{"title":"Proceedings of the 5th International Conference on Machine Learning and Machine Intelligence, MLMI 2022, Hangzhou, China, September 23-25, 2022","authors":"","doi":"10.1145/3568199","DOIUrl":"https://doi.org/10.1145/3568199","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88920217","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}
{"title":"Text Categorization of Filipino Tweets Using Naïve Byes Algorithm","authors":"Sharmaine Justyne Ramos Maglapuz, L. L. Lacatan","doi":"10.1145/3490725.3490732","DOIUrl":"https://doi.org/10.1145/3490725.3490732","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"56 1","pages":"44-49"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89871780","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}
{"title":"Sentiment Analysis of Facebook Posts Towards Good Governance Using SVM Algorithm: A Framework Proposal","authors":"Regina Garcia Almonte, L. L. Lacatan","doi":"10.1145/3490725.3490746","DOIUrl":"https://doi.org/10.1145/3490725.3490746","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"410 1","pages":"140-144"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76768855","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}
{"title":"Coompetency-Based Mapping Tool in Personnel Management System using Analytical Hierarchy Process","authors":"L. L. Lacatan, Gary Mendoza Penuliar","doi":"10.1145/3490725.3490734","DOIUrl":"https://doi.org/10.1145/3490725.3490734","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"21 1","pages":"57-64"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74449468","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}
{"title":"IoT and RS Techniques for Enhancing Water Use Efficiency and Achieving Water Security","authors":"Y. Al-Mulla, Taif B. Al-Badi","doi":"10.1145/3490725.3490738","DOIUrl":"https://doi.org/10.1145/3490725.3490738","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"58 1","pages":"83-88"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82127324","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}
Behavior design for Automated Guided Vehicles (AGV) systems is an active research area, fundamental for robotics, industrial systems automation. The rise of machine learning neural systems and deep learning make promising results in a multitude of areas including warehouse environments.In this paper, several different policies will be obtained by using reinforcement learning on a heterogeneous swarm robotic system, applied for solving logistical tasks in Automated Guided Vehicles. More specifically, two different types of agents will be used: the vehicles that collect, transport and deposit their package and the traffic lights that regulate the number of vehicles that circulate on the tracks. The main objective of our work is to learn simultaneously two different control policies, one for each kind of agent.The obtained policies have shown their ability to correctly learn the package transport behavior in addition to balance traffic flow to facilitate agent mobility and avoid collisions. Furthermore, the scalability of the system and the behavior performance for different number of vehicles has been shown.
{"title":"Swarm AGV Optimization Using Deep Reinforcement Learning","authors":"Pilar Arques-Corrales, F. A. Gregori","doi":"10.1145/3426826.3426839","DOIUrl":"https://doi.org/10.1145/3426826.3426839","url":null,"abstract":"Behavior design for Automated Guided Vehicles (AGV) systems is an active research area, fundamental for robotics, industrial systems automation. The rise of machine learning neural systems and deep learning make promising results in a multitude of areas including warehouse environments.In this paper, several different policies will be obtained by using reinforcement learning on a heterogeneous swarm robotic system, applied for solving logistical tasks in Automated Guided Vehicles. More specifically, two different types of agents will be used: the vehicles that collect, transport and deposit their package and the traffic lights that regulate the number of vehicles that circulate on the tracks. The main objective of our work is to learn simultaneously two different control policies, one for each kind of agent.The obtained policies have shown their ability to correctly learn the package transport behavior in addition to balance traffic flow to facilitate agent mobility and avoid collisions. Furthermore, the scalability of the system and the behavior performance for different number of vehicles has been shown.","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"21 1","pages":"65-69"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88154630","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}
{"title":"Comparison of Evolutionary Strategies for Reinforcement Learning in a Swarm Aggregation Behaviour","authors":"Jasmina Rais Martínez, F. A. Gregori","doi":"10.1145/3426826.3426835","DOIUrl":"https://doi.org/10.1145/3426826.3426835","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"19 1","pages":"40-45"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82878759","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}
{"title":"Proceedings of the 2nd International Conference on Machine Learning and Machine Intelligence, MLMI 2019, Jakarta, Indonesia, September 18-20, 2019","authors":"","doi":"10.1145/3366750","DOIUrl":"https://doi.org/10.1145/3366750","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"07 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85960575","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}
{"title":"Modeling and Feature Analysis of Air Traffic Management Technical Support System Based on Weighted Complex Network","authors":"Jiayu Quan, Songchen Han, Peng Li, Binbin Liang, Lisha Yu, Kunshan Yang","doi":"10.1145/3366750.3366763","DOIUrl":"https://doi.org/10.1145/3366750.3366763","url":null,"abstract":"","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"70 1","pages":"68-73"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86071892","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}
Pub Date : 2017-01-01Epub Date: 2017-09-07DOI: 10.1007/978-3-319-67389-9_26
Jinquan Sun, Yinghuan Shi, Yang Gao, Dinggang Shen
In this paper, we investigate if the MR prostate segmentation performance could be improved, by only providing one-point labeling information in the prostate region. To achieve this goal, by asking the physician to first click one point inside the prostate region, we present a novel segmentation method by simultaneously integrating the boundary detection results and the patch-based prediction. Particularly, since the clicked point belongs to the prostate, we first generate the location-prior maps, with two basic assumptions: (1) a point closer to the clicked point should be with higher probability to be the prostate voxel, (2) a point separated by more boundaries to the clicked point, will have lower chance to be the prostate voxel. We perform the Canny edge detector and obtain two location-prior maps from horizontal and vertical directions, respectively. Then, the obtained location-prior maps along with the original MR images are fed into a multi-channel fully convolutional network to conduct the patch-based prediction. With the obtained prostate-likelihood map, we employ a level-set method to achieve the final segmentation. We evaluate the performance of our method on 22 MR images collected from 22 different patients, with the manual delineation provided as the ground truth for evaluation. The experimental results not only show the promising performance of our method but also demonstrate the one-point labeling could largely enhance the results when a pure patch-based prediction fails.
{"title":"A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling.","authors":"Jinquan Sun, Yinghuan Shi, Yang Gao, Dinggang Shen","doi":"10.1007/978-3-319-67389-9_26","DOIUrl":"10.1007/978-3-319-67389-9_26","url":null,"abstract":"<p><p>In this paper, we investigate if the MR prostate segmentation performance could be improved, by only providing one-point labeling information in the prostate region. To achieve this goal, by asking the physician to first click one point inside the prostate region, we present a novel segmentation method by simultaneously integrating the boundary detection results and the patch-based prediction. Particularly, since the clicked point belongs to the prostate, we first generate the location-prior maps, with two basic assumptions: (1) a point closer to the clicked point should be with higher probability to be the prostate voxel, (2) a point separated by more boundaries to the clicked point, will have lower chance to be the prostate voxel. We perform the Canny edge detector and obtain two location-prior maps from horizontal and vertical directions, respectively. Then, the obtained location-prior maps along with the original MR images are fed into a multi-channel fully convolutional network to conduct the patch-based prediction. With the obtained prostate-likelihood map, we employ a level-set method to achieve the final segmentation. We evaluate the performance of our method on 22 MR images collected from 22 different patients, with the manual delineation provided as the ground truth for evaluation. The experimental results not only show the promising performance of our method but also demonstrate the one-point labeling could largely enhance the results when a pure patch-based prediction fails.</p>","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"10541 ","pages":"220-228"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67389-9_26","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36647955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：