural networks has the best performance on predicting the E-scale. The model has the positive predictive value (Introversion prediction) and the negative predictive value (Extraversion prediction) equal to 0.71 and 0.75, respectively. No model shows satisfying performance on neuroticism and stability prediction. This study shows a piece of evidence that extraversion and introversion in MPI, which have implications in businesses, can be effectively recognized from each person's conversational voice made through call centers. The model can be beneficial in many business applications such as call center management, personalized product offering, and personalized advertisement.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314736","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

Privacy Preservation Techniques for Sequential Data Releasing 顺序数据发布的隐私保护技术

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3470468

Surapon Riyana, Noppamas Riyana, Srikul Nanthachumphu

引用次数: 0

Unsupervised Segmentation of Non-Intersecting Manifolds 非相交流形的无监督分割

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3470467

Subhadip Boral, Sumedha Dhar, Ashish Ghosh

{"title":"Unsupervised Segmentation of Non-Intersecting Manifolds","authors":"Subhadip Boral, Sumedha Dhar, Ashish Ghosh","doi":"10.1145/3468784.3470467","DOIUrl":"https://doi.org/10.1145/3468784.3470467","url":null,"abstract":"Manifold learning has been an important research area as from literature it is evident that patterns in most real-life data sets can be embedded in low-dimensional space while maintaining the original structure of high-dimensional space. This work concentrates on one of the major research areas of manifold learning, which is the segregation of manifolds where more than one non-intersecting manifolds are present. The proposed method presents a solution to the problem by detecting the number of manifolds in a dataset using the Laplacian graph matrix and segregate the manifolds using agglomerative clustering. Eventually, locally linear embedding has been used for dimensionality reduction of every individual manifold in such a way that manifolds remain segregated and also holds the original global structure. The proposed method achieves finer results when applied on benchmark synthetic data sets SCurve, SwissRoll, Helix and real-life datasets COIL-20, optical digit recognition, att_faces, extended Yale Face Database B. While the state of the art methods fails to detect the number of manifolds in a dataset, the proposed method not only eclipses the performance of them but also carry the separable structure in the lower dimensional space.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124840835","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

A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-Privacy 一种比LKC-Privacy更安全高效的RFID数据集隐私保护模型

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3469853

Surapon Riyana, Noppamas Riyana

引用次数: 3

Precipitation Nowcasting Using Deep Learning on Radar Data Augmented with Satellite Data 基于卫星数据增强雷达数据的深度学习降水临近预报

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-29 DOI: 10.1145/3468784.3470469

Wikom Tosiri, Nutnaree Kleawsirikul, Patamawadee Leepaisomboon, Natnapat Gaviphatt, Hidetomo Sakaino, P. Vateekul

引用次数: 5

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning 基于深度学习的混合商业垃圾图像粒度估计

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-11 DOI: 10.1145/3468784.3471273

Phongsathorn Kittiworapanya, Kitsuchart Pasupa, P. Auer

引用次数: 3

Open source disease analysis system of cactus by artificial intelligence and image processing 基于人工智能和图像处理的开源仙人掌病害分析系统

The 12th International Conference on Advances in Information Technology Pub Date : 2021-06-07 DOI: 10.1145/3468784.3469075

Kanlayanee Kaweesinsakul, Siranee Nuchitprasitchai, Joshua M. Pearce

{"title":"Open source disease analysis system of cactus by artificial intelligence and image processing","authors":"Kanlayanee Kaweesinsakul, Siranee Nuchitprasitchai, Joshua M. Pearce","doi":"10.1145/3468784.3469075","DOIUrl":"https://doi.org/10.1145/3468784.3469075","url":null,"abstract":"There is a growing interest in cactus cultivation because of numerous cacti uses from houseplants to food and medicinal applications. Various diseases impact the growth of cacti. To develop an automated model for the analysis of cactus disease and to be able to quickly treat and prevent damage to the cactus. The Faster R-CNN and YOLO algorithm technique were used to analyze cactus diseases automatically distributed into six groups: 1) anthracnose, 2) canker, 3) lack of care, 4) aphid, 5) rusts and 6) normal group. Based on the experimental results the YOLOv5 algorithm was found to be more effective at detecting and identifying cactus disease than the Faster R-CNN algorithm. Data training and testing with YOLOv5S model resulted in a precision of 89.7% and an accuracy (recall) of 98.5%, which is effective enough for further use in a number of applications in cactus cultivation. Overall the YOLOv5 algorithm had a test time per image of only 26 milliseconds. Therefore, the YOLOv5 algorithm was found to suitable for mobile applications and this model could be further developed into a program for analyzing cactus disease.","PeriodicalId":341589,"journal":{"name":"The 12th International Conference on Advances in Information Technology","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132504881","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