2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)最新文献_第6页

Optimizing baseline in USBL using Costas hopping to increase navigation precision in shallow water 利用Costas跳频优化USBL基线，提高浅水导航精度

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721736

Nhat Hoang Bach, Lan Hai, Giang Tran Quang, Due Nguyen Van, Le Ha Vu, Trung Trinh Xuan

引用次数: 2

UAVs Handover Decision using Deep Reinforcement Learning 基于深度强化学习的无人机切换决策

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721627

Y. Jang, S. M. Raza, Hyunseung Choo, Moonseong Kim

引用次数: 3

An Investigation of The 5G LDPC and Polar Decoding Performance in Spatial Correlated MIMO-OFDMA System 空间相关MIMO-OFDMA系统中5G LDPC和极性解码性能研究

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721755

N. T. Nga, Chu Huu Khanh, Nguyen Quy Tho

引用次数: 2

Multiclass Imbalanced Handling using ADASYN Oversampling and Stacking Algorithm ADASYN过采样和堆叠算法的多类不平衡处理

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721632

Yoga Pristyanto, A. F. Nugraha, Akhmad Dahlan, Lucky Adhikrisna Wirasakti, Aditya Ahmad Zein, Irfan Pratama

{"title":"Multiclass Imbalanced Handling using ADASYN Oversampling and Stacking Algorithm","authors":"Yoga Pristyanto, A. F. Nugraha, Akhmad Dahlan, Lucky Adhikrisna Wirasakti, Aditya Ahmad Zein, Irfan Pratama","doi":"10.1109/IMCOM53663.2022.9721632","DOIUrl":"https://doi.org/10.1109/IMCOM53663.2022.9721632","url":null,"abstract":"Class imbalance conditions in datasets are common in real-world problems. Class imbalance is a condition where the number of classes in the dataset used in the classification process has a significant difference in number. In theory, most single classifiers have a weakness against class imbalance conditions in datasets, especially those with multiclass types, so their performance cannot be maximized. This study proposes two approaches to overcome the problem of multiclass imbalanced, namely the use of ADASYN (Adaptive Synthetic) Sampling and the Stacking Algorithm. As confirmed by testing on five multiclass datasets, the proposed method outperforms other methods in terms of accuracy values, sensitivity, specificity, and geometric mean values. As a result, the method proposed in this study can solve class imbalance problems in multiclass-type datasets. However, this study has limitations. Namely, the dataset used is a multiclass category with a maximum number of six classes. For this reason, further research will suggest testing using imbalanced class datasets in the category of multiclass datasets with more than six classes.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739121","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

Studying Borderline Personality Disorder Using Machine Learning 使用机器学习研究边缘型人格障碍

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721800

Koushik Deb, Hemangee De, Seshadri Sekhar Chatterjee, Anjan Pal

{"title":"Studying Borderline Personality Disorder Using Machine Learning","authors":"Koushik Deb, Hemangee De, Seshadri Sekhar Chatterjee, Anjan Pal","doi":"10.1109/imcom53663.2022.9721800","DOIUrl":"https://doi.org/10.1109/imcom53663.2022.9721800","url":null,"abstract":"Borderline Personality Disorder is a mental disorder that impacts a person’s way of thinking and feeling about himself or others. This creates self-doubt, self-image problems, difficulty in managing emotions and behavior, as well as leads to unstable relationships. But this disorder can be cured with proper treatment if diagnosed early. The aim of this research is to explore potential features in the detection of Borderline Personality Disorder using machine learning methods. Furthermore, it identifies the potential features(here emotions) which are responsible in the detection of Borderline Personality Disorder. Data were collected in two ways one from self declared user in social media(i.e. who declared themselves as Borderline Personality Disorder patients) and second by inviting people for a Borderline Personality Disorder screening test Permission has been taken to fetch their social media data for research. This research achieved an accuracy of 81.03% using Random Forest classification algorithm. Emotional features were extracted from each data point in the dataset to identify the potential features for Borderline Personality Disorder classification. Features such as nervousness, shame, and pain have been derived that can in turn contribute to a similar accuracy to about 81.25%. This attempt is to explain the heuristic explanation, how differently an individual with Borderline Personality Disorder reacts from other individuals. This research paves the way for identifying Borderline Personality Disorder among individuals.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114885317","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

