2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第10页

Deep Iris Feature Extraction 深层虹膜特征提取

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415202

A. Hafner, P. Peer, Ž. Emeršič, Matej Vitek

引用次数: 9

A Deep Learning Module Design for Workspace Identification in Manufacturing Industry 面向制造业工作空间识别的深度学习模块设计

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415257

Jeong-Su Kim, Dong Myung Lee

引用次数: 1

Classification of diabetic walking through machine learning: Survey targeting senior citizens 通过机器学习对糖尿病患者行走的分类:针对老年人的调查

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415250

Y. Woo, Pizarroso Troncoso Carlos Andres, Hieyong Jeong, Choonsung Shin

{"title":"Classification of diabetic walking through machine learning: Survey targeting senior citizens","authors":"Y. Woo, Pizarroso Troncoso Carlos Andres, Hieyong Jeong, Choonsung Shin","doi":"10.1109/ICAIIC51459.2021.9415250","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415250","url":null,"abstract":"We have an interest in diabetes, a metabolic disorder in which the levels of glucose in the blood are very high. Recently, the number of senior citizens who are detected with this disease is rapidly increasing. Moreover, diabetes does not end by lowering the levels of glucose concentration in the blood since it also causes different health complications while the disease is active, reducing the lifespan of patients. Thus, this study proposed a method to predict the possibility to find diabetes at its early stages through machine learning. The dataset for training consisted of nine features of senior citizens’ walking data measured with the shoe-type IMU sensor at three different speeds (fast, slow and preferred) of 200 human subjects in their 60s-80s. With this, we created a program which is able to predict whether a patient has diabetes or not by using Machine Learning algorithms such as Logistic Regression, Support Vector Machine and Random Forest. We also compared the accuracies obtained for each algorithm and found that both Support Vector Machine and Logistic Regression models reached an 84% of accuracy. Through the analysis results, we determined the feature importance for learning, which showed high importance for fast walking features. It was discussed that this could be related to problems with diabetic plantar ulcers when patients suffered from diabetes.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134452352","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: 8

Controller module implementation to reduce interrupt in CNPC uplink 控制器模块的实现减少了中石油上行链路的中断

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415209

Gwonhan Mun, Tae-Chul Hong, Deaho Kim

引用次数: 0

Enhanced Machine Learning-based Inter Coding for VVC 基于增强机器学习的VVC内部编码

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415184

Martin Benjak, H. Meuel, Thorsten Laude, J. Ostermann

引用次数: 2

Learning to Recognize Masked Faces by Data Synthesis 通过数据合成学习识别蒙面

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415252

Ziyan Wang, Tae Soo Kim

{"title":"Learning to Recognize Masked Faces by Data Synthesis","authors":"Ziyan Wang, Tae Soo Kim","doi":"10.1109/ICAIIC51459.2021.9415252","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415252","url":null,"abstract":"Face coverings have become the new normal for people living through the global COVID-19 pandemic crisis. While wearing a mask is a necessary public health measure, the social phenomenon raises new challenges to existing face recognition models. In this work, we evaluate deep neural network approaches for the masked face recognition task. We find that current deep networks can not generalize successfully to recognizing faces with masks. To address this issue, we investigate the use of images of faces with simulated masks to train a deep neural network model for face recognition. We train our model using a collection of two face recognition datasets: the Labeled Faces in the Wild (LFW) dataset, the Real-world Masked Face Recognition (RMFR) dataset and the Simulated Masked Face Recognition (SMFR) dataset. We find that the data sampling strategy during training plays a significant role when the number of simulated examples is much greater than that of available real instances. We show that the model trained using a combination of real and simulated data accurately classifies masked faces with an accuracy of 99%.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114218043","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

Network Traffic Classification Using Ensemble Learning in Software-Defined Networks 基于集成学习的软件定义网络流量分类

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415187

Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho

{"title":"Network Traffic Classification Using Ensemble Learning in Software-Defined Networks","authors":"Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho","doi":"10.1109/ICAIIC51459.2021.9415187","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415187","url":null,"abstract":"Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086070","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: 5

A Deep Q-Learning Design for Energy Harvesting QoS Routing in IoT-enabled Cognitive MANETs 物联网认知manet中能量收集QoS路由的深度q -学习设计

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415210

Toan-Van Nguyen, T. Tran, Beongku An

{"title":"A Deep Q-Learning Design for Energy Harvesting QoS Routing in IoT-enabled Cognitive MANETs","authors":"Toan-Van Nguyen, T. Tran, Beongku An","doi":"10.1109/ICAIIC51459.2021.9415210","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415210","url":null,"abstract":"In this paper, we propose an energy harvesting quality-of-service (EH-QoS) routing protocol based on a deep Q-learning design in Internet-of-Things-enabled cognitive radio mobile ad hoc networks (IoT-CMANETs), where mobile nodes harvest energy from a multiple antennas power beacon for their routing and data transmission processes. A deep Q-learning network (DQN) is proposed to establish a QoS route, which avoids the affected region of a primary user. In the forwarding route request (RREQ) process, relying on the designed DQN, the proposed EH-QoS routing protocol unicasts a RREQ packet to the neighbor associated with a minimum $Q^{ast} -$ value satisfying energy, queue size of each node, the number of hops, and cognitive radio constraints. The $Q^{ast} -$ value of each link is obtained by optimizing joint residual energy and speed of all nodes belonging to this link. Simulation results show that the proposed EH-QoS routing protocol outperforms the state-of-the-art routing protocols in terms of control overhead, packet delivery ratio, routing delay, and energy consumption, arising as an effective protocol in IoT-CMANETs.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122891637","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

Analysis on the Channel Prediction Accuracy of Deep Learning-based Approach 基于深度学习方法的信道预测精度分析

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415201

Woo-Sung Son, D. Han

引用次数: 9

CNPC deinterleaver implementation to increase hardware logic utilization on FPGA 在FPGA上实现中石油脱交织器，提高硬件逻辑利用率

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415239

Gwonhan Mun, H. Kim, Deaho Kim

引用次数: 1