发布求助
文献互助 智能选刊 最新文献

Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence最新文献

筛选
英文 中文
A Convolutional Neural Network for Dental Panoramic Radiograph Classification 基于卷积神经网络的牙科全景x线片分类
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461607
J. Faure, A. Engelbrecht
{"title":"A Convolutional Neural Network for Dental Panoramic Radiograph Classification","authors":"J. Faure, A. Engelbrecht","doi":"10.1145/3461598.3461607","DOIUrl":"https://doi.org/10.1145/3461598.3461607","url":null,"abstract":"Radiographs are X-rays of the craniofacial area, used for orthodontic diagnosis and treatment planning. Analysis of radiographs is a manual process. Public medical centers in developing countries such as South Africa experience a bottleneck in the analysis of these radiographs, due to excessive numbers of patients and severe shortages in orthodontic radiologists that serve at these public medical centers. Access to dental diagnostics is therefore becoming an ever-increasing problem in rural communities. This paper reports on the first phase of a framework to automate the analysis of panoramic radiographs, which are X-rays of the frontal croniofacial area. This first phase automates the process to predict whether a captured panoramic radiograph is workable or not, i.e. whether automated analysis of the X-ray can proceed. A is trained on a large set of panoramic radiographs, and results show that the prediction accuracy of this is very good in identifying workable radiographs.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114439890","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
Management of Traffic Signals using Deep Reinforcement Learning in Bidirectional Recurrent Neural Network in ITS 基于双向递归神经网络的交通信号管理
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461608
A. Paul, S. Mitra
{"title":"Management of Traffic Signals using Deep Reinforcement Learning in Bidirectional Recurrent Neural Network in ITS","authors":"A. Paul, S. Mitra","doi":"10.1145/3461598.3461608","DOIUrl":"https://doi.org/10.1145/3461598.3461608","url":null,"abstract":"The traffic flow management is primarily done through traffic signals, whose inefficient control causes numerous problems, such as long waiting time and huge waste of energy. To improve traffic flow efficiency, obtaining real-time traffic information as an input and dynamically adjusting the traffic signal duration accordingly is essential. Among the existing methods, Deep Reinforcement Learning (DRL) has shown to be the most effective solution. In this paper, a dynamic mechanism to control the traffic signal of a large scale road network is proposed using policy gradient method. A single agent is trained with spatio–temporal data of the multiple intersections of the network to alleviate congestion. The proposed system is implemented in two different types of deep bidirectional Recurrent Neural Network (RNN) - Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The simulation experiments demonstrate that the proposed system could reduce traffic congestion in terms of different simulation metrics during high density traffic flows.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114466223","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: 9
Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning 基于异构信息网络和对抗学习的跨领域推荐
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461609
Chang Su, Zongchao Hu, Xianzhong Xie
{"title":"Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning","authors":"Chang Su, Zongchao Hu, Xianzhong Xie","doi":"10.1145/3461598.3461609","DOIUrl":"https://doi.org/10.1145/3461598.3461609","url":null,"abstract":"In this paper, based on the heterogeneous information network, we propose a cross-domain recommendation model by integrating adversarial learning (Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning, CDR-HA). Using information from other domains to alleviate the target data sparseness of the domain can improve the accuracy and performance of recommendations. In this paper, we focus on the cross-domain recommendation. Firstly, due to the differences in the feature distributions of the same users in different domains, we use the HIN2Vec algorithm to extract the user's feature distribution in the network based on the heterogeneous information network. Secondly, we propose a multi-domain feature filtering method, which maximizes the difference in the distribution of different domains based on Wasserstein Distance to preserve the differences in the feature distributions of users in different domains. Then, separately establish a classifier for each domain, we consider the results of the two classifiers comprehensively, and take the best as the final result. We apply the proposed model to two datasets and experimental results demonstrate that our approach outperforms state-of-the-art recommender baselines.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117105346","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: 2