Bangla Handwritten Character and Digit Recognition Using Deep Convolutional Neural Network on Augmented Dataset and Its Applications 基于增强数据集的深度卷积神经网络孟加拉手写体字符和数字识别及其应用

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721634

Hasibul Huda, Md. Ariful Islam Fahad, Moonmoon Islam, A. Das

引用次数: 7

An Improved Sliding Mode Control Using Reduced-order PI Observer for PMSM system 基于降阶PI观测器的PMSM系统改进滑模控制

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721729

Ton Hoang Nguyen, T. Nguyen, H. N. Tran, Jaewook Jeon

引用次数: 2

Deep Representation for the Classification of Ultrasound Breast Tumors 超声乳腺肿瘤分类的深度表示

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721796

Mingue Song, Yanggon Kim

{"title":"Deep Representation for the Classification of Ultrasound Breast Tumors","authors":"Mingue Song, Yanggon Kim","doi":"10.1109/imcom53663.2022.9721796","DOIUrl":"https://doi.org/10.1109/imcom53663.2022.9721796","url":null,"abstract":"An automated classification of ultrasound breast tumor is a vital step for the early prevention of abnormal breast cells. In general, radiologists manually handle this procedure, but manual analysis performed by individual poses a problem of consistency depending on the experts. One of the standardized alternatives was to apply automated deep learning method in this field. In fact, majority ideas in literature are dominantly based on the supervised learning framework, but even such methods have still failed to present promising discrimination performance. In this work, we assume that unsupervised learning still can be a potential option and beneficial attribute that enables to accelerate discrimination is inherent in it. Hence, we present a deep representation for the ultrasound breast data utilizing two types of independent supervised and unsupervised network to reconstruct the principal features, while the volume of supervised features is set to be minimum and the volume of unsupervised is the maximum. Specifically, we adopted pretrained Resnet34 as a supervised network, and a convolutional autoencoder (CAE) was designed for the unsupervised network. Each representation vector is combined into a single vector, and the generated vector is given to the support vector machine as an input for the final discrimination. The results are verified that the proposed method shows far better performance compared to several conventional deep learning methods and the single use of each method. The value of accuracy, sensitivity and specificity are obtained by 88.18%, 85.25% and 100.00% respectively.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132247827","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

Implementation and Performance Analysis of Time-Determined Forwarding and Queuing Functions Based on IEEE 802.1Qav 基于IEEE 802.1Qav的定时转发和排队功能的实现及性能分析

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721743

Y. Do, Min Ho Kim, J. Jeon

{"title":"Implementation and Performance Analysis of Time-Determined Forwarding and Queuing Functions Based on IEEE 802.1Qav","authors":"Y. Do, Min Ho Kim, J. Jeon","doi":"10.1109/imcom53663.2022.9721743","DOIUrl":"https://doi.org/10.1109/imcom53663.2022.9721743","url":null,"abstract":"The audio-video bridging (AVB) technology has been standardized by IEEE to deliver high-quality audio/video in real time over an Ethernet network. The IEEE AVB Ethernet standard includes protocols to synchronize time, set up standards for stream reservation, and schedule standards for forwarding and queuing, among others. However, AVB is difficult to apply to industrial networks such as industrial automation and automobiles due to lack of consideration for control data that must ensure transmission time. To solve this issue, IEEE proposes the 802.1 time-sensitive network standard, and various studies are being conducted with regard to related applications. In this study, based on the structure of the existing IEEE 802.1Qav protocol, the time-determined forwarding and queuing functions designed using the time-aware shaper, priority queuing, and credit-based shaper algorithms are described. TC275 (Infineon) was used for functional implementation, and VN5620 (Vector) was used for data monitoring. Through the experimental process in the simulation environment, it was confirmed that the TAS algorithm transmits control data at intervals of 200ms and 500ms.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123301864","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 Novel Mutual Trust Evaluation Method for Identification of Trusted Devices in Smart Environment 一种新的智能环境下可信设备相互信任评估方法

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721756

U. Rehman, Amir Ali, H. M. Bilal, Muhammad Asif Razzaq, Seong-Bae Park, Sungyoung Lee

引用次数: 0