Deep Learning Approach for Prediction of Learning Disability 学习障碍预测的深度学习方法
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461611
Poonam Dhamal, S. Mehrotra
{"title":"Deep Learning Approach for Prediction of Learning Disability","authors":"Poonam Dhamal, S. Mehrotra","doi":"10.1145/3461598.3461611","DOIUrl":"https://doi.org/10.1145/3461598.3461611","url":null,"abstract":"One of the most common disorders in the world is learning disorder. Learning disorders are a disease that affects learning skills. The person with learning difficulties also understands, accept and recognize things in different ways. This can lead to problems learning new knowledge, skills and placing them into practice. It is therefore important to build a framework that can accurately recognize learning disabilities. This research goal is to perform Multilayer Perceptron of deep learning methods for predicting learning disorders (LD).","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132776081","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
Feature Selection and Feature Extraction: Highlights 特征选择和特征提取:亮点
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461606
Hiu-Man Wong, Xingjian Chen, Hiu-Hin Tam, Jiecong Lin, Shixiong Zhang, Shankai Yan, Xiangtao Li, Ka-chun Wong
{"title":"Feature Selection and Feature Extraction: Highlights","authors":"Hiu-Man Wong, Xingjian Chen, Hiu-Hin Tam, Jiecong Lin, Shixiong Zhang, Shankai Yan, Xiangtao Li, Ka-chun Wong","doi":"10.1145/3461598.3461606","DOIUrl":"https://doi.org/10.1145/3461598.3461606","url":null,"abstract":"In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction. Specifically, the objective is to obtain the non-redundant and informative set of input features (also known as attributes or predictor variables) for downstream data science tasks. In this study, we highlight the existing approaches in both feature selection and feature extraction. In particular, benchmark comparisons are conducted for independent evaluations.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"391 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133157766","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
Automatic Test Case Generation Method Based on Improved Whale Optimization Algorithm 基于改进鲸鱼优化算法的测试用例自动生成方法
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461600
Jing Wang, Weidong Zhao
{"title":"Automatic Test Case Generation Method Based on Improved Whale Optimization Algorithm","authors":"Jing Wang, Weidong Zhao","doi":"10.1145/3461598.3461600","DOIUrl":"https://doi.org/10.1145/3461598.3461600","url":null,"abstract":"∗In view of the slow convergence speed and parameter control of the existing heuristic algorithm in the automatic test case generation, this paper proposed to apply the whale optimization algorithm(Abbreviation: WOA) to the automatic test case generation, and used chaos strategy to improve WOA, and In order to solve the problem that WOA initialization is not uniform and easy to fall into local optimal solution, uses chaos initialization was used instead of random algorithm to initialize the population and solved the problem of uneven distribution of particles, consequently when the optimal value fell into the local optimal solution, the chaos disturbance operation was carried out on the optimal value. Based on this, an automatic test case generation method based on improved whale algorithm was proposed. This method aimed at one path at a time and used the improved whale optimization algorithm to optimize the population and find the optimal value.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128837592","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
Optimization of the Retardance in Dextran-citrate Coated Ferrofluids Using PSO and SA 用PSO和SA优化右旋糖酐-柠檬酸盐包被铁磁流体的缓速
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461601
Jing-Fung Lin, J. Sheu
{"title":"Optimization of the Retardance in Dextran-citrate Coated Ferrofluids Using PSO and SA","authors":"Jing-Fung Lin, J. Sheu","doi":"10.1145/3461598.3461601","DOIUrl":"https://doi.org/10.1145/3461598.3461601","url":null,"abstract":"Single or double coating citrate and dextran on the Fe3O4 ferrofluids (FFs) have been conducted for biomedical application such as hyperthermia and magnetic resonance imaging. The magnetic retardance of dextran-citrate (DC) coated FFs was measured and magnetic heating effect in alternating magnetic field was investigated previously. Conducting experiment by uniform design; enabling the formula to fit with experimental data of retardance through stepwise regression (SR) analysis. The developed regression model had highly predictable ability with a high correlation coefficient R of 0.99989 between measured and predicted retardances. In order to find the maximum retardance, intelligent search methods including particle swarm optimization (PSO) and simulated annealing (SA) were used. The optimized parametric combinations were determined as [0.0750, 75.7945, 0.3225, 0.6500] and [0.0750, 75.844, 0.323, 0.65], respectively, corresponding to Fe3O4 concentration, coating temperature, citrate mass, and dextran mass. The corresponding maximum retardances were found as 119.6576° and 119.6558°. Overall, PSO algorithm was more effective than SA to optimize the retardance of the DC coated FFs.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123605639","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 Comparison of Nearest Distance Optimization and Ant Colony Optimization for Order Picking in a Multi-Aisle Warehouse 多通道仓库拣货的最近距离优化与蚁群优化比较
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461599
Lukas Bertini, Kai-Uwe Krause, T. Hanne, Rolf Dornberger
{"title":"A Comparison of Nearest Distance Optimization and Ant Colony Optimization for Order Picking in a Multi-Aisle Warehouse","authors":"Lukas Bertini, Kai-Uwe Krause, T. Hanne, Rolf Dornberger","doi":"10.1145/3461598.3461599","DOIUrl":"https://doi.org/10.1145/3461598.3461599","url":null,"abstract":"Today, warehouses and the IT infrastructure behind them ensure smooth processing of customer orders throughout the day in all supply chains. These orders can consist of one item up to hundreds of items. The size and heterogeneity of warehouses also impedes the fastest possible processing of all orders. This research compares the nearest distance optimization heuristic and ant colony optimization to find out whether one or the other route leads to faster picking times in several scenarios depending on the warehouse size or the number of items on the picking list. For this purpose, we chose the widespread multi-aisle layout of a warehouse for our study assuming that only one human worker is involved in picking the items on the list.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683919","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 on Vespa Mandarinia's Invasion in the State of Washington 美国华盛顿州大黄蜂入侵研究
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461612
Ye Qi
{"title":"Research on Vespa Mandarinia's Invasion in the State of Washington","authors":"Ye Qi","doi":"10.1145/3461598.3461612","DOIUrl":"https://doi.org/10.1145/3461598.3461612","url":null,"abstract":"In recent years, a new invasive species, Vespa Mandarinia has become a problem for the State of Washington, the U.S.A. and regions near it. In this research, we used Kernel Density Estimation, Natural Language Processing and Convolution Neural Network(CNN) to evaluate the geographical and textual data of civilian reports and in what way we can detect new invasion cases without a professional's presence. The results of the research show that we could perform prediction with data available but it could be biased. However, image classification techniques based on CNN could be an incentive for more data input, therefore lead to a better simulation and estimation. The method of our research also indicates that these techniques may have great applicability to other invasive species.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134488197","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
Negative Learning in Ant Colony Optimization: Application to the Multi Dimensional Knapsack Problem 蚁群优化中的负学习:在多维背包问题中的应用
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2021-04-10 DOI: 10.1145/3461598.3461602
Teddy Nurcahyadi, C. Blum
{"title":"Negative Learning in Ant Colony Optimization: Application to the Multi Dimensional Knapsack Problem","authors":"Teddy Nurcahyadi, C. Blum","doi":"10.1145/3461598.3461602","DOIUrl":"https://doi.org/10.1145/3461598.3461602","url":null,"abstract":"In this paper we continue our recent work on the development of a negative learning component for ant colony optimization, which is a metaheuristic algorithm that is mostly based on positive learning, that is, on learning from positive examples. In particular, we apply our approach to the well-known multi dimensional knapsack problem as a test case. The obtained results show that our negative learning approach significantly outperforms the standard ant colony optimization approach.","PeriodicalId":408426,"journal":{"name":"Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765952","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信